python-sql-2110511008/notebook/06_model_jan23.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>domisili</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>join_date</th>\n",
       "      <th>resign_date</th>\n",
       "      <th>marriage_stat</th>\n",
       "      <th>dependant</th>\n",
       "      <th>education</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>...</th>\n",
       "      <th>total_income_work</th>\n",
       "      <th>income_dependant_ratio</th>\n",
       "      <th>work_efficiency</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</th>\n",
       "      <th>position_score</th>\n",
       "      <th>job_income_position_score</th>\n",
       "      <th>education_score</th>\n",
       "      <th>education_income_ratio</th>\n",
       "      <th>weighted_satisfaction_performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EM13274</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1999-01-23</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>2023-02-02</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>D2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.341320e+07</td>\n",
       "      <td>3.100943e+06</td>\n",
       "      <td>1.12750</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.100943e+06</td>\n",
       "      <td>3</td>\n",
       "      <td>1.033648e+06</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EM10730</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1998-04-12</td>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>2024-03-16</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.489849e+07</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>1.22500</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>1.146038e+06</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EM4510</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1981-06-10</td>\n",
       "      <td>2021-10-30</td>\n",
       "      <td>2023-12-15</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.003449e+08</td>\n",
       "      <td>2.671265e+06</td>\n",
       "      <td>1.18125</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.003449e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>8.013796e+06</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EM2622</td>\n",
       "      <td>Kabupaten Bekasi</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1981-07-26</td>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.537505e+08</td>\n",
       "      <td>2.537505e+06</td>\n",
       "      <td>1.22000</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.537505e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>1.015002e+07</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EM0633</td>\n",
       "      <td>Kota Jakarta Pusat</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1988-07-07</td>\n",
       "      <td>2022-08-22</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.312456e+07</td>\n",
       "      <td>1.274022e+06</td>\n",
       "      <td>1.18250</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>1</td>\n",
       "      <td>2.548043e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.548043e+06</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee_id            domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "0     EM13274  Kota Jakarta Timur     Perempuan    1999-01-23  2021-11-30   \n",
       "1     EM10730           Tangerang     Laki-laki    1998-04-12  2023-01-31   \n",
       "2      EM4510    Kabupaten Bekasi     Laki-laki    1981-06-10  2021-10-30   \n",
       "3      EM2622    Kabupaten Bekasi     Laki-laki    1981-07-26  2021-09-13   \n",
       "4      EM0633  Kota Jakarta Pusat     Laki-laki    1988-07-07  2022-08-22   \n",
       "\n",
       "  resign_date marriage_stat  dependant education  absent_90D  ...  \\\n",
       "0  2023-02-02        Single          0        D2         4.0  ...   \n",
       "1  2024-03-16        Single          0      SLTA         2.0  ...   \n",
       "2  2023-12-15       Married          2      SLTA         0.0  ...   \n",
       "3  2023-10-31       Married          3      SLTA         0.0  ...   \n",
       "4  2023-10-01       Married          1      SLTA         8.0  ...   \n",
       "\n",
       "   total_income_work income_dependant_ratio work_efficiency  \\\n",
       "0       4.341320e+07           3.100943e+06         1.12750   \n",
       "1       1.489849e+07           1.146038e+06         1.22500   \n",
       "2       2.003449e+08           2.671265e+06         1.18125   \n",
       "3       2.537505e+08           2.537505e+06         1.22000   \n",
       "4       3.312456e+07           1.274022e+06         1.18250   \n",
       "\n",
       "   active_work_category  work_stability_score position_score  \\\n",
       "0              Mid-term              2.800000              1   \n",
       "1              Mid-term              4.333333              1   \n",
       "2              Mid-term             25.000000              4   \n",
       "3              Mid-term             25.000000              4   \n",
       "4              Mid-term              1.444444              1   \n",
       "\n",
       "  job_income_position_score  education_score  education_income_ratio  \\\n",
       "0              3.100943e+06                3            1.033648e+06   \n",
       "1              1.146038e+06                1            1.146038e+06   \n",
       "2              2.003449e+06                1            8.013796e+06   \n",
       "3              2.537505e+06                1            1.015002e+07   \n",
       "4              2.548043e+06                1            2.548043e+06   \n",
       "\n",
       "   weighted_satisfaction_performance  \n",
       "0                                1.8  \n",
       "1                                2.6  \n",
       "2                                3.0  \n",
       "3                                4.0  \n",
       "4                                1.8  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"D:/Tugas Akhir/Codingan/Development/App/data/df_train_YESUSFIX.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y = df['active_work_months']\n",
    "\n",
    "df['join_date'] = pd.to_datetime(df['join_date'])\n",
    "\n",
    "train_data = df[df['join_date'] < '2023-01-01']\n",
    "valid_data = df[df['join_date'] >= '2023-01-01']\n",
    "\n",
    "X_train = train_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y_train = train_data['active_work_months']\n",
    "\n",
    "X_valid = valid_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y_valid = valid_data['active_work_months']\n",
    "\n",
    "cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', \n",
    "               'performance_rating',\n",
    "               'education', 'active_work_category', 'jenis_kelamin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y = df['active_work_months']\n",
    "\n",
    "df['join_date'] = pd.to_datetime(df['join_date'])\n",
    "\n",
    "train_data = df[df['join_date'] < '2023-01-01']\n",
    "valid_data = df[df['join_date'] >= '2023-01-01']\n",
    "\n",
    "X_train = train_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y_train = train_data['active_work_months']\n",
    "\n",
    "X_valid = valid_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y_valid = valid_data['active_work_months']\n",
    "\n",
    "cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', \n",
    "               'performance_rating',\n",
    "               'education', 'active_work_category', 'jenis_kelamin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y = df['active_work_months']\n",
    "\n",
    "df['join_date'] = pd.to_datetime(df['join_date'])\n",
    "\n",
    "train_data = df[df['join_date'] < '2023-01-01']\n",
    "valid_data = df[df['join_date'] >= '2023-01-01']\n",
    "\n",
    "X_train = train_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y_train = train_data['active_work_months']\n",
    "\n",
    "X_valid = valid_data.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y_valid = valid_data['active_work_months']\n",
    "\n",
    "cat_feature = ['departemen', 'position', 'domisili', 'marriage_stat', 'job_satisfaction', \n",
    "               'performance_rating',\n",
    "               'education', 'active_work_category', 'jenis_kelamin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 13.6415247\ttest: 22.1879174\tbest: 22.1879174 (0)\ttotal: 302ms\tremaining: 5m 1s\n",
      "200:\tlearn: 2.2485216\ttest: 4.1162352\tbest: 4.1162352 (200)\ttotal: 21.1s\tremaining: 1m 23s\n",
      "400:\tlearn: 0.5928453\ttest: 1.3994778\tbest: 1.3994778 (400)\ttotal: 42.9s\tremaining: 1m 4s\n",
      "600:\tlearn: 0.3630271\ttest: 0.9035525\tbest: 0.9035525 (600)\ttotal: 1m 2s\tremaining: 41.7s\n",
      "800:\tlearn: 0.3137175\ttest: 0.7312542\tbest: 0.7312542 (800)\ttotal: 1m 23s\tremaining: 20.8s\n",
      "999:\tlearn: 0.2992165\ttest: 0.6687708\tbest: 0.6687708 (998)\ttotal: 1m 43s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.6687708244\n",
      "bestIteration = 998\n",
      "\n",
      "Shrink model to first 999 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x1c715e4b7c0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "\n",
    "model = CatBoostRegressor(\n",
    "    iterations=1000,\n",
    "    learning_rate=0.01,\n",
    "    depth=6,\n",
    "    cat_features=cat_feature,\n",
    "    loss_function='RMSE',  # Fungsi kerugian regresi, seperti RMSE atau MAE\n",
    "    eval_metric='RMSE',    # Metrik evaluasi regresi\n",
    "    verbose=200\n",
    ")\n",
    "\n",
    "model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2 Score</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.087987</td>\n",
       "      <td>0.240740</td>\n",
       "      <td>0.296626</td>\n",
       "      <td>0.999536</td>\n",
       "      <td>0.010529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation</th>\n",
       "      <td>0.447254</td>\n",
       "      <td>0.483388</td>\n",
       "      <td>0.668771</td>\n",
       "      <td>0.984317</td>\n",
       "      <td>0.064341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MSE       MAE      RMSE  R2 Score      MAPE\n",
       "Train       0.087987  0.240740  0.296626  0.999536  0.010529\n",
       "Validation  0.447254  0.483388  0.668771  0.984317  0.064341"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Prediksi pada data training dan validasi\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_valid = model.predict(X_valid)\n",
    "\n",
    "# Menghitung metrik regresi untuk training\n",
    "mse_train = mean_squared_error(y_train, y_pred_train)\n",
    "mae_train = mean_absolute_error(y_train, y_pred_train)\n",
    "rmse_train = np.sqrt(mse_train)\n",
    "r2_train = r2_score(y_train, y_pred_train)\n",
    "mape_train = mean_absolute_percentage_error(y_train, y_pred_train)\n",
    "\n",
    "# Menghitung metrik regresi untuk validasi\n",
    "mse_valid = mean_squared_error(y_valid, y_pred_valid)\n",
    "mae_valid = mean_absolute_error(y_valid, y_pred_valid)\n",
    "rmse_valid = np.sqrt(mse_valid)\n",
    "r2_valid = r2_score(y_valid, y_pred_valid)\n",
    "mape_valid = mean_absolute_percentage_error(y_valid, y_pred_valid)\n",
    "\n",
    "# Membuat dataframe hasil metrik untuk training dan validation\n",
    "metrics = {\n",
    "    \"MSE\": [mse_train, mse_valid],\n",
    "    \"MAE\": [mae_train, mae_valid],\n",
    "    \"RMSE\": [rmse_train, rmse_valid],\n",
    "    \"R2 Score\": [r2_train, r2_valid],\n",
    "    \"MAPE\": [mape_train, mape_valid]\n",
    "}\n",
    "\n",
    "metrics_df = pd.DataFrame(metrics, index=[\"Train\", \"Validation\"])\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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44w7Dw8PD+Pnnnwttw2q1GoZhGEuWLCn0GhZwbNsC9evXN0aMGGG7ffbsWbvnaBiGkZKSYvj6+tq1XUpKiiHJmDdvXonP7/HHHze8vb2NkydP2sqys7ONsLAwY9SoUbay873WxZk3b54hyfj++++NY8eOGQcOHDA++ugjo2bNmoa/v79x8OBBwzD+9/o/9thjdvdft26dIclYsGCBXfm3335rV16wD/Tr18/W3oZhGE888YQhya4NV61aZfc65OXlGQ0aNDDq169vpKam2j3OudsaN26cUdTHWnnEWJK4uDjjtttus7t/RESEkZubayvbvXu34eHhYdx4442F9pdzH7tFixZG586dCz2GYxtZrVbjkksuMQYNGmRX7+OPPzYkGWvXrjUMwzBOnTplhIWFGXfffbddvcOHDxuhoaGFyh1lZGQYnp6exujRo21ll112mTF16lTDMAyjffv2xqRJk2zrIiMjjR49ehiG4dxxoHPnzoYk48033ywUQ/369Y1+/foZhmEYs2bNMiwWi/H000+XGPe5223RooVhGIZxxRVX2J5Hamqq4ePjY7z33nu2tl2yZIntfgMHDjR8fHyM5ORkW9lff/1lBAcHG506dbKVFbyfunfvbvc6Pvjgg4anp6eRlpZmKyvutS3tNpYuXWpIKvKYdj6XXnppoX3l3Mcu6s8wij5uFXdsSExMLNSORQkMDCz1e6sou3fvNvz8/Izbb7+90HbPPUYW+OqrrwxJxrfffmtXnpCQYEgyFi9efMGxoOqhRwmVXkZGhiSV+uT9gilAH3roIbvyhx9+WJIK/SLbvHlzXXXVVbbbHTp0kGQOTapXr16h8j///LPQY577K2DB0LmcnBy7mXr8/f1t/6empio9PV3XXnttkUOnOnfurObNm5/nmZrn+WzatKnYnozNmzfr6NGjuu++++zGnffr109NmzYtsjdr7NixdrevvfbaIp/zub7//nvl5OTogQcesPt17+6771ZISMhFnT+2YMECXXHFFbaJHwqGEZ37C77VatWyZcvUv3//IsfdnztE62L5+vranmN+fr5OnDihoKAgXXbZZRc0DG7IkCHKzc3VZ599Ziv77rvvlJaWZjfD3/le6/Pp3r27IiMjVbduXd16660KCgrS0qVLC81Wdu+999rdXrJkiUJDQ9WjRw+7X5/btWunoKAgrVq1StL/9oH777/frr0feOCB88aWmJiolJQUPfDAA4XOXSvNa+eKGAv89ttv2rZtm915XUOHDtXx48e1fPlyW9myZctktVr11FNPFfrF+0L2R4vFoltuuUVff/21MjMzbeWLFy/WJZdcYusJWrFihdLS0mwxFfx5enqqQ4cOtrYoTnBwsFq1amXrUTp+/Lh27txp682/+uqrbcPtdu3apWPHjtke29njgK+vr905J46ef/55/eMf/9CMGTP0r3/9q7RNZTNs2DB99tlnysnJ0SeffCJPT09bj/a58vPz9d1332ngwIFq2LChrTwqKsp2XlbB51CBe+65x+51vPbaa5Wfn699+/aVOr7zbaPgvfDll18W6o07nxMnTig8PLzY9XPmzNGKFSvs/s7H8dhQMEnE8uXLixwSWhZOnz6tW265Rf7+/vr3v/9tt+7MmTPy9fUtdJ+CzzrHmXAL2uN8vWeoXkiUUOmFhIRIkt3Y5JLs27dPHh4ehWZUq1OnjsLCwgp9kJ2bDEn/O/jXrVu3yHLHcx48PDzsPlwlqUmTJpJkN8zlyy+/VMeOHeXn56caNWooMjJSb7zxhm0s97kcZyMqzvPPP6/ff/9ddevWVfv27TVlyhS7pKbguV522WWF7tu0adNCbeHn56fIyEi7svDw8ELP2VFxj+Pj46OGDRs69eXhXGlpafr666/VuXNn7dmzx/Z39dVXa/PmzbahFceOHVNGRoZatmx5QY/jDKvVqpdfflmNGzeWr6+vIiIiFBkZqd9++63I1/J8WrduraZNm2rx4sW2ssWLFysiIsLupOPzvdbnU/DFaNWqVUpKSrKdV3cuLy8vXXrppXZlu3fvVnp6umrVqqXIyEi7v8zMTNvkHAWvseO0zJGRkSV+YZP+NwzwQl8/V8RY4MMPP1RgYKAaNmxo2x/9/PwUExNjl7wnJyfLw8OjVD94lNaQIUN05swZ/d///Z8kKTMzU19//bVuueUW2xfu3bt3SzJ/6HFsi++++87WFiW55pprbOciJSQkyNPTUx07dpQkxcfH65dfflF2dnah85OcPQ5ccsklxc7qt2bNGj366KN69NFHS31ekqOCc2W++eYbLViwQNdff32RP7gdO3ZMp0+fLvI42axZM1mtVh04cMCu3PFzo2D/Od+x0pltdO7cWYMGDdLUqVMVERGhAQMGaN68eYXOvSmOUcK5OO3bt1f37t3t/kpS1LGhQYMGeuihh/Sf//xHERER6tWrl+bMmXNBx8Gi5Ofn69Zbb1VSUpI++eQTRUdH26339/cvsi0KZjQ898dJ6X/tUZY/nKHyY9Y7VHohISGKjo7W77//7tT9SnswLBjXX9rykj58irNu3TrdcMMN6tSpk15//XVFRUXJ29tb8+bNs5uZp4DjAb44gwcP1rXXXqulS5fqu+++0wsvvKAZM2bos88+U58+fZyOs7jn7C5LlixRdna2XnzxxSJnb1qwYIGmTp1arjE4TmTx3HPP6cknn9SoUaP09NNPq0aNGvLw8NADDzxQ6Py10hoyZIieffZZHT9+XMHBwfq///s/DR061G4q+It9rdu3b1/sLFcFzu0tK2C1WlWrVq1iJ9BwTKzdwVUxGn9PU5+VlVVkAnT06FFlZmYqKCioTB7PUceOHRUTE6OPP/5Yw4YN0xdffKEzZ87Y9TwW7IMffPCB6tSpU2gbpbm8wDXXXKPXXntNGzZsUEJCgm3iEslMlLKzs/Xzzz9r/fr18vLysiVRzirpONeiRQulpaXZzmkp7Y9H54qKilKXLl304osvasOGDWU6011ZfD6cbxsF13rauHGjvvjiCy1fvlyjRo3Siy++qI0bN5a4n9WsWdOppO18ijo2SNKLL76okSNH6vPPP9d3332nCRMmaPr06dq4cWOhxMpZd999t7788kstWLCgyJnqoqKidOjQoULlBWWOiVVBezhOFoHqjUQJVcL111+vt99+Wz/++KPdMLmi1K9fX1arVbt377Y7Uf/IkSNKS0tT/fr1yzQ2q9WqP//809aLJMnW01EwCcOnn34qPz8/LV++3G6oQFEXb3RWVFSU7rvvPt133306evSo2rZtq2effVZ9+vSxPdedO3cW+qDZuXNnmbXFuY9zbu9aTk6OUlJSzvtrZXEWLFigli1bFpoYQ5LeeustLVy4UFOnTlVkZKRCQkLOm0yXlDyHh4cXmtwiJyen0AfxJ598oq5du2ru3Ll25WlpaRf8ATxkyBBNnTpVn376qWrXrq2MjIwiJx4p6bUuL7Gxsfr+++919dVXl/jFtmAf2L17t90+cOzYsfN+YYuNjZUk/f777yXuK8W9fq6IUTJ7OQ4ePKhp06YVmgQkNTVV99xzj5YtW6bhw4crNjZWVqtVSUlJuvzyy51+TsUZPHiwZs2apYyMDC1evFgxMTF2iUpBW9aqVeuC33fnTujw448/6uqrr7ati46OVv369bVhwwZt2LBBbdq0sZ1gX5bHgYiICH3yySe65pprdN1112n9+vWFvviWxrBhw3TXXXcpLCxMffv2LbJOZGSkAgICtHPnzkLr/vjjD3l4eBQaYVAaZdVz0bFjR3Xs2FHPPvusFi5cqNtuu00fffSR7rrrrmLv07RpU9uMluUtLi5OcXFx+te//qWEhARdffXVevPNN21T5l9IO0yaNEnz5s3TK6+8UuzlCy6//HKtW7dOVqvVLonbtGmTAgIC7D6Tpf/N8OnsBD6o2hh6hyrhkUceUWBgoO666y4dOXKk0Prk5GTbNMIFH4avvPKKXZ2XXnpJknl+TlmbPXu27X/DMDR79mx5e3vruuuuk2T+cmixWOx6J/bu3Vvqq5UXJT8/v9AQh1q1aik6Oto2HOGKK65QrVq19Oabb9oNUfjmm2+0Y8eOMmuL7t27y8fHR6+++qrdL6pz585Venr6BT3OgQMHtHbtWg0ePFg333xzob8777xTe/bs0aZNm+Th4aGBAwfqiy++0ObNmwttqyCmgut+OCZEkvkFc+3atXZlb7/9dqEeJU9Pz0K/Gi9ZsqTQdOvOaNasmeLi4rR48WItXrxYUVFRdtP6lua1Li+DBw9Wfn6+nn766ULr8vLybG3ZvXt3eXt767XXXrNrH8f3YVHatm2rBg0a6JVXXin02py7reJeP1fEKP1v2N2kSZMK7Y933323GjdubOvVGjhwoDw8PDRt2rRCPY2Oz6mo/bE4Q4YMUXZ2tt577z19++23haa67tWrl0JCQvTcc88VeV6L4/TTRYmOjlaDBg30ww8/aPPmzYVmG42Pj9eyZcu0c+dOu2nBy/o4cOmll+r777/XmTNn1KNHD504ccKp+0vSzTffrMmTJ+v1118vdpifp6enevbsqc8//9xuuPSRI0e0cOFCXXPNNbYh4M5w9rV1lJqaWuhYU5B0n+99f9VVV+n3338v1+NDRkaG8vLy7Mri4uLk4eFh97jOtsMLL7ygmTNn6oknnrC77Iejm2++WUeOHLE7v/P48eNasmSJ+vfvX+j8pV9++UWhoaFq0aJFqWNB1UePEqqE2NhYLVy4UEOGDFGzZs10xx13qGXLlsrJybFdcK7g2gytW7fWiBEj9PbbbystLU2dO3fWTz/9pPfee08DBw4sdgraC+Xn56dvv/1WI0aMUIcOHfTNN9/oq6++0hNPPGEb8tOvXz+99NJL6t27t4YNG6ajR49qzpw5atSokX777bcLetxTp07p0ksv1c0336zWrVsrKChI33//vX7++WfbMDVvb2/NmDFDd955pzp37qyhQ4fqyJEjtinHH3zwwTJpg8jISD3++OOaOnWqevfurRtuuEE7d+7U66+/riuvvFLDhw93epsLFy6UYRi64YYbilzft29feXl5acGCBerQoYOee+45fffdd+rcubPuueceNWvWTIcOHdKSJUu0fv16hYWF6fLLL5enp6dmzJih9PR0+fr6qlu3bqpVq5buuusujR07VoMGDVKPHj3066+/avny5YV6ia6//npNmzZNd955p+Lj47Vt2zYtWLCg0HlqzhoyZIieeuop+fn5afTo0Xa/kJbmtS4vnTt31pgxYzR9+nRt3bpVPXv2lLe3t3bv3q0lS5Zo1qxZuvnmm23X3Jo+fbquv/569e3bV4mJifrmm2/O29Pm4eGhN954Q/3799fll1+uO++8U1FRUfrjjz+0fft22yQJ7dq1kyRNmDBBvXr1kqenp2699VaXxFgwTX2PHj2KvSDnDTfcoFmzZuno0aNq1KiR/vnPf+rpp5/Wtddeq5tuukm+vr76+eefFR0drenTp9ue0xtvvKFnnnlGjRo1Uq1atUq8IGbbtm1t287OzrYbdieZQ5XfeOMN3X777Wrbtq1uvfVWRUZGav/+/frqq6909dVX2/2wU5xrrrnGNv36uT1KkpkoLVq0yFavQHkcBxo1aqTvvvtOXbp0Ua9evbRy5UqnkpbQ0NBSXavtmWee0YoVK3TNNdfovvvuk5eXl9566y1lZ2fr+eefdzpuyfnX1tF7772n119/XTfeeKNiY2N16tQpvfPOOwoJCSm2d6zAgAED9PTTT2vNmjXq2bPnBcV/PitXrtT48eN1yy23qEmTJsrLy9MHH3wgT09P26UFJLMdvv/+e7300ku2JLxgciRHS5cu1SOPPKLGjRurWbNm+vDDD+3W9+jRQ7Vr15ZkJkodO3bUnXfeqaSkJEVEROj1119Xfn5+kUOyV6xYof79+3OOEuy5fJ49oBzt2rXLuPvuu42YmBjDx8fHCA4ONq6++mrjtddeM86ePWurl5uba0ydOtVo0KCB4e3tbdStW9d4/PHH7eoYhv00tOeSVGgq5oJpU1944QVb2YgRI4zAwEAjOTnZ6NmzpxEQEGDUrl3bmDx5cqEpgefOnWs0btzY8PX1NZo2bWrMmzfPNlX2+R773HUFU1hnZ2cbkyZNMlq3bm0EBwcbgYGBRuvWrY3XX3+90P0WL15stGnTxvD19TVq1Khh3HbbbbZpoR2fi6OiYizO7NmzjaZNmxre3t5G7dq1jXvvvbfQdM+lnR48Li7OqFevXol1unTpYtSqVcs2LfO+ffuMO+64w4iMjDR8fX2Nhg0bGuPGjTOys7Nt93nnnXeMhg0bGp6ennbTL+fn5xuPPvqoERERYQQEBBi9evUy9uzZU+T04A8//LARFRVl+Pv7G1dffbXx448/Gp07d7abCri004MX2L17t22a3vXr19utc+a1dlQwHfD5phgu7vUv8Pbbbxvt2rUz/P39jeDgYCMuLs545JFHjL/++stWJz8/35g6daqtbbp06WL8/vvvhdrQcerrAuvXrzd69Ohhe46tWrUyXnvtNdv6vLw84/777zciIyMNi8VSaL8syxgdffrpp4YkY+7cucXWWb16tSHJmDVrlq3s3Xfftb33wsPDjc6dOxsrVqywrT98+LDRr18/Izg42JBk24eKayPDMIx//vOfhiSjUaNGxcayatUqo1evXkZoaKjh5+dnxMbGGiNHjjQ2b95c7H3OVXA5hEsuuaTQui1bttj21SNHjhRaX5rjwLnTeDsq6ri8adMm21TdjpdaKO12CxQ1PXjB8+rVq5cRFBRkBAQEGF27drW7dIRhFP9+Kur1Ku61Le02tmzZYgwdOtSoV6+e4evra9SqVcu4/vrrS/0atmrVym6a95Ieu0Bx04MXdWz4888/jVGjRhmxsbGGn5+fUaNGDaNr167G999/b1fvjz/+MDp16mT4+/ufdxr+gs+H4v4c3w8nT540Ro8ebdSsWdMICAgwOnfuXORz27Fjh+0yCcC5LIbBJYiB8jJy5Eh98skndtP1AgDgbh988IHGjRun/fv3F5p2v7p54IEHtHbtWv3yyy/0KMEO5ygBAABUM7fddpvq1aunOXPmuDsUtzpx4oT+85//6JlnniFJQiGcowQAAFDNeHh4OH1ZjaqoZs2ajPpAsehRAgAAAAAHnKMEAAAAAA7oUQIAAAAAByRKAAAAAOCgyk/mYLVa9ddffyk4OJjZTAAAAIBqzDAMnTp1StHR0XYXby9KlU+U/vrrL9WtW9fdYQAAAACoIA4cOKBLL720xDpVPlEKDg6WZDZGSEiIW2PJzc3Vd999p549e8rb29utsVQXtLnr0eauRXu7Hm3uerS5a9Herkebu05GRobq1q1ryxFKUuUTpYLhdiEhIRUiUQoICFBISAhvAhehzV2PNnct2tv1aHPXo81di/Z2Pdrc9UpzSg6TOQAAAACAAxIlAAAAAHBAogQAAAAADqr8OUoAAAAof4ZhKC8vT/n5+e4OpdLJzc2Vl5eXzp49S/tdJE9PT3l5eZXJZYFIlAAAAHBRcnJydOjQIZ0+fdrdoVRKhmGoTp06OnDgANf9LAMBAQGKioqSj4/PRW2HRAkAAAAXzGq1KiUlRZ6enoqOjpaPjw9f9p1ktVqVmZmpoKCg814EFcUzDEM5OTk6duyYUlJS1Lhx44tqTxIlAAAAXLCcnBxZrVbVrVtXAQEB7g6nUrJarcrJyZGfnx+J0kXy9/eXt7e39u3bZ2vTC8UrAQAAgIvGF3xUFGW1L7JHAwAAAIADEiUAAAAAcECiBAAAAFRAFotFy5YtK9fH6NKlix544IFyfYzKikQJAAAA1dqPP/4oT09P9evXz+n7xsTE6JVXXin7oM6jf//+6t27d5Hr1q1bJ4vFot9++83FUVUtJEoAAACoEKxWads2ae1ac2m1uuZx586dq/vvv19r167VX3/95ZoHvUijR4/WihUrdPDgwULr5s2bpyuuuEKtWrVyQ2RVB4kSUFkZViltm3R0rbk0XPRpAgBAOUhIkIYPl+64Qxo71lwOH26Wl6fMzEwtXrxY9957r/r166f58+cXqvPFF1/oyiuvlJ+fnyIiInTjjTdKMoet7du3Tw8++KAsFovt+lFTpkzR5ZdfbreNV155RTExMbbbP//8s3r06KGIiAiFh4erX79+2rJlS6njvv766xUZGVko3szMTC1ZskSjR4/WiRMnNHToUF1yySUKCAhQXFycFi1aVOJ2ixruFxYWZvc4Bw4c0ODBgxUWFqYaNWpowIAB2rt3r2396tWr1b59ewUGBiosLExXX3219u3bV+rnVlGQKAGV0bEEKWG49OMd0k9jzWXCcLMcAIBKJiFBmjhR2rJFCguTYmLMZWKiWV6eydLHH3+spk2b6rLLLtPw4cP17rvvyjAM2/qvvvpKN954o/r27avExET98MMPat++vSTps88+06WXXqpp06bp0KFDOnToUKkf99SpUxoxYoTWr1+vhIQExcbG6vrrr9epU6dKdX8vLy/dcccdmj9/vl28S5YsUX5+voYOHaqzZ8+qXbt2+uqrr/T777/rnnvu0e23366ffvqp1HE6ys3NVa9evRQcHKx169Zpw4YNCgoKUu/evZWTk6O8vDwNHDhQnTt31m+//aYff/xR99xzT6W8CDEXnAUqm2MJUuJEKfuk5B8l+flL+Wek1ESzvM1MKTLe3VECAFAqVqs0e7Z08qTUqJFU8H06KEiKjZWSk6U5c6SOHaXyuFTT3LlzNXz4cElS7969lZ6erjVr1qhLly6SpGeffVa33nqrpk6dartP69atJUk1atSQp6engoODVadOHacet1u3brb/rVarrcdpzZo1uv7660u1jVGjRumFF16wi3fevHkaNGiQQkNDFRoaqokTJ9rq33///Vq+fLk+/vhjW7LnrMWLF8tqteo///mPLfmZN2+ewsLCtHr1al1xxRVKT0/X9ddfr9jYWElSs2bNLuix3I0eJaAyMazSrtlmkhTUSPIKkiye5jIwVspJlXbNYRgeAKDS2L5d2rFDior6X5JUwGKR6tSRkpLMemVt586d+umnnzR06FBJZi/NkCFDNHfuXFudrVu36rrrrivzxz5y5IjuvvtuNW7cWOHh4apXr54yMzO1f//+Um+jadOmio+P17vvvitJ2rNnj9atW6fRo0dLkvLz8/X0008rLi5ONWrUUFBQkJYvX+7UYzj69ddftWfPHgUHBysoKEhBQUGqUaOGzp49q+TkZNWoUUMjR45Ur1691L9/f82aNcupnraKhB4loDJJ3y5l7DB7kor6NPGtI2UkmfXC4twTIwAATkhNlbKzJX//otf7+0tHjpj1ytrcuXOVl5en6OhoW5lhGPL19dXs2bMVGhoq/+ICK4GHh4fdcDjJHLJ2rhEjRujEiROaNWuW6tataxvSlpOT49RjjR49Wvfff7/mzJmjefPmKTY2Vp07d5YkvfDCC5o1a5ZeeeUVxcXFKTAwUA888ECJj2GxWEqMPTMzU+3atdOCBQsK3TcyMlKS2cM0YcIEffvtt1q8eLH+9a9/acWKFerYsaNTz83d6FECKpOcVCk/W/Is5qDt5W+uzymHTxMAAMpBeLjk6yudOVP0+jNnzPXh4WX7uHl5eXr//ff14osvauvWrba/X3/9VdHR0bZJD1q1aqUffvih2O34+PgoPz/friwyMlKHDx+2Szi2bt1qV2fDhg2aMGGC+vbtqxYtWsjHx0fHjx93+nkMHjxYHh4eWrhwod5//32NGjXKNiRuw4YNGjBggIYPH67WrVurYcOG2rVrV4nbi4yMtOsB2r17t06fPm273bZtW+3evVu1atVSo0aN7P5CQ0Nt9dq0aaPHH39cCQkJatmypRYuXOj0c3M3EiWgMvEJlzx9zXOSipJ3xlzvU8afJgAAlJMWLaRmzaTDhyWHjgwZhlnevLlZryx9+eWXSk1N1ejRo9WyZUu7v0GDBtmG302ePFmLFi3S5MmTtWPHDm3btk0zZsywbScmJkZr167Vf//7X1ui06VLFx07dkzPP/+8kpOTNWfOHH3zzTd2j9+4cWN98MEH2rFjhzZt2qR77rnngnqvgoKCNGTIED3++OM6dOiQRo4cafcYK1asUEJCgnbs2KExY8boyJEjJW6vW7dumj17thITE7V582aNHTtW3t7etvW33XabIiIiNGDAAK1bt04pKSlavXq1JkyYoIMHDyolJUWPP/64fvzxR+3bt0/fffeddu/eXSnPUyJRAiqT0BZSSDPpbDGfJtmHpZDmZj0AACoBDw9p/Hizxyg5WcrMlPLzzWVyslk+blzZT+Qwd+5cde/e3a4XpMCgQYO0efNm/fbbb+rSpYuWLFmi//u//9Pll1+ubt262c0aN23aNO3du1exsbG2oWfNmjXT66+/rjlz5qh169b66aef7CZVKHj81NRUtW3bViNGjNCYMWNUq1atC3ouo0ePVmpqqnr16mU3jPBf//qX2rZtq169eqlLly6qU6eOBg4cWOK2XnzxRdWtW1fXXnuthg0bpokTJyogIMC2PiAgQGvXrlW9evV00003qVmzZho9erTOnj2rkJAQBQQE6I8//tCgQYPUpEkT3XPPPRo3bpzGjBlzQc/NnSyG4yDEKiYjI0OhoaFKT09XSEiIW2PJzc3V119/rb59+9pl5ig/VbLNC2a9y0k1z0ny8jd7krIPmz1Jbp71rkq2eQVGe7sebe56tLlrOdveZ8+eVUpKiho0aCA/P78LftyEBHP2ux07zHOWfH3NnqRx46T4Kj6Zq9VqVUZGhkJCQuRRHlP7VTMl7ZPO5AZM5gBUNpHxZjK0a7Y5sUP2EXO4XXhbqck4pgYHAFRK8fHmFODbt5sTN4SHm8PtyBvgLiRKQGUUGS9FdDRnt8tJNXuSQltIFj5NAACVl4eHFMekraggSJSAysriwRTgAAAA5YSfnwEAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAACgnI0cOVIDBw603e7SpYseeOABl8exevVqWSwWpaWllevjWCwWLVu2rFwfo7yRKAEAAKBaGjlypCwWiywWi3x8fNSoUSNNmzZNeXl55f7Yn332mZ5++ulS1XVVcpOTk6OIiAj9+9//LnL9008/rdq1ays3N7dc46goSJQAAABQMRhWKW2bdHStuTSs5f6QvXv31qFDh7R79249/PDDmjJlil544YUi6+bk5JTZ49aoUUPBwcFltr2y4OPjo+HDh2vevHmF1hmGofnz5+uOO+6Qt7e3G6JzPRIlAAAAuN+xBClhuPTjHdJPY81lwnCzvBz5+vqqTp06ql+/vu699151795d//d//yfpf8Plnn32WUVHR+uyyy6TJB04cECDBw9WWFiYatSooQEDBmjv3r22bebn5+uhhx5SWFiYatasqUceeUSGYdg9ruPQu+zsbD322GOqW7eufH191ahRI82dO1d79+5V165dJUnh4eGyWCwaOXKkJMlqtWr69Olq0KCB/P391bp1a33yySd2j/P111+rSZMm8vf3V9euXe3iLMro0aO1a9curV+/3q58zZo1+vPPPzV69Gj9/PPP6tGjhyIiIhQaGqrOnTtry5YtxW6zqB6xrVu3ymKx2MWzfv16XXvttfL391fdunU1YcIEZWVl2da//vrraty4sfz8/FS7dm3dfPPNJT6Xi0WiBAAAAPc6liAlTpRObpG8w6TAGHOZmmiWl3OydC5/f3+7nqMffvhBO3fu1IoVK/Tll18qNzdXvXr1UnBwsNatW6cNGzYoKChIvXv3tt3vxRdf1Pz58/Xuu+9q/fr1OnnypJYuXVri495777366KOP9Oqrr2rHjh166623FBQUpLp16+rTTz+VJO3cuVOHDh3SrFmzJEnTp0/X+++/rzfffFPbt2/Xgw8+qOHDh2vNmjWSzITupptuUv/+/bV161bdddddeuyxx0qMIy4uTldeeaXeffddu/J58+YpPj5eTZs21alTpzRixAitX79eGzduVOPGjdW3b1+dOnXKucY+R3Jysnr37q1Bgwbpt99+0+LFi7V+/XqNHz9ekrR582ZNmDBB06ZN086dO/Xtt9+qU6dOF/x4peFVrlsHAAAASmJYpV2zpeyTUlAjyWIxy72CpMBYKStZ2jVHiugoWcrvN37DMPTDDz9o+fLluv/++23lgYGB+s9//iMfHx9J0ocffiir1ar//Oc/svwd67x58xQWFqbVq1erZ8+eeuWVV/T444/rpptukiS9+eabWr58ebGPvWvXLi1dulTLly9Xz549JUkNGza0ra9Ro4YkqVatWgoLC5Nk9kA999xz+v7773XVVVfZ7rN+/Xq99dZb6ty5s9544w3FxsbqxRdflCRddtll2rZtm2bMmFFiW4wePVoTJ07Uq6++qqCgIJ06dUqffPKJXn31VUlSt27d7Oq//fbbCgsL05o1a3T99deXuO3iTJ8+Xbfddputl61x48Z69dVXbc9j//79CgwM1PXXX6/g4GDVr19fbdq0uaDHKi16lAAAAOA+6duljB2Sf9T/kqQCFovkW0fKSDLrlYMvv/xSQUFB8vPzU58+fTRkyBBNmTLFtj4uLs6WJEnSr7/+qj179ig4OFhBQUEKCgpSjRo1dPbsWSUnJys9PV2HDh1Shw4dbPfx8vLSFVdcUWwMW7dulaenpzp37lzquPfs2aPTp0+rR48etjiCgoL0/vvvKzk5WZK0Y8cOuzgk2ZKqkgwdOlT5+fn6+OOPJUmLFy+Wh4eHhgwZIkk6cuSI7r77bjVu3FihoaEKCQlRZmam9u/fX+r4Hf3666+aP3++3XPp1auXrFarUlJS1KNHD9WvX18NGzbU7bffrgULFuj06dMX/HilQY8SAAAA3CcnVcrPlvz8i17v5S9lHzHrlYOuXbvqjTfekI+Pj6Kjo+XlZf/1ODAw0O52Zmam2rVrpwULFhTaVmRk5AXF4O9fzHMvQWZmpiTpq6++0iWXXGK3ztfX94LiKBASEqKbb75Z8+bN06hRozRv3jwNHjxYQUFBkqQRI0boxIkTmjVrlurXry9fX19dddVVxU524eFh9s2ce56W48x5mZmZGjNmjCZMmFDo/vXq1ZOPj4+2bNmi1atX67vvvtNTTz2lKVOm6Oeff7b1spU1EiUAAAC4j0+45Okr5Z8xh9s5yjtjrvcJL5eHDwwMVKNGjUpdv23btlq8eLFq1aqlkJCQIutERUVp06ZNtnNo8vLy9Msvv6ht27ZF1o+Li5PVatWaNWtsQ+/OVdCjlZ+fbytr3ry5fH19tX///mJ7opo1a2abmKLAxo0bz/8kZQ6/69Kli7788kslJCTYzQS4YcMGvf766+rbt68k81yo48ePF7utggTy0KFDCg83X8etW7fa1Wnbtq2SkpJKfC28vLzUvXt3de/eXZMnT1ZYWJhWrlxpG+JY1hh6hwrFapW2bZPWrjWX1vKfFbTSoq0AAFVCaAsppJl09rDkMDOcDEPKPiyFNDfrVQC33XabIiIiNGDAAK1bt04pKSlavXq1JkyYoIMHD0qS/vGPf+jf//63li1bpj/++EP33XdfiddAiomJ0dChQ3XXXXdp2bJltm0WDH2rX7++LBaLvvzySx07dkyZmZkKDg7WxIkT9eCDD+q9995TcnKytmzZotdee03vvfeeJGns2LHavXu3Jk2apJ07d2rhwoWaP39+qZ5np06d1KhRI91xxx1q2rSp4uPjbesaN26sDz74QDt27NCmTZt02223ldgr1qhRI9WtW1dTpkzR7t279dVXX9nOmyrw6KOPKiEhQePHj9fWrVu1e/duff7557bJHL788ku9+uqr2rp1q/bt26f3339fVqvVNhNheXBrorR27Vr1799f0dHRxV69d8eOHbrhhhsUGhqqwMBAXXnllRc1/hEVV0KCNHy4dMcd0tix5nL4cLMc9mgrAECVYfGQmow3e4yykqXcTMnIN5dZyWZ5k3HlOpGDMwICArR27VrVq1dPN910k5o1a6bRo0fr7Nmzth6mhx9+WLfffrtGjBihq666SsHBwbrxxhtL3O6LL76oQYMG6b777lPTpk11991326bGvuSSSzR16lQ99thjql27ti15ePrpp/Xkk09q+vTpatasmXr37q2vvvpKDRo0kGQOWfv000+1bNkytW7dWm+++aaee+65Uj1Pi8WiUaNGKTU1VaNGjbJbN3fuXKWmpqpt27a6/fbbNWHCBNWqVavYbXl7e2vRokX6448/1KpVK82YMUPPPPOMXZ1WrVppzZo12rVrl6699lq1adNGTz31lKKjoyVJYWFh+uyzz9StWzc1a9ZMb775phYtWqQWLcovgbYYjpO6u9A333yjDRs2qF27drrpppu0dOlSDRw40LY+OTlZ7du31+jRozV06FCFhIRo+/bt6tixY4kvxrkyMjIUGhqq9PT0YrtHXSU3N1dff/21+vbtW20u1FVaCQnSxInSyZNSVJTk7y+dOSMdPiyFh0szZ0rn/JBRalWxzcurrcpKVWzzioz2dj3a3PVoc9dytr3Pnj2rlJQUNWjQQH5+fhf+wMcSzNnvMnaY5yx5+po9SU3GSZFu/GBzAavVqoyMDIWEhNjO58GFK2mfdCY3cOs5Sn369FGfPn2KXf/Pf/5Tffv21fPPP28ri42NdUVocCGrVZo92/zi3+icWUGDgqTYWCk5WZozR+rYUaruxw7aCgBQZUXGm1OAp283J27wCTeH21WQniRUPxV2Mger1aqvvvpKjzzyiHr16qXExEQ1aNBAjz/+uF2vk6Ps7GxlZ2fbbmdkZEgyfx1xnF3D1Qoe391xVDRJSdKff0r160vnzL5pU6+emQBs2yY1b+7ctqtam5dnW5WVqtbmFR3t7Xq0uevR5q7lbHvn5ubKMAxZrVZZy+KE2ZBzhlIZMq+zVMUVDPAqaEdcHKvVKsMwlJubK09PT7t1zhxH3Dr07lwWi8Vu6N3hw4cVFRWlgIAAPfPMM+ratau+/fZbPfHEE1q1alWxs3tMmTJFU6dOLVS+cOFCBQQElOdTAAAAqHa8vLxUp04d1a1b1+56Q4C75OTk6MCBAzp8+LDy8vLs1p0+fVrDhg0r1dC7Cpso/fXXX7rkkks0dOhQLVy40FbvhhtuUGBgoBYtWlTkdorqUapbt66OHz9eIc5RWrFihXr06MEY63MkJUljxkihoZLDpQokSVlZUnq69NZbF9ajVJXavDzbqqxUtTav6Ghv16PNXY82dy1n2/vs2bM6cOCAYmJiLu4cpWrMMAydOnVKwcHBsjhedBdOO3v2rPbu3au6desWeY5SRERExT9HqSQRERHy8vJSc4dve82aNdP69euLvZ+vr2+RF9ny9vauMAfXihRLRRAXJzVsKCUmmufZnHt8MAxp/36pbVuz3oWed1NV2twVbVVWqkqbVxa0t+vR5q5Hm7tWads7Pz9fFotFFouFiQguUMFwO9qwbBTsj0Xtw84cQyrsK+Hj46Mrr7xSO3futCvftWuX6tev76aoUB48PKTx480Z25KTpcxMKT/fXCYnm+Xjxrn/i39FQFsBACqagi+ep0+fdnMkgKlgX7zYH1bc2qOUmZmpPXv22G6npKRo69atqlGjhurVq6dJkyZpyJAh6tSpk+0cpS+++EKrV692X9AoF/Hx5rTWs2dLO3ZIR45Ivr5m78i4ce6d7rqioa0AABWJp6enwsLCdPToUUnmdYYYPuYcq9WqnJwcnT17lh6li2AYhk6fPq2jR48qLCys0EQOznJrorR582Z17drVdvuhhx6SJI0YMULz58/XjTfeqDfffFPTp0/XhAkTdNlll+nTTz/VNddc466QUY7i481prbdvl1JTzd6RFi3oHSkKbQUAqEjq1KkjSbZkCc4xDENnzpyRv78/SWYZCAsLs+2TF8OtiVKXLl10vrkkRo0aVehqwKi6PDzM82twfrQVAKCisFgsioqKUq1atZjG/QLk5uZq7dq16tSpE+fhXSRvb++L7kkqUGEncwAAAEDl4unpWWZfUqsTT09P5eXlyc/Pj0SpAmGgDgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4IBECQAAAAAckCgBAAAAgAMSJQAAAABw4OXuAAA7hlVK3y7lpEo+4VJoC8lCPg8AAADXIlFCxXEsQdo1W8rYIeVnS56+Ukgzqcl4KTLe3dEBAACgGuGnelQMxxKkxInSyS2Sd5gUGGMuUxPN8mMJbg4QAAAA1QmJEtzPsJo9SdknpaBGkleQZPE0l4Gx5jC8XXPMegAAAIALkCjB/dK3m8Pt/KMki8V+ncUi+daRMpLMegAAAIALkCjB/XJS/z4nyb/o9V7+5vqcVNfGBQAAgGqLRAnu5xNuTtyQf6bo9XlnzPU+4a6NCwAAANUWiRLcL7SFObvd2cOSYdivMwwp+7AU0tysBwAAALgAiRLcz+JhTgHuEy5lJUu5mZKRby6zks3yJuO4nhIAAABchm+eqBgi46U2M6XwNlJempS111yGtzXLuY4SAAAAXIgLzqLiiIyXIjqas9vlpJo9SaEt6EkCAACAy5EooWKxeEhhce6OAgAAANUcP9UDAAAAgAMSJQAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcECiBAAAAAAOSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4IBECQAAAAAckCgBAAAAgAMSJQAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcECiBAAAAAAOSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADgwMvdAQBAuTKsUvp2KSdV8gmXQltIFn4jAgAAJSNRAlB1HUuQds2WMnZI+dmSp68U0kxqMl6KjHd3dAAAoALjZ1UAVdOxBClxonRyi+QdJgXGmMvURLP8WIKbAwQAABUZiRKAqsewmj1J2SeloEaSV5Bk8TSXgbHmMLxdc8x6AAAARSBRAlD1pG83h9v5R0kWi/06i0XyrSNlJJn1AAAAikCiBKDqyUn9+5wk/6LXe/mb63NSXRsXAACoNEiUAFQ9PuHmxA35Z4pen3fGXO8T7tq4AABApUGiBKDqCW1hzm539rBkGPbrDEPKPiyFNDfrAQAqPsMqpW2Tjq41l5xjChdgenAAVY/Fw5wCPHGilJVsnpPk5W/2JGUfNnuSmozjekoAUBlwqQe4Cd8SAFRNkfFSm5lSeBspL03K2msuw9ua5Xy4AkDFx6Ue4Eb0KAGouiLjpYiO5ux2OalmT1JoC3qSAKAycLzUQ8EspgWXeshKNi/1ENGR4zrKBYkSgKrN4iGFxbk7CgCAs5y51APHeZQD0m8AAABUPFzqAW5GogQAAICKh0s9wM1IlAAAAFDxcKkHuBmJEgAAACqegks9+ISbEzfkZkpGvrnMSuZSDyh37FkAAAComLjUA9yIWe8AAABQcXGpB7gJiRIAAAAqNi71ADcgFQcAAAAAB/QoASgzVquUlGT+n5QkxcVJHm7+OcZqlbZvl1JTpfBwqUUL98cElCX28dKhnUqnIh7HJV6/0qKdypZbE6W1a9fqhRde0C+//KJDhw5p6dKlGjhwoG39yJEj9d5779ndp1evXvr2229dHCmA80lIkGbPlv78U3r8cWnMGKlhQ2n8eCneTefaFsS0Y4eUnS35+krNmrk3JqAssY+XDu1UOhXxOH5uXLx+JaOdyp5bc8ysrCy1bt1ac+bMKbZO7969dejQIdvfokWLXBghgNJISJAmTpS2bJFCQ82y0FApMdEsT0hwb0xhYVJMjLl0Z0xAWWIfLx3aqXQq4nHcMS5ev+LRTuXDrYlSnz599Mwzz+jGG28sto6vr6/q1Klj+wsP5+rLQEVitZq/YJ08KTVqJAUGmuWBgVJsrNn9P2eOWc9dMQUFSZ6e5tJdMQFliX28dGin0qmIx/Gi4uL1KxrtVH4q/DlKq1evVq1atRQeHq5u3brpmWeeUc2aNYutn52drezsbNvtjIwMSVJubq5yc3PLPd6SFDy+u+OoTmjz8peUZA7TqF9f8vGRvL3Nti5Y1qsnJSdL27ZJzZu7JyZH7oipvLCPu15FaPPqtI9LF97m57aTr49Vl4b8oSCfNGXmhOlgRlPVq+dRpdrpQlXE43hRcTmqSvv5xRxXqlM7lQVn2thiGIZRjrGUmsViKXSO0kcffaSAgAA1aNBAycnJeuKJJxQUFKQff/xRnp6eRW5nypQpmjp1aqHyhQsXKiAgoLzCBwAAAFDBnT59WsOGDVN6erpCQkJKrFuhEyVHf/75p2JjY/X999/ruuuuK7JOUT1KdevW1fHjx8/bGOUtNzdXK1asUI8ePeTt7e3WWKoL2rz8JSWZJ/yGhprDNLy9czVs2AotXNhDubneysqS0tOlt95ybY/SuTE5ckdM5YV93PUqQptXp31cuvA2T0qSXpu8Sf/o8i8F+6Uq7WwdZef7ydfzrML8jujU2TDNWv2M7p/aoUq004WqiMfxouJyVJX284s5rlSndioLGRkZioiIKFWiVOGH3p2rYcOGioiI0J49e4pNlHx9feXr61uo3Nvbu8J8iahIsVQXtHn5iYszZ0VKTDTHQhfIzfVWTo639u+X2rZ17RSzjjFZLP9bZxhyS0zljX3c9dzZ5tVxH5ecb/O4llbdGf+6AjwO6+ipRpIsssiqHKuPjuZeqpq+ybrz6jcU1zJeHp5VqKGcdO7+1CjWqroh5vzgdfx368+sOO3f7+GW/ak67ucXclypju10MZxp30rVXAcPHtSJEycUFRXl7lAA/M3Dw5x6NDzcHAOdlWWWZ2WZt8PDpXHjXHtwdowpM1PKzzeX7ooJKEvs46XjcWq7WjfYobSzUcrKsigvz/zimJcnZWVZlHa2jlrHJMnj1HZ3h+pWBfvTtc0SNKbVcN3bbowk6d52YzSm1XBd2yzBLfsT+3np0E7lx61NlpmZqa1bt2rr1q2SpJSUFG3dulX79+9XZmamJk2apI0bN2rv3r364YcfNGDAADVq1Ei9evVyZ9gAHMTHSzNnSm3amN37krls29Ysd8f1G86NKS1N2rvXXLozJqAssY+XQk6qgvyz1aCRv0JDpdxc6fRpcxkaKjVo5K8g/2wpJ9XdkbpdfOMEzRw2UW3qb1HaaXN+8LTToWpTP1Ezh01UfGP3zC/Nfl46tFP5cOvQu82bN6tr16622w899JAkacSIEXrjjTf022+/6b333lNaWpqio6PVs2dPPf3000UOrQPgXvHxUseO5qw6e/eaY6Hd3c1fEBNXKUdVxT5+Hj7hkqevaoScUXibIJ06ZSZJ3t5ScLBkyT0j5fma9aozwyrtmq0Q35MKbtJI/qd8dMSQmjQLVHhwrCxZydKuOVJER8ni+p2L/bx0aKey59ZEqUuXLippLonly5e7MBoAF8vDwzxRdO9ec1kRDs4eHmbCBlRV7OMlCG0hhTSTUhNlCYxVSLDDyRvZh6Xwtma96ix9u5SxQ/KPksViUXCwpIy/k0mLRfKtI2UkmfXC3LOzsZ+XDu1UtirA1xgAAIByYPGQmow3e4yykqXcTMnIN5dZyWZ5k3Fu6SWpUHJSpfxsydO/6PVe/uZ6hiiimqnmRwYAAFClRcZLbWZK4W2kvDQpa6+5DG9rlkdy8kbBEEXlnyl6fd4Zc311H6KIaqdSTQ8OAADgtMh48/ya9O1mr4hPuDncrrr3JBU4Z4iiAmPt1zFEEdUYiRIAAKj6LB5uO7+mwisYopg48e8hifXM8twsKWc/QxQrE8PKDwJliEQJAACguisYorhrtpT+p1mWl272JDUZxxDFyuBYgvn6Zez4+5wzX7OnsMl4Xr8LRKIEAACA/w1RPLFN2rBXav+WVDOOHonK4FiC2SOYfVLyj5L8/M1zzlITzXLOx7sg7PkAAAAwWTyk0Obm/6HNSZIqg7+vg6Xsk1JQI8krSLJ4msvAWHMY3q45Zj04hb0fAAAAqKzOuQ6WLBb7dY7XwYJTSJQAAACAyorrYJUbEiUAAACgsuI6WOWmTBKltLS0stgMAAAAAGcUXAfr7GHzulfnKrgOVkhzroN1AZxOlGbMmKHFixfbbg8ePFg1a9bUJZdcol9//bVMgwNQyRhWKT3J/D89iRNHAQAobwXXwfIJN6+DlZspGfnmMiuZ62BdBKdb7M0331TdunUlSStWrNCKFSv0zTffqE+fPpo0aVKZBwigkjiWICUMl34aY97+aYx5+1iCe+MCAKCqK7gOVngbKS9NytprLsPbMjX4RXD6OkqHDx+2JUpffvmlBg8erJ49eyomJkYdOnQo8wABVALnXr/Br76UK8k7lOs3AADgKgXXwUrfbk7c4BNuDrejJ+mCOd1y4eHhOnDggCTp22+/Vffu3SVJhmEoPz+/bKMDUPEVun5DoFnuFcj1GwAAcCWLhxQWJ9XqZC5Jki6K0z1KN910k4YNG6bGjRvrxIkT6tOnjyQpMTFRjRo1KvMAAVRwjtdvOPc8UsfrN4TFuS1MAAAAZzidKL388suKiYnRgQMH9PzzzysoKEiSdOjQId13331lHiCACq7g+g1+JVy/IfsI128AAACVitOJkre3tyZOnFio/MEHHyyTgABUMudev8ErqPB6rt8AAAAqIacTJUnavXu3Vq1apaNHj8pqtT/v4KmnniqTwABUEgXXb0hNNM9JOlfB9RvC23L9BgAAUKk4nSi98847uvfeexUREaE6derIYrHY1lksFhIloLopuH5D4sS/r9dQzyzPzZJy9nP9BgAAUCk5nSg988wzevbZZ/Xoo4+WRzwAKqOC6zfsmi2l/2mW5aWbPUlNxjE1OAAAqHScTpRSU1N1yy23lEcsACqzgus3nNgmbdgrtX9LqsnUpAAAoHJy+hvMLbfcou+++648YgFQ2Vk8pNDm5v+hzUmSAABApVWqHqVXX33V9n+jRo305JNPauPGjYqLi5O3t7dd3QkTJpRthAAAAADgYqVKlF5++WW720FBQVqzZo3WrFljV26xWEiUAAAAAFR6pUqUUlJSyjsOAAAAAKgwnD6BYNq0aTp9+nSh8jNnzmjatGllEhQAAAAAuJPTidLUqVOVmZlZqPz06dOaOnVqmQQFAABKybBKaduko2vNpWE9/30AAOfl9PTghmHYXWS2wK+//qoaNWqUSVAAAKAUjiWY1y/L2CHlZ0uevlJIM/Mi0Fy/DAAuSqkTpfDwcFksFlksFjVp0sQuWcrPz1dmZqbGjh1bLkECAAAHxxKkxIlS9knJP0ry85fyz0ipiWZ5m5kkSwBwEUqdKL3yyisyDEOjRo3S1KlTFRoaalvn4+OjmJgYXXXVVeUSJAAAOIdhNXuSsk9KQY2kgh8vvYKkwFgpK1naNce8CDTXMwOAC1LqRGnEiBGSpAYNGig+Pr7Q9ZMAAICLpG83h9v5R/0vSSpgsUi+daSMJLNeWJx7YgSASs7pc5Q6d+4sq9WqXbt26ejRo7Ja7U8a7dSpU5kFBwAAipCTap6T5Odf9Hovfyn7iFkPAHBBnE6UNm7cqGHDhmnfvn0yDMNuncViUX5+fpkFBwAAiuATbk7ckH/GHG7nKO+Mud4n3PWxAUAV4fTA5bFjx+qKK67Q77//rpMnTyo1NdX2d/LkyfKIEQAAnCu0hTm73dnDksOPljIMKfuwFNLcrAcAuCBO9yjt3r1bn3zyiRo1alQe8QAAgPOxeJhTgCdONCdu8K1jDrfLO2MmST7hUpNxTOQAABfB6SNohw4dtGfPnvKIBQAAlFZkvDkFeHgbKS9NytprLsPbMjU4AJQBp3uU7r//fj388MM6fPiw4uLiCs1+16pVqzILDgAAlCAy3pwCPH27OXGDT7g53I6eJAC4aE4nSoMGDZIkjRo1ylZmsVhkGAaTOQAA4GoWD6YAB4By4HSilJKSUh5xAAAAAECF4XSiVL9+/fKIAwAAAAAqDKcTJUlKTk7WK6+8oh07dkiSmjdvrn/84x+KjY0t0+AAAAAAwB2cPttz+fLlat68uX766Se1atVKrVq10qZNm9SiRQutWLGiPGIEAAAAAJdyukfpscce04MPPqh///vfhcofffRR9ejRo8yCAwAAAAB3cLpHaceOHRo9enSh8lGjRikpKalMggIAAAAAd3I6UYqMjNTWrVsLlW/dulW1atUqi5gAAAAAwK2cHnp3991365577tGff/6p+Hjzqt8bNmzQjBkz9NBDD5V5gAAAAADgak4nSk8++aSCg4P14osv6vHHH5ckRUdHa8qUKZowYUKZBwgAAAAAruZ0omSxWPTggw/qwQcf1KlTpyRJwcHBZR4YAAAAALjLBV1HqQAJEgAAAICqqNSJUrdu3UpVb+XKlRccDAAANoZVSt8u5aRKPuFSaAvJ4vQcRAAAXJBSJ0qrV69W/fr11a9fP3l7e5dnTACA6u5YgrRrtpSxQ8rPljx9pZBmUpPxUmS8u6MDAFQDpU6UZsyYoXnz5mnJkiW67bbbNGrUKLVs2bI8YwMAVEfHEqTEiVL2Sck/SvLzl/LPSKmJZnmbmSRLAIByV+oxDJMmTVJSUpKWLVumU6dO6eqrr1b79u315ptvKiMjozxjBABUF4bV7EnKPikFNZK8giSLp7kMjDWH4e2aY9YDAKAcOT3Y+6qrrtI777yjQ4cOady4cXr33XcVHR1NsgQAuHjp283hdv5RksViv85ikXzrSBlJZj0AAMrRBZ8Vu2XLFq1Zs0Y7duxQy5YtOW8JAHDxclL/PifJv+j1Xv7m+pxU18YFAKh2nEqU/vrrLz333HNq0qSJbr75ZtWoUUObNm3Sxo0b5e9fzIcaAACl5RNuTtyQf6bo9XlnzPU+4a6NCwBQ7ZR6Moe+fftq1apV6tmzp1544QX169dPXl4XdRkmAADshbYwZ7dLTTTPSTp3+J1hSNmHpfC2Zj0AAMpRqTOdb7/9VlFRUdq/f7+mTp2qqVOnFllvy5YtZRYcAKCasXiYU4AnTpSyks1zkrz8zZ6k7MNmT1KTcVxPCQBQ7kqdKE2ePLk84wAAwBQZb04BXnAdpewj5nC78LZmksTU4AAAFyBRAgBUPJHxUkRHc3a7nFSzJym0BT1JAACX4SQjAEDFZPGQwuLcHQUAoJripzkAAAAAcECiBAAAAAAOSJQAAAAAwIHTidLBgweLXbdx48aLCgYAAAAAKgKnE6WePXvq5MmThco3bNig3r17l0lQAAAAAOBOTidKHTt2VM+ePXXq1Clb2dq1a9W3b1+mEAcAAABQJTidKP3nP/9RvXr11L9/f2VnZ2vVqlXq16+fpk2bpgcffLA8YgQAAAAAl3I6UfLw8NBHH30kb29vdevWTTfccIOmT5+uf/zjH+URHwAAAAC4XKkuOPvbb78VKpsyZYqGDh2q4cOHq1OnTrY6rVq1KtsIAQAAAMDFSpUoXX755bJYLDIMw1ZWcPutt97S22+/LcMwZLFYlJ+fX27BAgAAAIArlCpRSklJKe84AAAAAKDCKFWiVL9+fUlSbm6uxowZoyeffFINGjQo18AAAAAAwF2cmszB29tbn376aXnFAgAAAAAVgtOz3g0cOFDLli0rh1AAAAAAoGIo1dC7czVu3FjTpk3Thg0b1K5dOwUGBtqtnzBhQpkFBwAAAADu4HSiNHfuXIWFhemXX37RL7/8YrfOYrGQKAEAAACo9JxOlJgBDwAAAEBV5/Q5SucyDMPu2koAAAAAUBVcUKL0/vvvKy4uTv7+/vL391erVq30wQcflHVsAAAAAOAWTg+9e+mll/Tkk09q/PjxuvrqqyVJ69ev19ixY3X8+HE9+OCDZR4kAAAAALiS04nSa6+9pjfeeEN33HGHreyGG25QixYtNGXKFBIlAAAAAJWe00PvDh06pPj4+ELl8fHxOnToUJkEBQAAAADu5HSi1KhRI3388ceFyhcvXqzGjRuXSVAAAAAA4E5OD72bOnWqhgwZorVr19rOUdqwYYN++OGHIhMoAAAAAKhsnE6UBg0apE2bNunll1/WsmXLJEnNmjXTTz/9pDZt2pR1fACAaspqlbZvl1JTpfBwqUULyeOiLmoBAGWHY1TVV+pEafLkybruuuvUsWNHtWvXTh9++OFFP/jatWv1wgsv6JdfftGhQ4e0dOlSDRw4sMi6Y8eO1VtvvaWXX35ZDzzwwEU/NgCg4kpIkGbPlnbskLKzJV9fqVkzafx4qYjTZAHApThGVQ+lznvff/99denSRWFhYbruuuv07LPPKiEhQXl5eRf84FlZWWrdurXmzJlTYr2lS5dq48aNio6OvuDHAgBUDgkJ0sSJ0pYtUliYFBNjLhMTzfKEBDcHCKBa4xhVfZQ6UUpJSdGff/6pOXPm6NJLL9U777yja665RuHh4erdu7dmzJihn376yakH79Onj5555hndeOONxdb573//q/vvv18LFiyQt7e3U9sHAFQuVqv5K+3Jk1KjRlJQkOTpaS5jY80hLnPmmPUAwNU4RlUvTp2jFBMTozvvvFN33nmnJDN5WrVqlVavXq3nnntO//znPy+qh8mR1WrV7bffrkmTJqlFixaluk92drays7NttzMyMiRJubm5ys3NLbPYLkTB47s7juqENnc92ty1qlp7JyVJf/4p1a8v+fgUXl+vnpScLG3bJjVv7vr4pKrX5pUBbe5atHfxyusYRZu7jjNtbDEMw7iQB9m3b59Wr16tlStXas2aNTp69Kg6duyolStXXsjmZLFYCp2jNH36dK1atUrLly+XxWJRTEyMHnjggRLPUZoyZYqmTp1aqHzhwoUKCAi4oNgAAAAAVH6nT5/WsGHDlJ6erpCQkBLrlrpHaf/+/Vq9erWtB+n48eOKj49X586ddffdd6t9+/byKSq1vkC//PKLZs2apS1btshisZT6fo8//rgeeugh2+2MjAzVrVtXPXv2PG9jlLfc3FytWLFCPXr0YBihi9Dmrkebu1ZVa++kJGnMGCk0VAoMLLw+K0tKT5feesu9PUpVqc0rA9rctWjv4pXXMYo2d52C0WalUepEKSYmRvXq1dO9996re++9V+3atZOnp+cFBVga69at09GjR1WvXj1bWX5+vh5++GG98sor2rt3b5H38/X1la+vb6Fyb2/vCrPjVaRYqgva3PVoc9eqKu0dFyc1bGieFB0bK537O5lhSPv3S23bmvXcPQ1vVWnzyoQ2dy3au7DyPkbR5uXPmfYt9Us4ePBgZWdna8aMGXrmmWf0yiuvaMuWLbrAkXvndfvtt+u3337T1q1bbX/R0dGaNGmSli9fXi6PCQBwLw8Pc3rd8HBznH9mppSfby6Tk83ycePcnyQBqJ44RlUvpe5R+uijjyRJf/zxh2343QsvvKCzZ8/qmmuuUefOndWlSxddeeWVpX7wzMxM7dmzx3Y7JSVFW7duVY0aNVSvXj3VrFnTrr63t7fq1Kmjyy67rNSPAQCoXOLjpZkz/3eNkiNHzGuUtG1rfgHhGiUA3IljVPXh1Kx3ktS0aVM1bdpU9957ryQpKSlJCxcu1DPPPKPHH3/cqVnvNm/erK5du9puF5xbNGLECM2fP9/Z0AAAVUR8vNSxI1e9B1AxcYyqHpxOlCTpyJEjWr16tW1yh127dsnX11fXXnutU9vp0qWLU0P3ijsvCQBQ9Xh4mOP8AaAi4hhV9ZU6Ufr4449tydHOnTvl7e2tK6+8UoMHD1bXrl0VHx9f5CQKAAAAAFDZlDpRGj58uK644grdeOON6tq1q66++mr5+/uXZ2wAAAAA4BalTpRSU1MVWNSE8QAAAABQxZT6lDOSJAAAAADVBXNzAAAAAIADEiUAAAAAcECiBAAAAAAOLjhR2rNnj5YvX64zZ85IklPXQwIAAAAqNcMqpW2Tjq41l4bV3RGhjDl9wdkTJ05oyJAhWrlypSwWi3bv3q2GDRtq9OjRCg8P14svvlgecQIAAAAVw7EEaddsKWOHlJ8tefpKIc2kJuOlyHh3R4cy4nSP0oMPPigvLy/t379fAQEBtvIhQ4bo22+/LdPgAAAAgArlWIKUOFE6uUXyDpMCY8xlaqJZfizBzQGirDjdo/Tdd99p+fLluvTSS+3KGzdurH379pVZYAAAAECFYljNnqTsk1JQI8liMcu9gqTAWCkrWdo1R4roKFmYCqCyc/oVzMrKsutJKnDy5En5+vqWSVAAAABAhZO+3Rxu5x/1vySpgMUi+daRMpLMeqj0nE6Urr32Wr3//vu22xaLRVarVc8//7y6du1apsEBAAAAFUZO6t/nJPkXvd7L31yfk+rauFAunB569/zzz+u6667T5s2blZOTo0ceeUTbt2/XyZMntWHDhvKIEQAAAHA/n3Bz4ob8M+ZwO0d5Z8z1PuGujw1lzukepZYtW2rXrl265pprNGDAAGVlZemmm25SYmKiYmNjyyNGAAAAwP1CW5iz2509LDleGscwpOzDUkhzsx4qPad7lCQpNDRU//znP8s6FgAAAKDisniYU4AnTjQnbvCtYw63yztjJkk+4VKTcUzkUEU4/So2atRIU6ZM0e7du8sjHgAAAKDiioyX2syUwttIeWlS1l5zGd7WLOc6SlWG0z1K48aN08KFCzVt2jS1a9dOw4cP15AhQ1SnTp3yiA8AAACoWCLjzSnA07ebEzf4hJvD7ehJqlIu6IKzP//8s/744w/17dtXc+bMUd26ddWzZ0+72fAAAACAKsviIYXFSbU6mUuSpCrngl/RJk2aaOrUqdq1a5fWrVunY8eO6c477yzL2AAAAADALS5oMocCP/30kxYuXKjFixcrIyNDt9xyS1nFBQAAAABu43SitGvXLi1YsECLFi1SSkqKunXrphkzZuimm25SUFAR88kDAAAAQCXjdKLUtGlTXXnllRo3bpxuvfVW1a5duzziAgAAAAC3cTpR2rlzpxo3blwesQAAAABAheD0ZA4kSQAAAACqulL1KNWoUUO7du1SRESEwsPDZbFYiq178uTJMgsOAAAAANyhVInSyy+/rODgYNv/JSVKAAAAAFDZlSpRGjFihO3/kSNHllcsAAAAAFAhOH2Okqenp44ePVqo/MSJE/L09CyToAAAAADAnZxOlAzDKLI8OztbPj4+Fx0QAAAAALhbqacHf/XVVyVJFotF//nPf+wuLpufn6+1a9eqadOmZR8hAAAAALhYqROll19+WZLZo/Tmm2/aDbPz8fFRTEyM3nzzzbKPEAAAAABcrNSJUkpKiiSpa9eu+uyzzxQeHl5uQQEAAACAO5U6USqwatWq8ogDAAAAACoMpydzGDRokGbMmFGo/Pnnn9ctt9xSJkEBAAAAgDs5nSitXbtWffv2LVTep08frV27tkyCAgAAAAB3cjpRyszMLHIacG9vb2VkZJRJUAAAAADgTk4nSnFxcVq8eHGh8o8++kjNmzcvk6AAAAAAwJ2cnszhySef1E033aTk5GR169ZNkvTDDz9o0aJFWrJkSZkHCAAAAACu5nSi1L9/fy1btkzPPfecPvnkE/n7+6tVq1b6/vvv1blz5/KIEQAAAABcyulESZL69eunfv36FSr//fff1bJly4sOCgAAAADcyelzlBydOnVKb7/9ttq3b6/WrVuXRUwAAAAA4FYXnCitXbtWd9xxh6KiojRz5kx169ZNGzduLMvYAAAAAMAtnBp6d/jwYc2fP19z585VRkaGBg8erOzsbC1btowZ7wAAAABUGaXuUerfv78uu+wy/fbbb3rllVf0119/6bXXXivP2AAAAADALUrdo/TNN99owoQJuvfee9W4cePyjAkAAFRmhlVK3y7lpEo+4VJoC8ly0adFA4BLlfqotX79ep06dUrt2rVThw4dNHv2bB0/frw8YwMAAJXNsQQpYbj04x3ST2PNZcJwsxwAKpFSJ0odO3bUO++8o0OHDmnMmDH66KOPFB0dLavVqhUrVujUqVPlGScAAKjojiVIiROlk1sk7zApMMZcpiaa5SRLACoRp/vBAwMDNWrUKK1fv17btm3Tww8/rH//+9+qVauWbrjhhvKIEQAAVHSGVdo1W8o+KQU1kryCJIunuQyMNYfh7Zpj1gOASuCiBgxfdtllev7553Xw4EEtWrSorGICAACVTfp2KWOH5B8lWSz26ywWybeOlJFk1gOASsCp6cGL4+npqYEDB2rgwIFlsTm4CifbAgDKSk6qlJ8t+fkXvd7LX8o+YtYDgEqgTBIlVELHEswhEhk7zA82T18ppJnUZLwUGe/u6AAAlY1PuPlZkn/GHG7nKO+Mud4n3PWxAcAFoPugOuJkWwBAWQttYf7gdvawZBj26wxDyj4shTQ36wFAJUCiVN1wsi0AoDxYPMxRCT7hUlaylJspGfnmMivZLG8yjiHeACoNjlbVDSfbAgDKS2S81GamFN5GykuTsvaay/C2ZjlDuwFUIpyjVN1wsi0AoDxFxksRHZksCEClR6JU3XCyLQCgvFk8pLA4d0cBABeFn3eqG062BQAAAM6LRKm64WRbAAAA4Lz4NlwdcbItAAAAUCLOUaquONkWAAAAKBaJUnXGybYAAABAkeg+AAAAAAAHJEoAAAAA4IBECQAAAAAckCgBAAAAgAMSJQAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAADjggrMAAADuYFil9O1STqrkEy6FtjAvBg+gQiBRAgAAcLVjCdKu2VLGDik/W/L0lUKaSU3GS5Hx7o4OgBh6BwAA4FrHEqTEidLJLZJ3mBQYYy5TE83yYwluDhCARKIEAADgOobV7EnKPikFNZK8giSLp7kMjDWH4e2aY9YD4FYkSgAAAK6Svt0cbucfJVks9ussFsm3jpSRZNYD4FYkSgAAAK6Sk/r3OUn+Ra/38jfX56S6Ni4AhZAoAQAAuIpPuDlxQ/6ZotfnnTHX+4S7Ni4AhZAoAYCrGVYpbZt0dK255FwEoPoIbWHObnf2sGQY9usMQ8o+LIU0N+sBcCumBwcAV2JKYKB6s3iY7/fEiVJWsnlOkpe/2ZOUfdjsSWoyjuspARUA70IAcBWmBAYgmT+KtJkphbeR8tKkrL3mMrytWc6PJkCFQI8SALiC45TABbNdFUwJnJVsTgkc0ZFfkoHqIDLefL+nbzcnbvAJN4fb8f5HVWNYK+1+TqIEAK7gzJTAYXGuj68Sf5ABlZbFwz3vd8BVKvlwcxIlAHCFgimB/UqYEjj7iHumBK7kH2QAgAqoYLh59knzR0I/f3O2x4Lh5pVgmCk/FwKAK1TUKYE5bwoAUNYch5t7BUkWz/8NN89JNYebV/BZX0mUAMAVKuKUwFXkgwwAUME4M9y8AiNRAgBXKJgS2CfcnLghN1My8s1lVrJ7pgSuIh9kAIAKpmC4uWcJw83zs90z3NwJJEouYrVKSUnm/0lJ5m13s1qlbduktWvNZUWICajS/p4S2AhrozOn0pR1bK/OnEqT4a4pgc/5IDMkZZySTpw0l4ZUaT7IXKkiHssrIj5fSod2QpV1znDzIj9f3DXc3ElM5uACCQnS7NnSn39Kjz8ujRkjNWwojR8vxbvpHLaCmHbskLKzJV9fqVkz98YEVAcJu+M15/WOyjm2XX6eqTqbHy6fyBYaN95D8ZEuDubvD7K0k2f05/4gnTplflHz8JCCg6WG9c4oLKDif5C5SkU8lldEfL6UDu2EKu3v4ean/5uoP/4bq1OnLOd8vhhqeslhBVzS1rXDzS+AW3uU1q5dq/79+ys6OloWi0XLli2zWz9lyhQ1bdpUgYGBCg8PV/fu3bVp0yb3BHuBEhKkiROlLVuk0FCzLDRUSkw0yxPccJ70uTGFhUkxMebSnTEB1UHBe++XLR46nhenEx6ddDwvTlsSPdzz3gttoWM5zZR26LDS0w15e0sBAZK3t5Sebijt0GEdy3HxeVMVVEU8lldEfL6UDu2EKs/ioW3Z45V8IFyhHskK8c9UYEC+QvwzFeqZrOQD4dqW7eLh5hfArdFlZWWpdevWmjNnTpHrmzRpotmzZ2vbtm1av369YmJi1LNnTx07dszFkV4Yq9X8tejkSalRIykw0CwPDJRiY6XUVGnOHNd2tTvGFBQkeXqaS3fFBFQHFfG9ZzU8NPu78UrNClds7WQF+WXKw5KvIL9MxdZOVmpWuOasGCerUbE/yMpbRTyWV0QVcR+viGgnVAdWqzT9P/F67puZOpDVRiH+aaoTvFch/mk6kNlWz307U/+eG1/h93O3Dr3r06eP+vTpU+z6YcOG2d1+6aWXNHfuXP3222+67rrrirxPdna2srOzbbczMjIkSbm5ucrNzS2DqEsvKckcolG/vuTjI3l7m49fsKxXT0pONsclN2/unpgcuSOm8lTwmrv6ta/OaPOildd772LaOylJWv7LlUpLe143xr2t6OBdCvZPU26+r5LT22vZtru1KflK3bgtt0ocDy5URTyWV0Tl+flSlY4rleFzuCq1d2VR1dq8YD8PDb1Sr22ep0tD/lCQT5oyc8J0MKOpMj08lJ6c65b93Jk2thiG4zy17mGxWLR06VINHDiwyPU5OTl69dVX9cwzz2jPnj2KiIgost6UKVM0derUQuULFy5UQEBAWYYMAAAAoBI5ffq0hg0bpvT0dIWEhJRYt8InSl9++aVuvfVWnT59WlFRUVq2bJmuvPLKYrdTVI9S3bp1dfz48fM2RllLSjJP9u0Qu0k3xr2t2iF7teuSh9Xkvy/qSEaMlm67R5uSO+itt1zbozRmjDm2vmD4yLmysqT0dLk0pvKUm5urFStWqEePHvL29nZ3ONUCbV608nrvXUx7V7fjwYVybCdv71wNG7ZCCxf2UG6uN+30t/Lcn6rScaUyvO+qUntXFlWtzSvyfp6RkaGIiIhSJUoVfta7rl27auvWrTp+/LjeeecdDR48WJs2bVKtWrWKrO/r6ytfX99C5d7e3i7f8eLipF7tEjSw3iOKCDmpE9n1JUnZOf5qGPqzRrbao/DwmYqLi5eHi04BiIszZ2lKTDTHQp976RTDkPbvl9q2Neu5KiZXcMfrX93R5vbK+713Ie1dXY8HznJspwK5ud7KyfGmnf7miv2pKhxXKtP7riq0d2VTVdq8Iu/nzrRvhT+kBwYGqlGjRurYsaPmzp0rLy8vzZ07191hlYqHxarxPWcrPPCk9hxppMxsM6XOzA5U8pFYhQemalyPOfKwuO5MNg8Pc+rR8HBzDHRmppSfby6Tk83ycePcf3AGqpqK+N6riDFVRI7tlJVllmdl0U7nYn8qHdoJ1UFV2c8reHiFWa1Wu6F1FVr6dkX67FBYVJRCQy3K+/vcsbxcKTTUorCoOor0cf1V7+PjpZkzpTZtpLQ0ae9ec9m2rVnO9RuA8lER33sVMaaK6Nx2Sk83y9LTaSdH7E+lQzuhOqgK+7lbh95lZmZqz549ttspKSnaunWratSooZo1a+rZZ5/VDTfcoKioKB0/flxz5szRf//7X91yyy1ujNoJf1/1PqyGv9rUlFIzpHWG1KqVFB4iWQx/KeuIW656Hx8vdewobd9uTkUaHi61aFHxM3ugsquI772KGFNFVNBO27aZH/hvvVUxhkdVNOxPpUM7oTqo7Pu5WxOlzZs3q2vXrrbbDz30kCRpxIgRevPNN/XHH3/ovffe0/Hjx1WzZk1deeWVWrdunVq0qCQXP/z7qvfKPyOLV5CCgyVlmFe8t0hS3hlzvZuueu/hYX7IA3Ctivjeq4gxVUQeHuaJx3v3msvK8mHvauxPpUM7oTqozPu5WxOlLl26qKRJ9z777DMXRlMOQltIIc2k1EQpMNZ+nWFI2Yel8LZc9R4AAACoYPgtrDxZPKQm480eo6xkKffvM4Bzs8zbPuFSk3FmPQAAAAAVBt/Qy1tkvNRmphTeRsr7+wzgvHSzJ6nNTHM9AAAAgAqlwl9HqUqIjJciOkontkkb9krt35JqxtGTBAAAAFRQfFN3FYuHFPr3pYdDm5MkAQAAABUY39YBAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4IBECQAAAAAckCgBAAAAgAMSJQAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcECiBAAAAAAOSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAQGkZVik9yfw/Pcm8DQCokkiUAAAojWMJUsJw6acx5u2fxpi3jyW4Ny4AQLkgUQIA4HyOJUiJE6WTWyTvULPMO1RKTTTLSZYAoMohUQIAoCSGVdo1W8o+KQU1krwCzXKvQCkwVspJlXbNYRgeAFQxJEoAAJQkfbuUsUPyj5IsFvt1FovkW0fKSDLrAQCqDBIlAABKkpMq5WdLnv5Fr/fyN9fnpLo2LgBAuSJRAgCgJD7hkqevlH+m6PV5Z8z1PuGujQsAUK5IlAAAKEloCymkmXT2sGQY9usMQ8o+LIU0N+sBAKoMEiUAAEpi8ZCajDd7jLKSpdwsszw3y7ztEy41GWfWAwBUGRzVAQA4n8h4qc1MKbyNlJduluWlS+FtzfLIePfGBwAoc17uDgAAgEohMl6K6Cid2CZt2Cu1f0uqGUdPEgBUURzdAQAoLYuHFNrc/D+0OUkSAFRhHOEBAAAAwAGJEgAAAAA4IFECAAAAAAdM5gCcj2GV0rdLOanmNMChLTgvAQAAoIojUQJKcixB2jVbytgh5WdLnr7mhSebjGc6YAAAgCqMn8WB4hxLkBInSie3SN5hUmCMuUxNNMuPJbg5QAAAAJQXEiWgKIbV7EnKPikFNZK8giSLp7kMjDWH4e2aY9YDAABAlUOiBBQlfbs53M4/SrJY7NdZLJJvHSkjyawHAACAKodECShKTurf5yT5F73ey99cn5Pq2rgAAADgEiRKQFF8ws2JG/LPFL0+74y53ifctXEBAADAJUiUgKKEtjBntzt7WDIM+3WGIWUflkKam/UAAABQ5ZAoAUWxeJhTgPuES1nJUm6mZOSby6xks7zJOK6nBAAAUEXxLQ8oTmS81GamFN5GykuTsvaay/C2ZjnXUQIAAKiyuOAsUJLIeCmiozm7XU6q2ZMU2oKeJAAAgCqORAk4H4uHFBbn7igAAADgQvwsDgAAAAAOSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4IBECQAAAAAckCgBAAAAgAMSJQAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcODl7gCAis5qlbZvl1JTpfBwqUULyYOfGAAAAKo0t37dW7t2rfr376/o6GhZLBYtW7bMti43N1ePPvqo4uLiFBgYqOjoaN1xxx3666+/3Bcwqp2EBGn4cOmOO6SxY83l8OFmOQAAAKoutyZKWVlZat26tebMmVNo3enTp7VlyxY9+eST2rJliz777DPt3LlTN9xwgxsiRXWUkCBNnCht2SKFhUkxMeYyMdEsJ1kCAACoutw69K5Pnz7q06dPketCQ0O1YsUKu7LZs2erffv22r9/v+rVq+eKEFFNWa3S7NnSyZNSo0aSxWKWBwVJsbFScrI0Z47UsSPD8AAAAKqiSnWOUnp6uiwWi8LCwoqtk52drezsbNvtjIwMSeZQvtzc3PIOsUQFj+/uOKqTC23zpCTpzz+l+vUlH5/C6+vVM5Olbduk5s3LItKqg/3ctWhv16PNXY82dy3a2/Voc9dxpo0thmEY5RhLqVksFi1dulQDBw4scv3Zs2d19dVXq2nTplqwYEGx25kyZYqmTp1aqHzhwoUKCAgoq3ABAAAAVDKnT5/WsGHDlJ6erpCQkBLrVopEKTc3V4MGDdLBgwe1evXqEp9UUT1KdevW1fHjx8/bGOUtNzdXK1asUI8ePeTt7e3WWKqLC23zpCRpzBgpNFQKDCy8PitLSk+X3nqLHiVH7OeuRXu7Hm3uerS5a9Herkebu05GRoYiIiJKlShV+KF3ubm5Gjx4sPbt26eVK1ee9wn5+vrK19e3ULm3t3eF2fEqUizVhbNtHhcnNWxoTtwQG/u/c5QkyTCk/fultm3NepyjVDT2c9eivV2PNnc92ty1aG/Xo83LnzPtW6G/4hUkSbt379b333+vmjVrujskVBMeHtL48eZ1k5KTpcxMKT/fXCYnm+XjxpEkAQAAVFVu7VHKzMzUnj17bLdTUlK0detW1ahRQ1FRUbr55pu1ZcsWffnll8rPz9fhw4clSTVq1JBPUWfYA2UoPl6aOdOc/W7HDunIEcnX1+xJGjfOXA8AAICqya2J0ubNm9W1a1fb7YceekiSNGLECE2ZMkX/93//J0m6/PLL7e63atUqdenSxVVhohqLjzenAN++XUpNNXuSWrSgJwkAAKCqc2ui1KVLF5U0l0QFmWcC1ZyHh3kuEgAAAKoPfhcHAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4IBECQAAAAAckCgBAAAAgAMSJQAAAABw4OXuAMqbYRiSpIyMDDdHIuXm5ur06dPKyMiQt7e3u8OpFmhz16PNXYv2dj3a3PVoc9eivV2PNnedgpygIEcoSZVPlE6dOiVJqlu3rpsjAQAAAFARnDp1SqGhoSXWsRilSacqMavVqr/++kvBwcGyWCxujSUjI0N169bVgQMHFBIS4tZYqgva3PVoc9eivV2PNnc92ty1aG/Xo81dxzAMnTp1StHR0fLwKPkspCrfo+Th4aFLL73U3WHYCQkJ4U3gYrS569HmrkV7ux5t7nq0uWvR3q5Hm7vG+XqSCjCZAwAAAAA4IFECAAAAAAckSi7k6+uryZMny9fX192hVBu0uevR5q5Fe7sebe56tLlr0d6uR5tXTFV+MgcAAAAAcBY9SgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAolTG5syZo5iYGPn5+alDhw766aefSqy/ZMkSNW3aVH5+foqLi9PXX3/tokgrv+nTp+vKK69UcHCwatWqpYEDB2rnzp0l3mf+/PmyWCx2f35+fi6KuPKbMmVKofZr2rRpifdhH784MTExhdrcYrFo3LhxRdZnH3fO2rVr1b9/f0VHR8tisWjZsmV26w3D0FNPPaWoqCj5+/ure/fu2r1793m36+xnQXVSUpvn5ubq0UcfVVxcnAIDAxUdHa077rhDf/31V4nbvJBjU3Vyvv185MiRhdqvd+/e590u+3nRztfeRR3TLRaLXnjhhWK3yT7uHiRKZWjx4sV66KGHNHnyZG3ZskWtW7dWr169dPTo0SLrJyQkaOjQoRo9erQSExM1cOBADRw4UL///ruLI6+c1qxZo3Hjxmnjxo1asWKFcnNz1bNnT2VlZZV4v5CQEB06dMj2t2/fPhdFXDW0aNHCrv3Wr19fbF328Yv3888/27X3ihUrJEm33HJLsfdhHy+9rKwstW7dWnPmzCly/fPPP69XX31Vb775pjZt2qTAwED16tVLZ8+eLXabzn4WVDcltfnp06e1ZcsWPfnkk9qyZYs+++wz7dy5UzfccMN5t+vMsam6Od9+Lkm9e/e2a79FixaVuE328+Kdr73PbedDhw7p3XfflcVi0aBBg0rcLvu4GxgoM+3btzfGjRtnu52fn29ER0cb06dPL7L+4MGDjX79+tmVdejQwRgzZky5xllVHT161JBkrFmzptg68+bNM0JDQ10XVBUzefJko3Xr1qWuzz5e9v7xj38YsbGxhtVqLXI9+/iFk2QsXbrUdttqtRp16tQxXnjhBVtZWlqa4evrayxatKjY7Tj7WVCdObZ5UX766SdDkrFv375i6zh7bKrOimrzESNGGAMGDHBqO+znpVOafXzAgAFGt27dSqzDPu4e9CiVkZycHP3yyy/q3r27rczDw0Pdu3fXjz/+WOR9fvzxR7v6ktSrV69i66Nk6enpkqQaNWqUWC8zM1P169dX3bp1NWDAAG3fvt0V4VUZu3fvVnR0tBo2bKjbbrtN+/fvL7Yu+3jZysnJ0YcffqhRo0bJYrEUW499vGykpKTo8OHDdvtwaGioOnToUOw+fCGfBShZenq6LBaLwsLCSqznzLEJha1evVq1atXSZZddpnvvvVcnTpwoti77edk5cuSIvvrqK40ePfq8ddnHXY9EqYwcP35c+fn5ql27tl157dq1dfjw4SLvc/jwYafqo3hWq1UPPPCArr76arVs2bLYepdddpneffddff755/rwww9ltVoVHx+vgwcPujDayqtDhw6aP3++vv32W73xxhtKSUnRtddeq1OnThVZn328bC1btkxpaWkaOXJksXXYx8tOwX7qzD58IZ8FKN7Zs2f16KOPaujQoQoJCSm2nrPHJtjr3bu33n//ff3www+aMWOG1qxZoz59+ig/P7/I+uznZee9995TcHCwbrrpphLrsY+7h5e7AwDKwrhx4/T777+fd7zuVVddpauuusp2Oz4+Xs2aNdNbb72lp59+urzDrPT69Olj+79Vq1bq0KGD6tevr48//rhUv4bh4sydO1d9+vRRdHR0sXXYx1FV5ObmavDgwTIMQ2+88UaJdTk2XZxbb73V9n9cXJxatWql2NhYrV69Wtddd50bI6v63n33Xd12223nnXSHfdw96FEqIxEREfL09NSRI0fsyo8cOaI6deoUeZ86deo4VR9FGz9+vL788kutWrVKl156qVP39fb2Vps2bbRnz55yiq5qCwsLU5MmTYptP/bxsrNv3z59//33uuuuu5y6H/v4hSvYT53Zhy/kswCFFSRJ+/bt04oVK0rsTSrK+Y5NKFnDhg0VERFRbPuxn5eNdevWaefOnU4f1yX2cVchUSojPj4+ateunX744QdbmdVq1Q8//GD36+65rrrqKrv6krRixYpi68OeYRgaP368li5dqpUrV6pBgwZObyM/P1/btm1TVFRUOURY9WVmZio5ObnY9mMfLzvz5s1TrVq11K9fP6fuxz5+4Ro0aKA6derY7cMZGRnatGlTsfvwhXwWwF5BkrR79259//33qlmzptPbON+xCSU7ePCgTpw4UWz7sZ+Xjblz56pdu3Zq3bq10/dlH3cRd88mUZV89NFHhq+vrzF//nwjKSnJuOeee4ywsDDj8OHDhmEYxu2332489thjtvobNmwwvLy8jJkzZxo7duwwJk+ebHh7exvbtm1z11OoVO69914jNDTUWL16tXHo0CHb3+nTp211HNt86tSpxvLly43k5GTjl19+MW699VbDz8/P2L59uzueQqXz8MMPG6tXrzZSUlKMDRs2GN27dzciIiKMo0ePGobBPl5e8vPzjXr16hmPPvpooXXs4xfn1KlTRmJiopGYmGhIMl566SUjMTHRNsPav//9byMsLMz4/PPPjd9++80YMGCA0aBBA+PMmTO2bXTr1s147bXXbLfP91lQ3ZXU5jk5OcYNN9xgXHrppcbWrVvtju3Z2dm2bTi2+fmOTdVdSW1+6tQpY+LEicaPP/5opKSkGN9//73Rtm1bo3HjxsbZs2dt22A/L73zHVcMwzDS09ONgIAA44033ihyG+zjFQOJUhl77bXXjHr16hk+Pj5G+/btjY0bN9rWde7c2RgxYoRd/Y8//tho0qSJ4ePjY7Ro0cL46quvXBxx5SWpyL958+bZ6ji2+QMPPGB7fWrXrm307dvX2LJli+uDr6SGDBliREVFGT4+PsYll1xiDBkyxNizZ49tPft4+Vi+fLkhydi5c2ehdezjF2fVqlVFHkcK2tRqtRpPPvmkUbt2bcPX19e47rrrCr0O9evXNyZPnmxXVtJnQXVXUpunpKQUe2xftWqVbRuObX6+Y1N1V1Kbnz592ujZs6cRGRlpeHt7G/Xr1zfuvvvuQgkP+3npne+4YhiG8dZbbxn+/v5GWlpakdtgH68YLIZhGOXaZQUAAAAAlQznKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4IBECQAAAAAckCgBAKoFi8WiZcuWuTsMAEAlQaIEAKjwRo4cqYEDB7o7DABANUKiBAAAAAAOSJQAAJVKly5dNGHCBD3yyCOqUaOG6tSpoylTptjV2b17tzp16iQ/Pz81b95cK1asKLSdAwcOaPDgwQoLC1ONGjU0YMAA7d27V5L0xx9/KCAgQAsXLrTV//jjj+Xv76+kpKTyfHoAgAqCRAkAUOm89957CgwM1KZNm/T8889r2rRptmTIarXqpptuko+PjzZt2qQ333xTjz76qN39c3Nz1atXLwUHB2vdunXasGGDgoKC1Lt3b+Xk5Khp06aaOXOm7rvvPu3fv18HDx7U2LFjNWPGDDVv3twdTxkA4GIWwzAMdwcBAEBJRo4cqbS0NC1btkxdunRRfn6+1q1bZ1vfvn17devWTf/+97/13XffqV+/ftq3b5+io6MlSd9++6369OmjpUuXauDAgfrwww/1zDPPaMeOHbJYLJKknJwchYWFadmyZerZs6ck6frrr1dGRoZ8fHzk6empb7/91lYfAFC1ebk7AAAAnNWqVSu721FRUTp69KgkaceOHapbt64tSZKkq666yq7+r7/+qj179ig4ONiu/OzZs0pOTrbdfvfdd9WkSRN5eHho+/btJEkAUI2QKAEAKh1vb2+72xaLRVartdT3z8zMVLt27bRgwYJC6yIjI23///rrr8rKypKHh4cOHTqkqKioCw8aAFCpkCgBAKqUZs2a6cCBA3aJzcaNG+3qtG3bVosXL1atWrUUEhJS5HZOnjypkSNH6p///KcOHTqk2267TVu2bJG/v3+5PwcAgPsxmQMAoErp3r27mjRpohEjRujXX3/VunXr9M9//tOuzm233aaIiAgNGDBA69atU0pKilavXq0JEybo4MGDkqSxY8eqbt26+te//qWXXnpJ+fn5mjhxojueEgDADUiUAABVioeHh5YuXaozZ86offv2uuuuu/Tss8/a1QkICNDatWtVr1493XTTTWrWrJlGjx6ts2fPKiQkRO+//76+/vprffDBB/Ly8lJgYKA+/PBDvfPOO/rmm2/c9MwAAK7ErHcAAAAA4IAeJQAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcECiBAAAAAAOSJQAAAAAwMH/A4olomljngM4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Membuat DataFrame untuk mempermudah visualisasi\n",
    "comparison_df = pd.DataFrame({'Actual': y_valid, 'Predicted': y_pred_valid})\n",
    "\n",
    "# Membatasi hanya pada 20 indeks pertama\n",
    "comparison_df_subset = comparison_df.iloc[:20]\n",
    "\n",
    "# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', alpha=0.7, color='blue')\n",
    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', alpha=0.7, color='orange')\n",
    "plt.title('Comparison of Actual vs Predicted Active Work Months (First 20)')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Active Work Months')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Line plot untuk membandingkan prediksi dan nilai asli\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', marker='o', linestyle='-', color='blue')\n",
    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
    "plt.title('Actual vs Predicted Active Work Months (First 20 - Line Plot)')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Active Work Months')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Tugas Akhir\\Codingan\\Development\\App\\.venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "[I 2025-04-04 22:29:56,300] A new study created in memory with name: no-name-5f450915-e8ec-423f-af93-dc5cee2abef1\n",
      "[I 2025-04-04 22:30:38,921] Trial 0 finished with value: 5.027911535965598 and parameters: {'iterations': 828, 'learning_rate': 0.003039755066748184, 'depth': 5, 'subsample': 0.6406811688382896, 'colsample_bylevel': 0.6700948872717083, 'l2_leaf_reg': 10.502812317409354, 'random_strength': 7.116328800518758}. Best is trial 0 with value: 5.027911535965598.\n",
      "[I 2025-04-04 22:31:06,178] Trial 1 finished with value: 2.389210818104853 and parameters: {'iterations': 572, 'learning_rate': 0.011984055286167236, 'depth': 4, 'subsample': 0.7785668325606546, 'colsample_bylevel': 0.6268870831020625, 'l2_leaf_reg': 17.958517174882992, 'random_strength': 9.80620110760972}. Best is trial 1 with value: 2.389210818104853.\n",
      "[I 2025-04-04 22:32:03,560] Trial 2 finished with value: 1.2183257399000136 and parameters: {'iterations': 931, 'learning_rate': 0.044751273914746106, 'depth': 4, 'subsample': 0.6335317366011604, 'colsample_bylevel': 0.6138467285692508, 'l2_leaf_reg': 14.839072862003002, 'random_strength': 6.729014821556904}. Best is trial 2 with value: 1.2183257399000136.\n",
      "[I 2025-04-04 22:32:48,429] Trial 3 finished with value: 1.6772365947625778 and parameters: {'iterations': 572, 'learning_rate': 0.01861659022526599, 'depth': 6, 'subsample': 0.64495760934697, 'colsample_bylevel': 0.7511164770029715, 'l2_leaf_reg': 11.367556185206242, 'random_strength': 7.97273031406439}. Best is trial 2 with value: 1.2183257399000136.\n",
      "[I 2025-04-04 22:33:32,618] Trial 4 finished with value: 8.183300284194496 and parameters: {'iterations': 826, 'learning_rate': 0.002056116564827396, 'depth': 4, 'subsample': 0.704865024844018, 'colsample_bylevel': 0.5970286838240058, 'l2_leaf_reg': 17.47749651638024, 'random_strength': 7.52312541667942}. Best is trial 2 with value: 1.2183257399000136.\n",
      "[I 2025-04-04 22:34:16,561] Trial 5 finished with value: 2.8397106470259685 and parameters: {'iterations': 717, 'learning_rate': 0.006038283828656678, 'depth': 5, 'subsample': 0.7680919505749417, 'colsample_bylevel': 0.6166810568075495, 'l2_leaf_reg': 16.620239803580432, 'random_strength': 7.133959785015604}. Best is trial 2 with value: 1.2183257399000136.\n",
      "[I 2025-04-04 22:34:42,712] Trial 6 finished with value: 0.9537960099286911 and parameters: {'iterations': 528, 'learning_rate': 0.09823593507100707, 'depth': 4, 'subsample': 0.618352254564517, 'colsample_bylevel': 0.6608849872143843, 'l2_leaf_reg': 13.07772743889768, 'random_strength': 6.546889911070138}. Best is trial 6 with value: 0.9537960099286911.\n",
      "[I 2025-04-04 22:35:23,233] Trial 7 finished with value: 5.3565939832363805 and parameters: {'iterations': 684, 'learning_rate': 0.00335211205636102, 'depth': 5, 'subsample': 0.6721813259005531, 'colsample_bylevel': 0.7925262682109884, 'l2_leaf_reg': 9.021856375800716, 'random_strength': 7.246060962442956}. Best is trial 6 with value: 0.9537960099286911.\n",
      "[I 2025-04-04 22:36:25,418] Trial 8 finished with value: 1.9774179653076003 and parameters: {'iterations': 961, 'learning_rate': 0.009582050204573483, 'depth': 5, 'subsample': 0.6938083478490439, 'colsample_bylevel': 0.5579477687352501, 'l2_leaf_reg': 8.046829889974966, 'random_strength': 7.768870452600733}. Best is trial 6 with value: 0.9537960099286911.\n",
      "[I 2025-04-04 22:37:29,306] Trial 9 finished with value: 7.843370785761409 and parameters: {'iterations': 858, 'learning_rate': 0.0018365491573334816, 'depth': 6, 'subsample': 0.6584682331540754, 'colsample_bylevel': 0.7151805562971464, 'l2_leaf_reg': 19.682389274719522, 'random_strength': 5.007723977131761}. Best is trial 6 with value: 0.9537960099286911.\n",
      "[I 2025-04-04 22:37:59,103] Trial 10 finished with value: 0.6157041187412208 and parameters: {'iterations': 503, 'learning_rate': 0.0866185851000775, 'depth': 4, 'subsample': 0.5371663779257849, 'colsample_bylevel': 0.5016226137544185, 'l2_leaf_reg': 5.346951929033616, 'random_strength': 5.681306576208469}. Best is trial 10 with value: 0.6157041187412208.\n",
      "[I 2025-04-04 22:38:19,785] Trial 11 finished with value: 0.7993526874188119 and parameters: {'iterations': 501, 'learning_rate': 0.09336840038378058, 'depth': 4, 'subsample': 0.5365899896196827, 'colsample_bylevel': 0.5086743214578483, 'l2_leaf_reg': 5.151488357763842, 'random_strength': 5.545686697341054}. Best is trial 10 with value: 0.6157041187412208.\n",
      "[I 2025-04-04 22:38:35,839] Trial 12 finished with value: 0.6869412263746858 and parameters: {'iterations': 642, 'learning_rate': 0.09943072696940182, 'depth': 4, 'subsample': 0.5193418767794686, 'colsample_bylevel': 0.5024094945326202, 'l2_leaf_reg': 5.009573442114559, 'random_strength': 5.053892849579723}. Best is trial 10 with value: 0.6157041187412208.\n",
      "[I 2025-04-04 22:38:50,750] Trial 13 finished with value: 0.7902640256647104 and parameters: {'iterations': 637, 'learning_rate': 0.03824103399560826, 'depth': 4, 'subsample': 0.5097512933416714, 'colsample_bylevel': 0.5013846541138423, 'l2_leaf_reg': 5.596109930006111, 'random_strength': 5.795193204336122}. Best is trial 10 with value: 0.6157041187412208.\n",
      "[I 2025-04-04 22:39:05,866] Trial 14 finished with value: 0.805776573438267 and parameters: {'iterations': 599, 'learning_rate': 0.03745636902548847, 'depth': 4, 'subsample': 0.5631339823480827, 'colsample_bylevel': 0.5490185134557223, 'l2_leaf_reg': 7.3629998448676774, 'random_strength': 5.015399481412863}. Best is trial 10 with value: 0.6157041187412208.\n",
      "[I 2025-04-04 22:39:25,874] Trial 15 finished with value: 1.136422394920065 and parameters: {'iterations': 658, 'learning_rate': 0.022836291497187758, 'depth': 5, 'subsample': 0.5896042050429467, 'colsample_bylevel': 0.5485167407192844, 'l2_leaf_reg': 7.084204902011286, 'random_strength': 5.923883718549473}. Best is trial 10 with value: 0.6157041187412208.\n",
      "[I 2025-04-04 22:39:43,793] Trial 16 finished with value: 0.6331497918625174 and parameters: {'iterations': 735, 'learning_rate': 0.06675304755633828, 'depth': 4, 'subsample': 0.5020063467313315, 'colsample_bylevel': 0.5667868618115119, 'l2_leaf_reg': 9.50524646904722, 'random_strength': 8.670935889387712}. Best is trial 10 with value: 0.6157041187412208.\n",
      "[I 2025-04-04 22:40:12,319] Trial 17 finished with value: 0.7017005758675859 and parameters: {'iterations': 769, 'learning_rate': 0.0536822725986469, 'depth': 6, 'subsample': 0.5411656422458399, 'colsample_bylevel': 0.57662612106551, 'l2_leaf_reg': 9.718420975930146, 'random_strength': 8.771541973250127}. Best is trial 10 with value: 0.6157041187412208.\n",
      "[I 2025-04-04 22:40:33,122] Trial 18 finished with value: 1.1480817205645661 and parameters: {'iterations': 896, 'learning_rate': 0.02398337862218458, 'depth': 4, 'subsample': 0.5848435291779875, 'colsample_bylevel': 0.5382837389585489, 'l2_leaf_reg': 12.669718340967453, 'random_strength': 8.66262252323245}. Best is trial 10 with value: 0.6157041187412208.\n",
      "[I 2025-04-04 22:40:56,898] Trial 19 finished with value: 0.9430443019246626 and parameters: {'iterations': 762, 'learning_rate': 0.06050270057225599, 'depth': 5, 'subsample': 0.5658425795685865, 'colsample_bylevel': 0.5830504502190621, 'l2_leaf_reg': 7.013844277642954, 'random_strength': 9.834159765934743}. Best is trial 10 with value: 0.6157041187412208.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best trial:\n",
      "  RMSE: 0.6157041187412208\n",
      "  Params: {'iterations': 503, 'learning_rate': 0.0866185851000775, 'depth': 4, 'subsample': 0.5371663779257849, 'colsample_bylevel': 0.5016226137544185, 'l2_leaf_reg': 5.346951929033616, 'random_strength': 5.681306576208469}\n"
     ]
    }
   ],
   "source": [
    "import optuna\n",
    "from catboost import CatBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Fungsi objective untuk Optuna\n",
    "def objective(trial):\n",
    "    # Definisikan parameter yang akan dioptimasi\n",
    "    params = {\n",
    "        'iterations': trial.suggest_int('iterations', 500, 1000),\n",
    "        'learning_rate': trial.suggest_float('learning_rate', 0.001, 0.1, log=True),\n",
    "        'depth': trial.suggest_int('depth', 4, 6),\n",
    "        'subsample': trial.suggest_float('subsample', 0.5, 0.8),\n",
    "        'colsample_bylevel': trial.suggest_float('colsample_bylevel', 0.5, 0.8),\n",
    "        'l2_leaf_reg': trial.suggest_float('l2_leaf_reg', 5, 20),\n",
    "        'random_strength': trial.suggest_float('random_strength', 5, 10),\n",
    "        'cat_features': cat_feature,\n",
    "        'loss_function': 'RMSE',  # Fungsi kerugian untuk regresi\n",
    "        'random_state': 42,\n",
    "        'verbose': 0\n",
    "    }\n",
    "\n",
    "    # Inisialisasi model dengan parameter yang dioptimasi\n",
    "    model = CatBoostRegressor(**params)\n",
    "\n",
    "    # Melatih model dengan validasi\n",
    "    model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)\n",
    "\n",
    "    # Prediksi nilai target\n",
    "    y_pred = model.predict(X_valid)\n",
    "\n",
    "    # Hitung RMSE\n",
    "    rmse = np.sqrt(mean_squared_error(y_valid, y_pred))\n",
    "\n",
    "    return rmse  # Mengembalikan RMSE sebagai skor yang ingin diminimalkan\n",
    "\n",
    "# Membuat studi Optuna\n",
    "study = optuna.create_study(direction=\"minimize\")  # Minimalkan RMSE\n",
    "study.optimize(objective, n_trials=20)\n",
    "\n",
    "# Menampilkan hasil terbaik\n",
    "print(\"Best trial:\")\n",
    "print(f\"  RMSE: {study.best_value}\")\n",
    "print(f\"  Params: {study.best_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 12.9088521\ttest: 21.5261995\tbest: 21.5261995 (0)\ttotal: 20ms\tremaining: 10s\n",
      "200:\tlearn: 0.5583762\ttest: 0.9760662\tbest: 0.9760662 (200)\ttotal: 4.51s\tremaining: 6.78s\n",
      "400:\tlearn: 0.3688402\ttest: 0.6742576\tbest: 0.6742576 (400)\ttotal: 9.22s\tremaining: 2.35s\n",
      "502:\tlearn: 0.3278307\ttest: 0.6157051\tbest: 0.6157041 (501)\ttotal: 11.6s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.6157041187\n",
      "bestIteration = 501\n",
      "\n",
      "Shrink model to first 502 iterations.\n",
      "Final RMSE: 0.6157041187412208\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Ambil parameter terbaik dari Optuna\n",
    "best_params = study.best_trial.params\n",
    "\n",
    "# Tambahkan parameter tetap (yang tidak dioptimasi)\n",
    "best_params.update({\n",
    "    'loss_function': 'RMSE',  # Gunakan RMSE sebagai loss function\n",
    "    'cat_features': cat_feature,\n",
    "    'random_state': 42,\n",
    "    'verbose': 200,  # Aktifkan output verbose\n",
    "    'od_type': 'Iter',\n",
    "    'od_wait': 50\n",
    "})\n",
    "\n",
    "# Latih model dengan parameter terbaik\n",
    "final_model = CatBoostRegressor(**best_params)\n",
    "final_model.fit(X_train, y_train, eval_set=(X_valid, y_valid), use_best_model=True)\n",
    "\n",
    "# Evaluasi model final\n",
    "y_pred = final_model.predict(X_valid)\n",
    "final_rmse = np.sqrt(mean_squared_error(y_valid, y_pred))  # Hitung RMSE\n",
    "print(f\"Final RMSE: {final_rmse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2 Score</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>0.103282</td>\n",
       "      <td>0.251723</td>\n",
       "      <td>0.321376</td>\n",
       "      <td>0.999455</td>\n",
       "      <td>0.010893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Validation</th>\n",
       "      <td>0.379092</td>\n",
       "      <td>0.457700</td>\n",
       "      <td>0.615704</td>\n",
       "      <td>0.986707</td>\n",
       "      <td>0.055945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MSE       MAE      RMSE  R2 Score      MAPE\n",
       "Train       0.103282  0.251723  0.321376  0.999455  0.010893\n",
       "Validation  0.379092  0.457700  0.615704  0.986707  0.055945"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Prediksi pada data training dan validasi\n",
    "y_pred_train = final_model.predict(X_train)\n",
    "y_pred_valid = final_model.predict(X_valid)\n",
    "\n",
    "# Menghitung metrik regresi untuk training\n",
    "mse_train = mean_squared_error(y_train, y_pred_train)\n",
    "mae_train = mean_absolute_error(y_train, y_pred_train)\n",
    "rmse_train = np.sqrt(mse_train)\n",
    "r2_train = r2_score(y_train, y_pred_train)\n",
    "mape_train = mean_absolute_percentage_error(y_train, y_pred_train)\n",
    "\n",
    "# Menghitung metrik regresi untuk validasi\n",
    "mse_valid = mean_squared_error(y_valid, y_pred_valid)\n",
    "mae_valid = mean_absolute_error(y_valid, y_pred_valid)\n",
    "rmse_valid = np.sqrt(mse_valid)\n",
    "r2_valid = r2_score(y_valid, y_pred_valid)\n",
    "mape_valid = mean_absolute_percentage_error(y_valid, y_pred_valid)\n",
    "\n",
    "# Membuat dataframe hasil metrik untuk training dan validation\n",
    "metrics = {\n",
    "    \"MSE\": [mse_train, mse_valid],\n",
    "    \"MAE\": [mae_train, mae_valid],\n",
    "    \"RMSE\": [rmse_train, rmse_valid],\n",
    "    \"R2 Score\": [r2_train, r2_valid],\n",
    "    \"MAPE\": [mape_train, mape_valid]\n",
    "}\n",
    "\n",
    "metrics_df = pd.DataFrame(metrics, index=[\"Train\", \"Validation\"])\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tuLm5Gb/++mu+dVitVsMwDGPx4sX53sM8jm2bp27dusawYcNs98+dO2f3Gg3DMJKSkgxvb2+7tktKSjIkGXPnzi3y9T311FOGp6encfr0aVtZVlaWERISYowYMcJWdrH3ujBz5841JBk//vijceLECePQoUPGJ598YlSvXt3w9fU1Dh8+bBjG/97/J5980u7xa9asMSQZ8+fPtyv//vvv7crztoHevXvb2tswDOPpp582JNm14YoVK+zeh/Pnzxv169c36tatayQnJ9s9z4XrGjNmjFHQ11pZxFiU2NhY484777R7fFhYmJGTk2Mr27t3r+Hm5mbccsst+baXC587JibG6NChQ77ncGwjq9VqXHHFFcaAAQPs6n366aeGJGP16tWGYRjG2bNnjZCQEOO+++6zq3f06FEjODg4X7mj1NRUw93d3Rg5cqSt7KqrrjImT55sGIZhtGnTxpgwYYJtWXh4uNG1a1fDMEq2H+jQoYMhyXj77bfzxVC3bl2jd+/ehmEYxsyZMw2LxWI899xzRcZ94XpjYmIMwzCMa665xvY6kpOTDS8vL+ODDz6wte3ixYttj+vfv7/h5eVlJCYm2sr+/vtvIzAw0Gjfvr2tLO/z1KVLF7v38ZFHHjHc3d2NM2fO2MoKe2+Lu44lS5YYkgrcp13MlVdemW9bufC5C/ozjIL3W4XtGxISEvK1Y0H8/f2L/dkqyN69ew0fHx/jrrvuyrfeC/eReb755htDkvH999/blcfHxxuSjEWLFl1yLKh86FFChZeamipJxT55P28K0EcffdSu/LHHHpOkfL/INm3aVNddd53tftu2bSWZQ5Pq1KmTr/zPP//M95wX/gqYN3QuOzvbbqYeX19f2//JyclKSUnRjTfeWODQqQ4dOqhp06YXeaXmeT4bNmwotCdj06ZNOn78uB588EG7cee9e/dW48aNC+zNGj16tN39G2+8scDXfKEff/xR2dnZevjhh+1+3bvvvvsUFBR0WeePzZ8/X9dcc41t4oe8YUQX/oJvtVq1dOlS9e3bt8Bx9xcO0bpc3t7etteYm5urU6dOKSAgQFddddUlDYMbNGiQcnJy9MUXX9jKfvjhB505c8Zuhr+LvdcX06VLF4WHh6t27dq64447FBAQoCVLluSbreyBBx6wu7948WIFBwera9eudr8+t27dWgEBAVqxYoWk/20DDz30kF17P/zwwxeNLSEhQUlJSXr44YfznbtWnPfOGTHm2bZtm7Zv3253XtfgwYN18uRJLVu2zFa2dOlSWa1WPfvss/l+8b6U7dFisej222/Xt99+q7S0NFv5okWLdMUVV9h6gpYvX64zZ87YYsr7c3d3V9u2bW1tUZjAwEA1b97c1qN08uRJ7d6929abf/3119uG2+3Zs0cnTpywPXdJ9wPe3t5255w4eumll/SPf/xD06ZN0//93/8Vt6lshgwZoi+++ELZ2dn67LPP5O7ubuvRvlBubq5++OEH9e/fXw0aNLCVR0RE2M7LyvseynP//ffbvY833nijcnNzdeDAgWLHd7F15H0Wvv7663y9cRdz6tQphYaGFrp89uzZWr58ud3fxTjuG/ImiVi2bFmBQ0JLQ0ZGhm6//Xb5+vrqX//6l92yzMxMeXt753tM3ned40y4ee1xsd4zVC0kSqjwgoKCJMlubHJRDhw4IDc3t3wzqtWqVUshISH5vsguTIak/+38a9euXWC54zkPbm5udl+uktSoUSNJshvm8vXXX6tdu3by8fFRtWrVFB4errfeess2lvtCjrMRFeall17S77//rtq1a6tNmzaaNGmSXVKT91qvuuqqfI9t3Lhxvrbw8fFReHi4XVloaGi+1+yosOfx8vJSgwYNSnTwcKEzZ87o22+/VYcOHbRv3z7b3/XXX69NmzbZhlacOHFCqampatas2SU9T0lYrVa99tpratiwoby9vRUWFqbw8HBt27atwPfyYlq0aKHGjRtr0aJFtrJFixYpLCzM7qTji73XF5N3YLRixQrt3LnTdl7dhTw8PHTllVfale3du1cpKSmqUaOGwsPD7f7S0tJsk3PkvceO0zKHh4cXecAm/W8Y4KW+f86IMc/HH38sf39/NWjQwLY9+vj4qF69enbJe2Jiotzc3Ir1g0dxDRo0SJmZmfrPf/4jSUpLS9O3336r22+/3XbAvXfvXknmDz2ObfHDDz/Y2qIoN9xwg+1cpPj4eLm7u6tdu3aSpLi4OP3222/KysrKd35SSfcDV1xxRaGz+q1atUpPPPGEnnjiiWKfl+Qo71yZ7777TvPnz1efPn0K/MHtxIkTysjIKHA/2aRJE1mtVh06dMiu3PF7I2/7udi+siTr6NChgwYMGKDJkycrLCxM/fr109y5c/Ode1MYo4hzcdq0aaMuXbrY/RWloH1D/fr19eijj+rf//63wsLC1L17d82ePfuS9oMFyc3N1R133KGdO3fqs88+U2RkpN1yX1/fAtsib0bDC3+clP7XHqX5wxkqPma9Q4UXFBSkyMhI/f777yV6XHF3hnnj+otbXtSXT2HWrFmjm2++We3bt9ebb76piIgIeXp6au7cuXYz8+Rx3MEXZuDAgbrxxhu1ZMkS/fDDD3r55Zc1bdo0ffHFF+rZs2eJ4yzsNbvK4sWLlZWVpVdeeaXA2Zvmz5+vyZMnl2kMjhNZvPjii3rmmWc0YsQIPffcc6pWrZrc3Nz08MMP5zt/rbgGDRqkF154QSdPnlRgYKD+85//aPDgwXZTwV/ue92mTZtCZ7nKc2FvWR6r1aoaNWoUOoGGY2LtCs6K0fjvNPXp6ekFJkDHjx9XWlqaAgICSuX5HLVr10716tXTp59+qiFDhuirr75SZmamXc9j3jb40UcfqVatWvnWUZzLC9xwww164403tG7dOsXHx9smLpHMRCkrK0u//vqr1q5dKw8PD1sSVVJF7ediYmJ05swZ2zktxf3x6EIRERHq2LGjXnnlFa1bt65UZ7orje+Hi60j71pP69ev11dffaVly5ZpxIgReuWVV7R+/foit7Pq1auXKGm7mIL2DZL0yiuvaPjw4fryyy/1ww8/aNy4cZo6darWr1+fL7Eqqfvuu09ff/215s+fX+BMdRERETpy5Ei+8rwyx8Qqrz0cJ4tA1UaihEqhT58+evfdd/XLL7/YDZMrSN26dWW1WrV37167E/WPHTumM2fOqG7duqUam9Vq1Z9//mnrRZJk6+nIm4Th888/l4+Pj5YtW2Y3VKCgizeWVEREhB588EE9+OCDOn78uFq1aqUXXnhBPXv2tL3W3bt35/ui2b17d6m1xYXPc2HvWnZ2tpKSki76a2Vh5s+fr2bNmuWbGEOS3nnnHS1YsECTJ09WeHi4goKCLppMF5U8h4aG5pvcIjs7O98X8WeffaZOnTppzpw5duVnzpy55C/gQYMGafLkyfr8889Vs2ZNpaamFjjxSFHvdVmJiorSjz/+qOuvv77IA9u8bWDv3r1228CJEycuesAWFRUlSfr999+L3FYKe/+cEaNk9nIcPnxYU6ZMyTcJSHJysu6//34tXbpUQ4cOVVRUlKxWq3bu3Kmrr766xK+pMAMHDtTMmTOVmpqqRYsWqV69enaJSl5b1qhR45I/dxdO6PDLL7/o+uuvty2LjIxU3bp1tW7dOq1bt04tW7a0nWBfmvuBsLAwffbZZ7rhhht00003ae3atfkOfItjyJAhuvfeexUSEqJevXoVWCc8PFx+fn7avXt3vmV//PGH3Nzc8o0wKI7S6rlo166d2rVrpxdeeEELFizQnXfeqU8++UT33ntvoY9p3LixbUbLshYbG6vY2Fj93//9n+Lj43X99dfr7bfftk2ZfyntMGHCBM2dO1czZswo9PIFV199tdasWSOr1WqXxG3YsEF+fn5238nS/2b4LOkEPqjcGHqHSuHxxx+Xv7+/7r33Xh07dizf8sTERNs0wnlfhjNmzLCr8+qrr0oyz88pbbNmzbL9bxiGZs2aJU9PT910002SzF8OLRaLXe/E/v37i3218oLk5ubmG+JQo0YNRUZG2oYjXHPNNapRo4befvttuyEK3333nXbt2lVqbdGlSxd5eXnp9ddft/tFdc6cOUpJSbmk5zl06JBWr16tgQMH6rbbbsv3d88992jfvn3asGGD3Nzc1L9/f3311VfatGlTvnXlxZR33Q/HhEgyDzBXr15tV/buu+/m61Fyd3fP96vx4sWL8023XhJNmjRRbGysFi1apEWLFikiIsJuWt/ivNdlZeDAgcrNzdVzzz2Xb9n58+dtbdmlSxd5enrqjTfesGsfx89hQVq1aqX69etrxowZ+d6bC9dV2PvnjBil/w27mzBhQr7t8b777lPDhg1tvVr9+/eXm5ubpkyZkq+n0fE1FbQ9FmbQoEHKysrSBx98oO+//z7fVNfdu3dXUFCQXnzxxQLPa3GcfrogkZGRql+/vn766Sdt2rQp32yjcXFxWrp0qXbv3m03LXhp7weuvPJK/fjjj8rMzFTXrl116tSpEj1ekm677TZNnDhRb775ZqHD/Nzd3dWtWzd9+eWXdsOljx07pgULFuiGG26wDQEviZK+t46Sk5Pz7Wvyku6Lfe6vu+46/f7772W6f0hNTdX58+ftymJjY+Xm5mb3vCVth5dfflnTp0/X008/bXfZD0e33Xabjh07Znd+58mTJ7V48WL17ds33/lLv/32m4KDgxUTE1PsWFD50aOESiEqKkoLFizQoEGD1KRJE919991q1qyZsrOzbRecy7s2Q4sWLTRs2DC9++67OnPmjDp06KCNGzfqgw8+UP/+/QudgvZS+fj46Pvvv9ewYcPUtm1bfffdd/rmm2/09NNP24b89O7dW6+++qp69OihIUOG6Pjx45o9e7aio6O1bdu2S3res2fP6sorr9Rtt92mFi1aKCAgQD/++KN+/fVX2zA1T09PTZs2Tffcc486dOigwYMH69ixY7Ypxx955JFSaYPw8HA99dRTmjx5snr06KGbb75Zu3fv1ptvvqlrr71WQ4cOLfE6FyxYIMMwdPPNNxe4vFevXvLw8ND8+fPVtm1bvfjii/rhhx/UoUMH3X///WrSpImOHDmixYsXa+3atQoJCdHVV18td3d3TZs2TSkpKfL29lbnzp1Vo0YN3XvvvRo9erQGDBigrl27auvWrVq2bFm+XqI+ffpoypQpuueeexQXF6ft27dr/vz5+c5TK6lBgwbp2WeflY+Pj0aOHGn3C2lx3uuy0qFDB40aNUpTp07Vli1b1K1bN3l6emrv3r1avHixZs6cqdtuu812za2pU6eqT58+6tWrlxISEvTdd99dtKfNzc1Nb731lvr27aurr75a99xzjyIiIvTHH39ox44dtkkSWrduLUkaN26cunfvLnd3d91xxx1OiTFvmvquXbsWekHOm2++WTNnztTx48cVHR2tf/7zn3ruued044036tZbb5W3t7d+/fVXRUZGaurUqbbX9NZbb+n5559XdHS0atSoUeQFMVu1amVbd1ZWlt2wO8kcqvzWW2/prrvuUqtWrXTHHXcoPDxcBw8e1DfffKPrr7/e7oedwtxwww226dcv7FGSzERp4cKFtnp5ymI/EB0drR9++EEdO3ZU9+7d9fPPP5coaQkODi7Wtdqef/55LV++XDfccIMefPBBeXh46J133lFWVpZeeumlEsctlfy9dfTBBx/ozTff1C233KKoqCidPXtW7733noKCggrtHcvTr18/Pffcc1q1apW6det2SfFfzM8//6yxY8fq9ttvV6NGjXT+/Hl99NFHcnd3t11aQDLb4ccff9Srr75qS8LzJkdytGTJEj3++ONq2LChmjRpoo8//thuedeuXVWzZk1JZqLUrl073XPPPdq5c6fCwsL05ptvKjc3t8Ah2cuXL1ffvn05Rwn2nD7PHlCG9uzZY9x3331GvXr1DC8vLyMwMNC4/vrrjTfeeMM4d+6crV5OTo4xefJko379+oanp6dRu3Zt46mnnrKrYxj209BeSFK+qZjzpk19+eWXbWXDhg0z/P39jcTERKNbt26Gn5+fUbNmTWPixIn5pgSeM2eO0bBhQ8Pb29to3LixMXfuXNtU2Rd77guX5U1hnZWVZUyYMMFo0aKFERgYaPj7+xstWrQw3nzzzXyPW7RokdGyZUvD29vbqFatmnHnnXfapoV2fC2OCoqxMLNmzTIaN25seHp6GjVr1jQeeOCBfNM9F3d68NjYWKNOnTpF1unYsaNRo0YN27TMBw4cMO6++24jPDzc8Pb2Nho0aGCMGTPGyMrKsj3mvffeMxo0aGC4u7vbTb+cm5trPPHEE0ZYWJjh5+dndO/e3di3b1+B04M/9thjRkREhOHr62tcf/31xi+//GJ06NDBbirg4k4Pnmfv3r22aXrXrl1rt6wk77WjvOmALzbFcGHvf553333XaN26teHr62sEBgYasbGxxuOPP278/ffftjq5ubnG5MmTbW3TsWNH4/fff8/Xho5TX+dZu3at0bVrV9trbN68ufHGG2/Ylp8/f9546KGHjPDwcMNiseTbLkszRkeff/65IcmYM2dOoXVWrlxpSDJmzpxpK3v//fdtn73Q0FCjQ4cOxvLly23Ljx49avTu3dsIDAw0JNm2ocLayDAM45///KchyYiOji40lhUrVhjdu3c3goODDR8fHyMqKsoYPny4sWnTpkIfc6G8yyFcccUV+ZZt3rzZtq0eO3Ys3/Li7AcunMbbUUH75Q0bNtim6na81EJx15unoOnB815X9+7djYCAAMPPz8/o1KmT3aUjDKPwz1NB71dh721x17F582Zj8ODBRp06dQxvb2+jRo0aRp8+fYr9HjZv3txumveinjtPYdODF7Rv+PPPP40RI0YYUVFRho+Pj1GtWjWjU6dOxo8//mhX748//jDat29v+Pr6XnQa/rzvh8L+HD8Pp0+fNkaOHGlUr17d8PPzMzp06FDga9u1a5ftMgnAhSyGwSWIgbIyfPhwffbZZ3bT9QIA4GofffSRxowZo4MHD+abdr+qefjhh7V69Wr99ttv9CjBDucoAQAAVDF33nmn6tSpo9mzZ7s6FJc6deqU/v3vf+v5558nSUI+nKMEAABQxbi5uZX4shqVUfXq1Rn1gULRowQAAAAADjhHCQAAAAAc0KMEAAAAAA5IlAAAAADAQaWfzMFqtervv/9WYGAgs5kAAAAAVZhhGDp79qwiIyPtLt5ekEqfKP3999+qXbu2q8MAAAAAUE4cOnRIV155ZZF1Kn2iFBgYKMlsjKCgIJfGkpOTox9++EHdunWTp6enS2OpKmhz56PNnYv2dj7a3Ploc+eivZ2PNnee1NRU1a5d25YjFKXSJ0p5w+2CgoLKRaLk5+enoKAgPgROQps7H23uXLS389HmzkebOxft7Xy0ufMV55QcJnMAAAAAAAckSgAAAADggEQJAAAAABxU+nOUAAAAUPYMw9D58+eVm5vr6lAqnJycHHl4eOjcuXO032Vyd3eXh4dHqVwWiEQJAAAAlyU7O1tHjhxRRkaGq0OpkAzDUK1atXTo0CGu+1kK/Pz8FBERIS8vr8taD4kSAAAALpnValVSUpLc3d0VGRkpLy8vDvZLyGq1Ki0tTQEBARe9CCoKZxiGsrOzdeLECSUlJalhw4aX1Z4kSgAAALhk2dnZslqtql27tvz8/FwdToVktVqVnZ0tHx8fEqXL5OvrK09PTx04cMDWppeKdwIAAACXjQN8lBeltS2yRQMAAACAAxIlAAAAAHBAogQAAACUQxaLRUuXLi3T5+jYsaMefvjhMn2OiopECQAAAFXaL7/8Ind3d/Xu3bvEj61Xr55mzJhR+kFdRN++fdWjR48Cl61Zs0YWi0Xbtm1zclSVC4kSAAAAygWrVdq+XVq92ry1Wp3zvHPmzNFDDz2k1atX6++//3bOk16mkSNHavny5Tp8+HC+ZXPnztU111yj5s2buyCyyoNECaioDKt0Zrt0fLV5azjp2wQAgDIQHy8NHSrdfbc0erR5O3SoWV6W0tLStGjRIj3wwAPq3bu35s2bl6/OV199pWuvvVY+Pj4KCwvTLbfcIskctnbgwAE98sgjslgstutHTZo0SVdffbXdOmbMmKF69erZ7v/666/q2rWrwsLCFBoaqt69e2vz5s3FjrtPnz4KDw/PF29aWpoWL16skSNH6tSpUxo8eLCuuOIK+fn5KTY2VgsXLixyvQUN9wsJCbF7nkOHDmngwIEKCQlRtWrV1K9fP+3fv9+2fOXKlWrTpo38/f0VEhKi66+/XgcOHCj2aysvSJSAiuhEvBQ/VPrlbmnjaPM2fqhZDgBABRMfL40fL23eLIWESPXqmbcJCWZ5WSZLn376qRo3bqyrrrpKQ4cO1fvvvy/DMGzLv/nmG91yyy3q1auXEhIS9NNPP6lNmzaSpC+++EJXXnmlpkyZoiNHjujIkSPFft6zZ89q2LBhWrt2reLj4xUVFaU+ffro7NmzxXq8h4eH7r77bs2bN88u3sWLFys3N1eDBw/WuXPn1Lp1a33zzTf6/fffdf/99+uuu+7Sxo0bix2no5ycHHXv3l2BgYFas2aN1q1bp4CAAPXo0UPZ2dk6f/68+vfvrw4dOmjbtm365ZdfdP/991fIixBzwVmgojkRLyWMl7JOS74Rko+vlJspJSeY5S2nS+Fxro4SAIBisVqlWbOk06el6Ggp73g6IECKipISE6XZs6V27aSyuFTTnDlzNHToUElSjx49lJKSolWrVqljx46SpBdeeEF33HGHJk+ebHtMixYtJEnVqlWTu7u7AgMDVatWrRI9b+fOnW3/W61WW4/TqlWr1KdPn2KtY8SIEXr55Zft4p07d64GDBig4OBgBQcHa/z48bb6Dz30kJYtW6ZPP/3UluyV1KJFi2S1WvXvf//blvzMnTtXISEhWrlypa655hqlpKSoT58+ioqKkiQ1adLkkp7L1ehRAioSwyrtmWUmSQHRkkeAZHE3b/2jpOxkac9shuEBACqMHTukXbukiIj/JUl5LBapVi1p506zXmnbvXu3Nm7cqMGDB0sye2kGDRqkOXPm2Ops2bJFN910U6k/97Fjx3TfffepYcOGCg0NVZ06dZSWlqaDBw8Wex2NGzdWXFyc3n//fUnSvn37tGbNGo0cOVKSlJubq+eee06xsbGqVq2aAgICtGzZshI9h6OtW7dq3759CgwMVEBAgAICAlStWjWdO3dOiYmJqlatmoYPH67u3burb9++mjlzZol62soTepSAiiRlh5S6y+xJKujbxLuWlLrTrBcS65oYAQAogeRkKStL8vUteLmvr3TsmFmvtM2ZM0fnz59XZGSkrcwwDHl7e2vWrFkKDg6Wb2GBFcHNzc1uOJxkDlm70LBhw3Tq1CnNnDlTtWvXtg1py87OLtFzjRw5Ug899JBmz56tuXPnKioqSh06dJAkvfzyy5o5c6ZmzJih2NhY+fv76+GHHy7yOSwWS5Gxp6WlqXXr1po/f36+x4aHh0sye5jGjRun77//XosWLdL//d//afny5WrXrl2JXpur0aMEVCTZyVJuluReyE7bw9dcnl0G3yYAAJSB0FDJ21vKzCx4eWamuTw0tHSf9/z58/rwww/1yiuvaMuWLba/rVu3KjIy0jbpQfPmzfXTTz8Vuh4vLy/l5ubalYWHh+vo0aN2CceWLVvs6qxbt07jxo1Tr169FBMTIy8vL508ebLEr2PgwIFyc3PTggUL9OGHH2rEiBG2IXHr1q1Tv379NHToULVo0UINGjTQnj17ilxfeHi4XQ/Q3r17lZGRYbvfqlUr7d27VzVq1FB0dLTdX3BwsK1ey5Yt9dRTTyk+Pl7NmjXTggULSvzaXI1ECahIvEIld2/znKSCnM80l3uV8rcJAABlJCZGatJEOnpUcujIkGGY5U2bmvVK09dff63k5GSNHDlSzZo1s/sbMGCAbfjdxIkTtXDhQk2cOFG7du3S9u3bNW3aNNt66tWrp9WrV+uvv/6yJTodO3bUiRMn9NJLLykxMVGzZ8/Wd999Z/f8DRs21EcffaRdu3Zpw4YNuv/++y+p9yogIECDBg3SU089pSNHjmj48OF2z7F8+XLFx8dr165dGjVqlI4dO1bk+jp37qxZs2YpISFBmzZt0ujRo+Xp6WlbfueddyosLEz9+vXTmjVrlJSUpJUrV2rcuHE6fPiwkpKS9NRTT+mXX37RgQMH9MMPP2jv3r0V8jwlEiWgIgmOkYKaSOcK+TbJOioFNTXrAQBQAbi5SWPHmj1GiYlSWpqUm2veJiaa5WPGlP5EDnPmzFGXLl3sekHyDBgwQJs2bdK2bdvUsWNHLV68WP/5z3909dVXq3Pnznazxk2ZMkX79+9XVFSUbehZkyZN9Oabb2r27Nlq0aKFNm7caDepQt7zJycnq1WrVho2bJhGjRqlGjVqXNJrGTlypJKTk9W9e3e7YYT/93//p1atWql79+7q2LGjatWqpf79+xe5rldeeUW1a9fWjTfeqCFDhmj8+PHy8/OzLffz89Pq1atVp04d3XrrrWrSpIlGjhypc+fOKSgoSH5+fvrjjz80YMAANWrUSPfff7/GjBmjUaNGXdJrcyWL4TgIsZJJTU1VcHCwUlJSFBQU5NJYcnJy9O2336pXr152mTnKTqVs87xZ77KTzXOSPHzNnqSso2ZPkotnvauUbV6O0d7OR5s7H23uXCVt73PnzikpKUn169eXj4/PJT9vfLw5+92uXeY5S97eZk/SmDFSXCWfzNVqtSo1NVVBQUFyK4up/aqYorbJkuQGTOYAVDThcWYytGeWObFD1jFzuF1oK6nRGKYGBwBUSHFx5hTgO3aYEzeEhprD7cgb4CokSkBFFB4nhbUzZ7fLTjZ7koJjJAvfJgCAisvNTYpl0laUEyRKQEVlcWMKcAAAgDLCz88AAAAA4IBECQAAAAAckCgBAAAAgAMSJQAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAUMaGDx+u/v372+537NhRDz/8sNPjWLlypSwWi86cOVOmz2OxWLR06dIyfY6yRqIEAACAKmn48OGyWCyyWCzy8vJSdHS0pkyZovPnz5f5c3/xxRd67rnnilXXWclNdna2wsLC9K9//avA5c8995xq1qypnJycMo2jvCBRAgAAQPlgWKUz26Xjq81bw1rmT9mjRw8dOXJEe/fu1WOPPaZJkybp5ZdfLrBudnZ2qT1vtWrVFBgYWGrrKw1eXl4aOnSo5s6dm2+ZYRiaN2+e7r77bnl6erogOucjUQIAAIDrnYiX4odKv9wtbRxt3sYPNcvLkLe3t2rVqqW6devqgQceUJcuXfSf//xH0v+Gy73wwguKjIzUVVddJUk6dOiQBg4cqJCQEFWrVk39+vXT/v37bevMzc3Vo48+qpCQEFWvXl2PP/64DMOwe17HoXdZWVl68sknVbt2bXl7eys6Olpz5szR/v371alTJ0lSaGioLBaLhg8fLkmyWq2aOnWq6tevL19fX7Vo0UKfffaZ3fN8++23atSokXx9fdWpUye7OAsycuRI7dmzR2vXrrUrX7Vqlf7880+NHDlSv/76q7p27aqwsDAFBwerQ4cO2rx5c6HrLKhHbMuWLbJYLHbxrF27VjfeeKN8fX1Vu3ZtjRs3Tunp6bblb775pho2bCgfHx/VrFlTt912W5Gv5XKRKAEAAMC1TsRLCeOl05slzxDJv555m5xglpdxsnQhX19fu56jn376Sbt379by5cv19ddfKycnR927d1dgYKDWrFmjdevWKSAgQD169LA97pVXXtG8efP0/vvva+3atTp9+rSWLFlS5PM+8MAD+uSTT/T6669r165deueddxQQEKDatWvr888/lyTt3r1bR44c0cyZMyVJU6dO1Ycffqi3335bO3bs0COPPKKhQ4dq1apVksyE7tZbb1Xfvn21ZcsW3XvvvXryySeLjCM2NlbXXnut3n//fbvyuXPnKi4uTo0bN9bZs2c1bNgwrV27VuvXr1fDhg3Vq1cvnT17tmSNfYHExET16NFDAwYM0LZt27Ro0SKtXbtWY8eOlSRt2rRJ48aN05QpU7R79259//33at++/SU/X3F4lOnaAQAAgKIYVmnPLCnrtBQQLVksZrlHgOQfJaUnSntmS2HtJEvZ/cZvGIZ++uknLVu2TA899JCt3N/fX//+97/l5eUlSfr4449ltVr173//W5b/xjp37lyFhIRo5cqV6tatm2bMmKGnnnpKt956qyTp7bff1rJlywp97j179mjJkiVatmyZunXrJklq0KCBbXm1atUkSTVq1FBISIgkswfqxRdf1I8//qjrrrvO9pi1a9fqnXfeUYcOHfTWW28pKipKr7zyiiTpqquu0vbt2zVt2rQi22LkyJEaP368Xn/9dQUEBOjs2bP67LPP9Prrr0uSOnfubFf/3XffVUhIiFatWqU+ffoUue7CTJ06VXfeeaetl61hw4Z6/fXXba/j4MGD8vf3V58+fRQYGKi6deuqZcuWl/RcxUWPEgAAAFwnZYeUukvyjfhfkpTHYpG8a0mpO816ZeDrr79WQECAfHx81LNnTw0aNEiTJk2yLY+NjbUlSZK0detW7du3T4GBgQoICFBAQICqVaumc+fOKTExUSkpKTpy5Ijatm1re4yHh4euueaaQmPYsmWL3N3d1aFDh2LHvW/fPmVkZKhr1662OAICAvThhx8qMTFRkrRr1y67OCTZkqqiDB48WLm5ufr0008lSYsWLZKbm5sGDRokSTp27Jjuu+8+NWzYUMHBwQoKClJaWpoOHjxY7Pgdbd26VfPmzbN7Ld27d5fValVSUpK6du2qunXrqkGDBrrrrrs0f/58ZWRkXPLzFQc9SgAAAHCd7GQpN0vy8S14uYevlHXMrFcGOnXqpLfeekteXl6KjIyUh4f94bG/v7/d/bS0NLVu3Vrz58/Pt67w8PBLisHXt5DXXoS0tDRJ0jfffKMrrrjCbpm3t/clxZEnKChIt912m+bOnasRI0Zo7ty5GjhwoAICAiRJw4YN06lTpzRz5kzVrVtX3t7euu666wqd7MLNzeybufA8LceZ89LS0jRq1CiNGzcu3+Pr1KkjLy8vbd68WStXrtQPP/ygZ599VpMmTdKvv/5q62UrbSRKAAAAcB2vUMndW8rNNIfbOTqfaS73Ci2Tp/f391d0dHSx67dq1UqLFi1SjRo1FBQUVGCdiIgIbdiwwXYOzfnz5/Xbb7+pVatWBdaPjY2V1WrVqlWrbEPvLpTXo5Wbm2sra9q0qby9vXXw4MFCe6KaNGlim5giz/r16y/+ImUOv+vYsaO+/vprxcfH280EuG7dOr355pvq1auXJPNcqJMnTxa6rrwE8siRIwoNNd/HLVu22NVp1aqVdu7cWeR74eHhoS5duqhLly6aOHGiQkJC9PPPP9uGOJY2ht4BAADAdYJjpKAm0rmjksPMcDIMKeuoFNTUrFcO3HnnnQoLC1O/fv20Zs0aJSUlaeXKlRo3bpwOHz4sSfrHP/6hf/3rX1q6dKn++OMPPfjgg0VeA6levXoaPHiw7r33Xi1dutS2zryhb3Xr1pXFYtHXX3+tEydOKC0tTYGBgRo/frweeeQRffDBB0pMTNTmzZv1xhtv6IMPPpAkjR49Wnv37tWECRO0e/duLViwQPPmzSvW62zfvr2io6N19913q3HjxoqLi7Mta9iwoT766CPt2rVLGzZs0J133llkr1h0dLRq166tSZMmae/evfrmm29s503leeKJJxQfH6+xY8dqy5Yt2rt3r7788kvbZA5ff/21Xn/9dW3ZskUHDhzQhx9+KKvVapuJsCyQKAEAAMB1LG5So7Fmj1F6opSTJhm55m16olneaEyZTuRQEn5+flq9erXq1KmjW2+9VU2aNNHIkSN17tw5Ww/TY489prvuukvDhg3Tddddp8DAQN1yyy1FrveVV17RgAED9OCDD6px48a67777bFNjX3HFFZo8ebKefPJJ1axZ05Y8PPfcc3rmmWc0depUNWnSRD169NA333yj+vXrSzKHrH3++edaunSpWrRoobffflsvvvhisV6nxWLRiBEjlJycrBEjRtgtmzNnjpKTk9WqVSvdddddGjdunGrUqFHoujw9PbVw4UL98ccfat68uaZNm6bnn3/erk7z5s21atUq7dmzRzfeeKNatmypZ599VpGRkZKkkJAQffHFF+rcubOaNGmit99+WwsXLlRMTNkl0BbDcVL3SiY1NVXBwcFKSUkptHvUWXJycvTtt9+qV69eVeZCXa5Gmzsfbe5ctLfz0ebOR5s7V0nb+9y5c0pKSlL9+vXl4+Nz6U98It6c/S51l3nOkru32ZPUaIwUHnfxx1dgVqtVqampCgoKsp3Pg0tX1DZZktyAc5QAAADgeuFx5hTgKTvMiRu8Qs3hduWkJwlVD4kSAAAAygeLmxQS6+ooAEmcowQAAAAA+ZAoAQAAAIADEiUAAABctko+PxgqkNLaFkmUAAAAcMnyZsbLyMhwcSSAKW9bvNxZMpnMAQAAAJfM3d1dISEhOn78uCTzOkMWi8XFUVUsVqtV2dnZOnfuHNODXwbDMJSRkaHjx48rJCRE7u7ul7U+EiUAAABcllq1akmSLVlCyRiGoczMTPn6+pJkloKQkBDbNnk5SJQAAABwWSwWiyIiIlSjRg3l5OS4OpwKJycnR6tXr1b79u25qPJl8vT0vOyepDwkSgAAACgV7u7upXaQWpW4u7vr/Pnz8vHxIVEqRxgECQAAAAAO6FFCuWK1Sjt2SMnJUmioFBMjcU5jwWgrAACAsuPSw6rVq1erb9++ioyMlMVi0dKlS/PV2bVrl26++WYFBwfL399f1157rQ4ePOj8YFHm4uOlu4ZaNeXR7XrvxdWa8uh23TXUqvh4V0dW/sTHS0OHSnffLY0ebd4OHSraCgAAoJS4tEcpPT1dLVq00IgRI3TrrbfmW56YmKgbbrhBI0eO1OTJkxUUFKQdO3bIx8fHBdGiLMXHS/Omx+uOq2apcbtd8vbMUlaOt3YfbaJ508dK4+MUF+fqKMuH+Hhp/Hjp9GkpIkLy9ZUyM6WEBLN8+nTRVgAAAJfJpYlSz5491bNnz0KX//Of/1SvXr300ksv2cqioqKcERqcyGqVls2P1wNtxyss6LSSz0XoTJavvD0y1bx2gq4IHq8vF0xXu3ZxVX5omdUqzZplJknR0VLeDKIBAVJUlJSYKM2eLbVrxzA8AACAy1Fuz1GyWq365ptv9Pjjj6t79+5KSEhQ/fr19dRTT6l///6FPi4rK0tZWVm2+6mpqZLMaRddPV1l3vO7Oo7yZucOq9pWf1vBgen6K6OJJIvkJmVYfZSREaIagUm6tto72r6ttZrGlOzov7K1+c6d0p9/SnXrSl5e+ZfXqWMmS9u3S02bOj8+qfK1eXlHezsfbe58tLlz0d7OR5s7T0na2GIYhlGGsRSbxWLRkiVLbEnQ0aNHFRERIT8/Pz3//PPq1KmTvv/+ez399NNasWKFOnToUOB6Jk2apMmTJ+crX7Bggfz8/MryJQAAAAAoxzIyMjRkyBClpKQoKCioyLrlNlH6+++/dcUVV2jw4MFasGCBrd7NN98sf39/LVy4sMD1FNSjVLt2bZ08efKijVHWcnJytHz5cnXt2pU58i+Q9Gu8LL89rOMZdeTukf/aC9bcXIX7HpTReobqX1uyk28qW5vv3CmNGiUFB0v+/vmXp6dLKSnSO++4tkepMrV5eUd7Ox9t7ny0uXPR3s5HmztPamqqwsLCipUolduhd2FhYfLw8FBTh6O9Jk2aaO3atYU+ztvbW97e3vnKPT09y82GV55iKQ+iG1fTwa2S+/lUubkF5FtuyUmTZ6BUp3E1uV1iu1WWNo+NlRo0MCduiIr63zlKkmQY0sGDUqtWZj1Xn6NUWdq8oqC9nY82dz7a3Llob+ejzcteSdq33J7u7eXlpWuvvVa7d++2K9+zZ4/q1q3roqhQFtxCY+Qf2US1go8qPd3Q+fPmQf/581J6uqGawUflH9lUbqExrg7V5dzcpLFjzesmJSZKaWlSbq55m5holo8Z4/okCQAAoKJzaY9SWlqa9u3bZ7uflJSkLVu2qFq1aqpTp44mTJigQYMGqX379rZzlL766iutXLnSdUGj9FncFB43Vt6rxsvTM1F/na6lzExf+Xplqm7EUQVWD1VQ3BjJwtG/ZE79PX26Ofvdrl3SsWOSt7fZkzRmDFODAwAAlAaXJkqbNm1Sp06dbPcfffRRSdKwYcM0b9483XLLLXr77bc1depUjRs3TldddZU+//xz3XDDDa4KGWUlPE5BHaYrcPcsVTu+S9bzx+Tm4S2fmq1kaTRGCufo/0JxceYU4Dt2SMnJZk9STAw9SQAAAKXFpYlSx44ddbG5JEaMGKERI0Y4KSK4VHicLGHt5JuyQ8pOlrxCpeAYepIK4eZmnosEAACA0lduJ3NAFWVxk0I4+gcAAIBr8VM9AAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcECiBAAAAAAOSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4IBECQAAAAAckCgBAAAAgAMSJQAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcECiBAAAAAAOSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4MDD1QEAAIDLYFillB1SdrLkFSoFx0gWfgcFgMtFogQAQEV1Il7aM0tK3SXlZknu3lJQE6nRWCk8ztXRAUCFxk9OAABURCfipYTx0unNkmeI5F/PvE1OMMtPxLs4QACo2EiUAACoaAyr2ZOUdVoKiJY8AiSLu3nrH2UOw9sz26wHALgkJEoAAFQ0KTvM4Xa+EZLFYr/MYpG8a0mpO816AIBLQqIEAEBFk53833OSfAte7uFrLs9Odm5cAFCJkCgBAFDReIWaEzfkZha8/Hymudwr1LlxAUAlQqIEAEBFExxjzm537qhkGPbLDEPKOioFNTXrAQAuCYkSAAAVjcXNnALcK1RKT5Ry0iQj17xNTzTLG43hekoAcBnYgwIAUBGFx0ktp0uhLaXzZ6T0/eZtaCuznOsoAcBl4YKzAABUVOFxUlg7c3a77GSzJyk4hp4kACgFJEoAAFRkFjcpJNbVUQBApcNPTgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOOA6SgAAAEBlYFi5AHUpIlECAAAAKroT8dKeWVLqLik3S3L3loKaSI3GSuFxro6uQiLFBAAAACqyE/FSwnjp9GbJM0Tyr2feJieY5SfiXRxgxUSiBAAAAFRUhtXsSco6LQVESx4BksXdvPWPMofh7Zlt1kOJkCgBAACgfDOs0pnt0vHV5i0H/f+TssMcbucbIVks9sssFsm7lpS606yHEuEcJQAAAJRfnHtTtOxks118fAte7uErZR0z66FE6FECAABA+cS5NxfnFWomj7mZBS8/n2ku9wp1blyVAIkSAAAAyh/OvSme4Bizh+3cUckw7JcZhpR1VApqatZDiZAoAQAAoPzh3JvisbiZwxC9QqX0RCknTTJyzdv0RLO80Riup3QJaDEAAACUP3nn3rgXce5Nbhbn3kjmuVotp0uhLaXzZ6T0/eZtaCuznHO5LgmTOQAAAMBkWKWUneb/KTul6rGu64m48Nwbj4D8yzn3xl54nBTWzuxhy0422yU4hp6ky0CiBAAAgP/NLpfyp6SnpI2jpOAGrptdLu/cm+QE85ykC4ff5Z17E9qKc28uZHGTQmJdHUWlQYoJAABQ1dnNLhdslnkGu3Z2Oc69gYuxZQEAAFRl+WaX8zfLPfxdP7sc597AhRh6BwAAUJU5zi534QzTjrPLuWJYF+fewEVIlAAAAKqyvNnlfIqYXS7rmGtnl+PcG7gAqTiA0uM4W1JVvwggAFQEF84uVxBml0MVRaIEoHSciJfih5qzJEnmbfxQ15wADAAovrzZ5c4dNWeTu1De7HJBTZldDlUOiRKAy1ceZ0sCABRPvtnl0s3ynHRml0OVxhYP4PKU59mSAADFYze7XIpZdj6F2eVQpTGZA4DLc8FsSYbForOpZvHZs1JokEUWF8+WZLVKO3ZIyclSaKgUEyO58RMRKhG28eKhnYohPE7Wau20b9N26eh+7av+jq66JlZu7q5vKN6/4qGdSpdLE6XVq1fr5Zdf1m+//aYjR45oyZIl6t+/v2358OHD9cEHH9g9pnv37vr++++dHCmAQv13tqTTmb7af0BKTZcUK23bJgX5S/Xq+qqal2tmS4qPl2bNknbtkrKyJG9vqUkTaexYKY4fR1EJsI0XD+1UPGY7uenPP5vqqaf2655/NFWDBm4ubyfev+KhnUqfS3PM9PR0tWjRQrNnzy60To8ePXTkyBHb38KFC50YIYCL8gpVWqa3kvZlKiVF8vA0iz08pZQUKWlfptIynT9bUny8NH68tHmzFBIi1atn3iYkmOXxnDaFCo5tvHhop+K5sJ2C/3uqaXCw69uJ9694aKey4dJEqWfPnnr++ed1yy23FFrH29tbtWrVsv2FhjI1JVCeWANjtDWpiUK8j8rf35DHf/upPTwkf39DIT5HtXV/U1kDnTdbktVq/qp2+rQUHS0FBEju7uZtVJQ5JGH2bLMeUBGxjRfPhe3UMNqqmCu2q3mt1Yq5Yruio6y00385bk/+/z3V1N/ftdsT23nx0E5lp9yfo7Ry5UrVqFFDoaGh6ty5s55//nlVr1690PpZWVnKysqy3U9NNU+YyMnJUU5OTpnHW5S853d1HFUJbV72du6U5sY/qH90/D/VCDys01lXSpK8PLJVzfuwUs/V1Nx1DyigW66aNs11Wkx//inVrSt5e1l1ZdAfCvA6o7TsEB1Obaw6ddyUmCht3y41beqUkMoM27jzlYc2v3Ab9/LKv7xOHVWabVy69DbPa6furTfolth3FRm4R57uWcrJ9dbfZxtpSfD92pDYttK006Vy3J48Pc12zrt11fZUlbbzy9mvVKV2Kg0laWOLYThOmO8aFosl3zlKn3zyifz8/FS/fn0lJibq6aefVkBAgH755Re5u7sXuJ5JkyZp8uTJ+coXLFggPz+/sgofAAAAQDmXkZGhIUOGKCUlRUFBQUXWLdeJkqM///xTUVFR+vHHH3XTTTcVWKegHqXatWvr5MmTF22MspaTk6Ply5era9eu8vT0dGksVQVtXvZ27pRGjTLHsgf4W1W32k516HtQq76qowOnmyot3U0pKdI77zjvl6ydO6U3Jm7QPzr+nwJ9knXmXC1l5frI2/2cQnyO6ey5EM1c+bwemty2wv+6xjbufOWhzS/83OUNk7pQerqc/rkrS5fa5jt3WPXXF/epca2tOp7ZQJLlgqWGavgmadfRFrry1nfVNKbqTg3muD15euZoyJDlWrCgq3JyPF22PVWl7fxy9itVqZ1KQ2pqqsLCwoqVKJX7oXcXatCggcLCwrRv375CEyVvb295e3vnK/f09Cw3BxHlKZaqgjYvO7GxUoMG5gmjUVHS/tPN1EEHtf90M2Vne+rgQalVK7Oes6YojW1m1T1xb8rP7aiOn42WZJFFVmVbvXQ850pV907UPde/pdhmceVi2tvSwDbufK5sc8fPneWC43/DkEs+d85Q0jaPrbNdwRHbdfR0Nbl5nc+3/ERyqGIitqlOnT1y83T+5QvKC8ftKU9OjqfL9uMFxVUVtvNL2a9UxXa6HCVp3wrVXIcPH9apU6cUERHh6lAA/Jebmzn1aGioOQY6/b8XdE9PN++Hhkpjxjh35+x2doda1N+lM+cilJ5u0fnz5pfF+fNSerpFZ87VUot6O+V2dofzggJKkePnLi1Nys01b131uSuP3M4nq0ZYlnItvkpPl8O+QMq1+KpGWJbczjv/8gXlSXncjxcUF9t5wWinsuPSJktLS9OWLVu0ZcsWSVJSUpK2bNmigwcPKi0tTRMmTND69eu1f/9+/fTTT+rXr5+io6PVvXt3V4YNwEFcnDR9utSypdm9L5m3rVqZ5U6/fkN2sgJ8s1Q/2lfBwVJOjpSRYd4GB0v1o30V4Jvlkms7AaXlws/dmTPS/v3mrcs+d+WRV6j8ArwVc1VmgfuCpldlyi/A+ZcvKI/K3X68gLjYzgtHO5UNlw6927Rpkzp16mS7/+ijj0qShg0bprfeekvbtm3TBx98oDNnzigyMlLdunXTc889V+DQOgCuFRcntWtnzqqzf785Ftpl3fxeoZK7t6oFZSq0ZYDOnjUPjDw9pcBAyZKTKZ3n4AgVX97nbscOcwrg0FApJoZfjm2CY6SgJgrJTVDLq6N0Ns3yv31BgCFL+lEpqJVZD+VrP15AXGznRaOdSl+pJEpnzpxRSEhIiR/XsWNHFTWXxLJlyy4jKgDO5uZmnii6f79567Kd838PjpScIIt/lIICHQZsZx2VQjk4QuXg5mYezKIAFjep0VgpYbws6YkK8qklBfhK5zOl9KPmjyWNxpj1IKkc7ccdsJ0XD+1Uukq8+U+bNk2LFi2y3R84cKCqV6+uK664Qlu3bi3V4ADgkuQdHHmFSumJUk6aZOSat+mJHBwBVUl4nNRyuhTaUjp/Rkrfb96GtjLLwxmTBKBgJe5RevvttzV//nxJ0vLly7V8+XJ99913+vTTTzVhwgT98MMPpR4kAJRY3sHRnllS6i4p65jk7m0eHDUaw8ERUJWEx0lh7aSUHea5iV6hZo8yP5YAKEKJE6WjR4+qdu3akqSvv/5aAwcOVLdu3VSvXj21bdu21AMEgEvGwRGAPBY3KYQxSQCKr8RHC6GhoTp06JAk6fvvv1eXLl0kSYZhKDc3t3SjA4DLlXdwVKO9eUuSBAAAiqHEPUq33nqrhgwZooYNG+rUqVPq2bOnJCkhIUHR0dGlHiAAAAAAOFuJE6XXXntN9erV06FDh/TSSy8pICBAknTkyBE9+OCDpR4gAAAAADhbiRMlT09PjR8/Pl/5I488UioBAQAAAICrXdJ1lPbu3asVK1bo+PHjslqtdsueffbZUgkMAAAAAFylxInSe++9pwceeEBhYWGqVauWLJb/XcjRYrGQKAEAAACo8EqcKD3//PN64YUX9MQTT5RFPAAAAADgciWeJzc5OVm33357WcQCAAAAAOVCiROl22+/XT/88ENZxAIAAAAA5UKxht69/vrrtv+jo6P1zDPPaP369YqNjZWnp6dd3XHjxpVuhAAAAADgZMVKlF577TW7+wEBAVq1apVWrVplV26xWEiUAAAAAFR4xUqUkpKSyjoOAAAAACg3SnyO0pQpU5SRkZGvPDMzU1OmTCmVoAAAAADAlUqcKE2ePFlpaWn5yjMyMjR58uRSCQoAAAAAXKnEiZJhGHYXmc2zdetWVatWrVSCAgAAAABXKvYFZ0NDQ2WxWGSxWNSoUSO7ZCk3N1dpaWkaPXp0mQQJAAAAAM5U7ERpxowZMgxDI0aM0OTJkxUcHGxb5uXlpXr16um6664rkyABAAAAwJmKnSgNGzZMklS/fn3FxcXlu34SAAAAAFQWxU6U8nTo0EFWq1V79uzR8ePHZbVa7Za3b9++1IIDAAAAAFcocaK0fv16DRkyRAcOHJBhGHbLLBaLcnNzSy04AAAAAHCFEidKo0eP1jXXXKNvvvlGERERBc6ABwAAAAAVWYkTpb179+qzzz5TdHR0WcQDAAAAAC5X4usotW3bVvv27SuLWAAAAACgXChxj9JDDz2kxx57TEePHlVsbGy+2e+aN29easEBAAAAgCuUOFEaMGCAJGnEiBG2MovFIsMwmMwBAAAAQKVQ4kQpKSmpLOIAAAAAgHKjxIlS3bp1yyIOAAAAACg3SpwoSVJiYqJmzJihXbt2SZKaNm2qf/zjH4qKiirV4AAAAADAFUo8692yZcvUtGlTbdy4Uc2bN1fz5s21YcMGxcTEaPny5WURIwAAAAA4VYl7lJ588kk98sgj+te//pWv/IknnlDXrl1LLTgAAAAAcIUS9yjt2rVLI0eOzFc+YsQI7dy5s1SCAgAAAABXKnGiFB4eri1btuQr37Jli2rUqFEaMQEAAACAS5V46N19992n+++/X3/++afi4uIkSevWrdO0adP06KOPlnqAAAAAAOBsJU6UnnnmGQUGBuqVV17RU089JUmKjIzUpEmTNG7cuFIPEAAAAACcrcSJksVi0SOPPKJHHnlEZ8+elSQFBgaWemAAAAAA4CqXdB2lPCRIAAAAACqjYidKnTt3Lla9n3/++ZKDAQAAAIDyoNiJ0sqVK1W3bl317t1bnp6eZRkTAAAAALhUsROladOmae7cuVq8eLHuvPNOjRgxQs2aNSvL2AAAAADAJYp9HaUJEyZo586dWrp0qc6ePavrr79ebdq00dtvv63U1NSyjBEAAAAAnKrEF5y97rrr9N577+nIkSMaM2aM3n//fUVGRpIsAQAAAKg0Spwo5dm8ebNWrVqlXbt2qVmzZpy3BAAAAKDSKFGi9Pfff+vFF19Uo0aNdNttt6latWrasGGD1q9fL19f37KKEQAAAACcqtiTOfTq1UsrVqxQt27d9PLLL6t3797y8LisyzABAAAAQLlU7Ezn+++/V0REhA4ePKjJkydr8uTJBdbbvHlzqQUHAAAAAK5Q7ERp4sSJZRkHAAAAAJQbJEoAAAAA4OCSZ70DAAAAgMqKRAkAAAAAHJAoAQAAAIADEiUAAAAAcFDiROnw4cOFLlu/fv1lBQMAAAAA5UGJE6Vu3brp9OnT+crXrVunHj16lEpQAAAAAOBKJU6U2rVrp27duuns2bO2stWrV6tXr15MIQ4AAACgUihxovTvf/9bderUUd++fZWVlaUVK1aod+/emjJlih555JGyiBEAUBUZVunMdun4avPWsLo6IgBAFVLsC87mcXNz0yeffKLevXurc+fO2rZtm6ZOnaqxY8eWRXwAgKroRLy0Z5aUukvKzZLcvaWgJlKjsVJ4nKujAwBUAcVKlLZt25avbNKkSRo8eLCGDh2q9u3b2+o0b968dCMEAFQtJ+KlhPFS1mnJN0Ly8ZVyM6XkBLO85XSSJQBAmStWonT11VfLYrHIMAxbWd79d955R++++64Mw5DFYlFubm6ZBQsAqOQMq9mTlHVaCoiWLBaz3CNA8o+S0hOlPbOlsHaShStcAADKTrESpaSkpLKOAwAAKWWHOdzON+J/SVIei0XyriWl7jTrhcS6JkYAQJVQrESpbt26kqScnByNGjVKzzzzjOrXr1+mgQEAqqDsZPOcJB/fgpd7+EpZx8x6AACUoRKNW/D09NTnn39eVrEAAKo6r1Bz4obczIKXn880l3uFOjcuAECVU+IB3v3799fSpUvLIBQAQJUXHGPObnfuqHTBebGSzPtZR6WgpmY9AADKUImnB2/YsKGmTJmidevWqXXr1vL397dbPm7cuFILDgBQxVjczCnAE8abEzd41zKH253PNJMkr1Cp0RgmcgAAlLkSJ0pz5sxRSEiIfvvtN/322292yywWC4kSAODyhMeZU4DnXUcp65g53C60lZkkMTU4AMAJSpwoMQMeAKDMhceZU4Cn7DAnbvAKNYfb0ZMEAHCSEidKF8q7rpLFcQpXAAAul8WNKcABAC5zST/Nffjhh4qNjZWvr698fX3VvHlzffTRR6UdGwAAAAC4RIl7lF599VU988wzGjt2rK6//npJ0tq1azV69GidPHlSjzzySKkHCQAAAADOVOJE6Y033tBbb72lu+++21Z28803KyYmRpMmTSJRAgAAAFDhlXjo3ZEjRxQXl3/Gobi4OB05cqRUggIAAAAAVypxohQdHa1PP/00X/miRYvUsGHDUgkKAAAAAFypxEPvJk+erEGDBmn16tW2c5TWrVunn376qcAECgAAAAAqmhL3KA0YMEAbNmxQWFiYli5dqqVLlyosLEwbN27ULbfcUhYxAgAAAIBTFbtHaeLEibrpppvUrl07tW7dWh9//HFZxgUAAAAALlPsHqUPP/xQHTt2VEhIiG666Sa98MILio+P1/nz58syPgAAAABwumInSklJSfrzzz81e/ZsXXnllXrvvfd0ww03KDQ0VD169NC0adO0cePGsowVAAAAAJyiROco1atXT/fcc48++OAD7d+/X4mJiZo5c6Zq1KihF198scBpwwEAAACgoinxZA55Dhw4oNWrV2vVqlVavXq1cnJy1L59+9KMDQAAAABcotiTORw8eFArV67UihUrtHLlSp08eVJxcXHq0KGD7rvvPrVp00ZeXl5lGSsAAAAAOEWxE6V69eqpTp06euCBB/TAAw+odevWcnd3L8vYAABVmNUq7dghJSdLoaFSTIzkdsnjIACgdLGPqvyK/XYOHDhQWVlZmjZtmp5//nnNmDFDmzdvlmEYl/zkq1evVt++fRUZGSmLxaKlS5cWWnf06NGyWCyaMWPGJT8fAKBiiI+Xhg6V7r5bGj3avB061CwHAFdjH1U1FDtR+uSTT3TkyBHFx8erZ8+e2rhxo3r16qXQ0FD16dNHL7/8sn799dcSPXl6erpatGih2bNnF1lvyZIlWr9+vSIjI0u0fgBAxRMfL40fL23eLIWESPXqmbcJCWY5ByIAXIl9VNVR4g7Cxo0b64EHHtCiRYt09OhRxcfH6+qrr9bzzz+v6667rkTr6tmzp55//nndcssthdb566+/9NBDD2n+/Pny9PQsabgAgArEapVmzZJOn5aio6WAAMnd3byNijKHuMyebdYDAGdjH1W1FPscpQsdO3ZMK1eutE3usGfPHnl7e+vGG28s1eCsVqvuuusuTZgwQTExMcV6TFZWlrKysmz3U1NTJUk5OTnKyckp1fhKKu/5XR1HVUKbOx9t7lyVrb137pT+/FOqW1cqaH6gOnWkxERp+3apaVPnxydVvjavCGhz56K9C1dW+yja3HlK0sYWo5gnGX366ae25Gj37t3y9PTUtddeq06dOqlTp06Ki4uTt7f3JQdtsVi0ZMkS9e/f31Y2depUrVixQsuWLZPFYlG9evX08MMP6+GHHy50PZMmTdLkyZPzlS9YsEB+fn6XHB8AAACAii0jI0NDhgxRSkqKgoKCiqxb7B6loUOH6pprrtEtt9yiTp066frrr5evr+9lB1uY3377TTNnztTmzZtlsViK/binnnpKjz76qO1+amqqateurW7dul20McpaTk6Oli9frq5duzKM0Eloc+ejzZ2rsrX3zp3SqFFScLDk759/eXq6lJIivfOOa3uUKlObVwS0uXPR3oUrq30Ube48eaPNiqPYiVJycrL8C9oiysiaNWt0/Phx1alTx1aWm5urxx57TDNmzND+/fsLfJy3t3eBPVuenp7lZsMrT7FUFbS589HmzlVZ2js2VmrQwDwpOipKuvB3MsOQDh6UWrUy67l6Gt7K0uYVCW3uXLR3fmW9j6LNy15J2rfYb6EzkyRJuuuuu7Rt2zZt2bLF9hcZGakJEyZo2bJlTo0FAOAcbm7S2LHmNUkSE6W0NCk317xNTDTLx4xxfZIEoGpiH1W1XNJkDqUlLS1N+/bts91PSkrSli1bVK1aNdWpU0fVq1e3q+/p6alatWrpqquucnaoAAAniYuTpk83Z5batUs6dkzy9jZ/pR0zxlwOAK7CPqrqcGmitGnTJnXq1Ml2P+/comHDhmnevHkuigoA4GpxcVK7dlz1HkD5xD6qanBpotSxY0cVc9I9SSr0vCQAQOXj5maO8weA8oh9VOV3yXnvvn37tGzZMmVmZkpSiRIeAAAAACjPSpwonTp1Sl26dFGjRo3Uq1cvHTlyRJI0cuRIPfbYY6UeIAAAAAA4W4kTpUceeUQeHh46ePCg3QVcBw0apO+//75UgwMAAAAAVyjxOUo//PCDli1bpiuvvNKuvGHDhjpw4ECpBQYAAAAArlLiHqX09HS7nqQ8p0+fLvBCrwAAAABQ0ZQ4Ubrxxhv14Ycf2u5bLBZZrVa99NJLdlN9AwAAAEBFVeKhdy+99JJuuukmbdq0SdnZ2Xr88ce1Y8cOnT59WuvWrSuLGAEAAADAqUrco9SsWTPt2bNHN9xwg/r166f09HTdeuutSkhIUFRUVFnECAAAAABOdUkXnA0ODtY///nP0o4FAAAAAMqFEvcoRUdHa9KkSdq7d29ZxAMAAAAALlfiRGnMmDH65ptvdNVVV+naa6/VzJkzdfTo0bKIDQAAAABc4pIuOPvrr7/qjz/+UK9evTR79mzVrl1b3bp1s5sNDwAAAAAqqhInSnkaNWqkyZMna8+ePVqzZo1OnDihe+65pzRjAwAAAACXuKTJHPJs3LhRCxYs0KJFi5Samqrbb7+9tOICAAAAAJcpcaK0Z88ezZ8/XwsXLlRSUpI6d+6sadOm6dZbb1VAQEBZxAgAAAAATlXiRKlx48a69tprNWbMGN1xxx2qWbNmWcQFAAAAAC5T4kRp9+7datiwYVnEAgAAAFQMhlVK2SFlJ0teoVJwjGS55NP/UQ6VOFEiSQIAAECVdiJe2jNLSt0l5WZJ7t5SUBOp0VgpPM7V0aGUFCtRqlatmvbs2aOwsDCFhobKYrEUWvf06dOlFhwAAABQrpyIlxLGS1mnJd8IycdXys2UkhPM8pbTSZYqiWIlSq+99poCAwNt/xeVKAEAAACVkmE1e5KyTksB0VLeMbFHgOQfJaUnSntmS2HtGIZXCRQrURo2bJjt/+HDh5dVLAAAAED5lbLDHG7nG/G/JCmPxSJ515JSd5r1QmJdEyNKTYlTXXd3dx0/fjxf+alTp+Tu7l4qQQEAAADlTnbyf89J8i14uYevuTw72blxoUyUOFEyDKPA8qysLHl5eV12QAAAAEC55BVqTtyQm1nw8vOZ5nKvUOfGhTJR7FnvXn/9dUmSxWLRv//9b7uLy+bm5mr16tVq3Lhx6UcIAAAAlAfBMebsdskJ5jlJFw6/Mwwp66gU2sqshwqv2InSa6+9JsnsUXr77bfthtl5eXmpXr16evvtt0s/QgAAAKA8sLiZU4AnjDcnbvCuZQ63O59pJkleoVKjMUzkUEkUO1FKSkqSJHXq1ElffPGFQkPpUgQAAEAVEx5nTgGedx2lrGPmcLvQVmaSxNTglUaJLzi7YsWKsogDAAAAqBjC48wpwFN2mBM3eIWaw+3oSapUSvxuDhgwQNOmTctX/tJLL+n2228vlaAAAACAcs3iZk4BXqO9eUuSVOmU+B1dvXq1evXqla+8Z8+eWr16dakEBQAAAACuVOJEKS0trcBpwD09PZWamloqQQEAAACAK5U4UYqNjdWiRYvylX/yySdq2rRpqQQFAAAAAK5U4skcnnnmGd16661KTExU586dJUk//fSTFi5cqMWLF5d6gAAAAADgbCVOlPr27aulS5fqxRdf1GeffSZfX181b95cP/74ozp06FAWMQIAAACAU5U4UZKk3r17q3fv3vnKf//9dzVr1uyygwIAAAAAV7rseQzPnj2rd999V23atFGLFi1KIyYAAAAAcKlLTpRWr16tu+++WxEREZo+fbo6d+6s9evXl2ZsAAAAAOASJRp6d/ToUc2bN09z5sxRamqqBg4cqKysLC1dupQZ7wAAAABUGsXuUerbt6+uuuoqbdu2TTNmzNDff/+tN954oyxjAwAAAACXKHaP0nfffadx48bpgQceUMOGDcsyJgAAAABwqWL3KK1du1Znz55V69at1bZtW82aNUsnT54sy9gAAAAAwCWKnSi1a9dO7733no4cOaJRo0bpk08+UWRkpKxWq5YvX66zZ8+WZZwAAAAA4DQlnvXO399fI0aM0Nq1a7V9+3Y99thj+te//qUaNWro5ptvLosYAQAAAMCpLus6SldddZVeeuklHT58WAsXLiytmAAAAADApS77grOS5O7urv79++s///lPaawOAAAAAFyqVBIlAAAAAKhMSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4MDD1QHAhQyrlLJDyk6WvEKl4BjJQu4MAAAAkChVVSfipT2zpNRdUm6W5O4tBTWRGo2VwuNcHR0AAADgUnQfVEUn4qWE8dLpzZJniORfz7xNTjDLT8S7OEAAAADAtUiUqhrDavYkZZ2WAqIljwDJ4m7e+keZw/D2zDbrAQAAAFUUiVJVk7LDHG7nGyFZLPbLLBbJu5aUutOsBwAAAFRRJEpVTXbyf89J8i14uYevuTw72blxAQAAAOUIiVJV4xVqTtyQm1nw8vOZ5nKvUOfGBQAAAJQjJEpVTXCMObvduaOSYdgvMwwp66gU1NSsBwAAAFRRJEpVjcXNnALcK1RKT5Ry0iQj17xNTzTLG43hekoAAACo0jgarorC46SW06XQltL5M1L6fvM2tJVZznWUAAAAUMVxwdmqKjxOCmtnzm6XnWz2JAXH0JMEAAAAiESparO4SSGxro4CAAAAKHfoPgAAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADJnMAAGczrMw4CQBAOUeiBADOdCJe2jNLSt0l5WZJ7t5SUBPzQtBcwwwAgHKDnzABwFlOxEsJ46XTmyXPEMm/nnmbnGCWn4h3cYAAACAPiRIAOINhNXuSsk5LAdGSR4BkcTdv/aPMYXh7Zpv1AACAy5EoAYAzpOwwh9v5RkgWi/0yi0XyriWl7jTrARWdYZXObJeOrzZv+QEAQAXEOUoA4AzZyeY5ST6+BS/38JWyjpn1gIqM8/AAVBL0KAGAM3iFmgeMuZkFLz+faS73CnVuXEBp4jw8AJUIiRIAOENwjPmr+rmjkmHYLzMMKeuoFNTUrAdURJyHV3IMUQTKNYbeAYAzWNzMoUcJ46X0RPOcJA9fsycp66jZk9RojOuup8S1nXC5SnIeXkisa2IsTxiiCJR7JEoA4CzhcVLL6f87OMo6Zh4chbYykyRXHRxxwIbSwHl4xZc3RDHrtJlY+viaw3Lzhii2nM5nDygHSJScxGqVdu40/9+5U4qNldxc/GOt1Srt2CElJ0uhoVJMjOtjAiq98DhZq7XTvs07lHkmWb4hoYpuFSM3dxd9+P57wGZknVa6NUJZ533l7ZEp/9MJsnDAlk953JeXGxech2d4BOjsWSknR/L0lAIDJQvn4ZkuGKJoBETrbJrlv+0UoMCAKFnSE80himHt6NVFpVCRjzddmiitXr1aL7/8sn777TcdOXJES5YsUf/+/W3LJ02apE8++USHDh2Sl5eXWrdurRdeeEFt27Z1XdCXID5emjVL+vNP6amnpFGjpAYNpLFjpTgXHX/kxbRrl5SVJXl7S02auDYmoCowP3tu2rUr1vWfvf8esGWcOa0//orW2bMWWa2Sm1uAAgOj1PiKRPlxwGZTHvfl5cp/z8PL+CtBf/wVdcH2JAUGGmp8xVH5XdGK8/D+O0TxTHaE/txi0dmzuqCdLGpQp5ZCGKKISqKiH2+69JsvPT1dLVq00OzZswtc3qhRI82aNUvbt2/X2rVrVa9ePXXr1k0nTpxwcqSXLj5eGj9e2rxZCg42y4KDpYQEszzeBRMAXRhTSIhUr55568qYgKqg3H32UnYo7e9d2rU/QikpFnl6Sn5+Zg9ASopFu/bXUtrfXNtJKp/78nLH4qbtWWOVeChUwW6JCvJNk79froJ80xTsnqjEQ6HanuXC8/DKi+xkZaRlacduX6WkyOFzJ+3c7auMtCyGKKLCK3ffeZfApXurnj176vnnn9ctt9xS4PIhQ4aoS5cuatCggWJiYvTqq68qNTVV27Ztc3Kkl8ZqNbPo06el6GjJ398s9/eXoqLMLsjZs816roopIEBydzdvXRUTUBWUx8+e9VyyUk5nKS3TV/7+koeHec69h4e5n0rL9FXK6SxZz1XtA7byuC8vj6xWaeq/4/Tid9N1KL2lgnzPqFbgfgX5ntGhtFZ68fvp+tecONrJI1THT3rL3cgs8HPnbmTq+ElvWT2q+BBFVGjl8TvvUlSYc5Sys7P17rvvKjg4WC1atCi0XlZWlrKysmz3U1NTJUk5OTnKyckp8zgvtHOnOUSjbl3Jy0vy9DSfP++2Th0pMVHavl1q2tQ1MTlyRUxlKe89d/Z7X5XR5gUrq8/e5bT3vgPBsmYGyT8gV+flk295QEC2zmQGKfVAsKKrV933szzuy8ujvHYKDr5Wb2yaqyuD/lCA1xmlZYfocGpjpbm5KSUx55LaqTLtV3YebKS/jsSqca1tOp4ZIunCGQIN1QhN1o4jLZRysJGaBrrm9Vam9q4oKlubl+fjzZK0scUwHC/o4RoWiyXfOUqS9PXXX+uOO+5QRkaGIiIitHTpUl177bWFrmfSpEmaPHlyvvIFCxbIz8+vtMMGAAAAUEFkZGRoyJAhSklJUVBQUJF1y32ilJ6eriNHjujkyZN677339PPPP2vDhg2qUaNGgespqEepdu3aOnny5EUbo7Tt3Gme7BscbHane3rmaMiQ5VqwoKtycjyVnm6OR37nHef2KF0YkyNXxFSWcnJytHz5cnXt2lWenp6uDqdKoM0LVlafvctp7507pTcmbtA/Ov6fAn3O6My5msrK9ZG3+zmF+BxT6rkQvb7yeT00uW2l2B9cqvK4Ly+PyvL7pTLtV/LaqW3UBt0S+64iA/fI0z1LObne+uvsVVq6/T5tSGzr0u2pMrV3RVHZ2rw8H2+mpqYqLCysWIlSuR965+/vr+joaEVHR6tdu3Zq2LCh5syZo6eeeqrA+t7e3vL29s5X7unp6fQNLzbWnBEpIcEcj5knJ8dT2dmeOnhQatXKudPLOsZ04TUBDUMuickZXPH+V3W0ub2y/uxdSnvHxkoZfjfoxW9e1KhOs1Q7aJeqeZsHbPtPx+idFWPkViOu0u0PSqo87svLI2d8v1SG/UpeO/2w+Qb9mRKnuiE7FOCVrLTsUB04E6N9iW7lZnuqDO1d0VSWNi/Px5slad8Kt0u3Wq12PUblmZubOf1haKg5DjM93SxPTzfvh4ZKY8Y4dwNxjCktTcrNNW9dFRNQFZTHz15eTAfS4/TgRx/rhZ8/1KwNb+uFnz/Ugx99pAPpcewPVD735eVRedzGy6ML22lfopt2/BWrbUfba8dfsdqX6EY7oVKoLPsDl4aXlpamLVu2aMuWLZKkpKQkbdmyRQcPHlR6erqefvpprV+/XgcOHNBvv/2mESNG6K+//tLtt9/uyrBLJC5Omj5datnS7GKUzNtWrcxyV8whf2FMZ85I+/ebt66MCagKyuNnLy+mq1u6KSEpVl9vbK+EpFi1bOXG/uAC5XFfXh6Vx228PKKdUBVUhu3cpUPvNm3apE6dOtnuP/roo5KkYcOG6e2339Yff/yhDz74QCdPnlT16tV17bXXas2aNYqJqVgXq4uLk9q1M2f22L/fHI/p6i71vJgq6pWSgYqqPH72ymNM5VF53JeXR2xPxUM7oSqo6Nu5SxOljh07qqi5JL744gsnRlO23NzMk9X27zdvy8MG4uZmfskDcK7y+NkrjzGVR+VxX14esT0VD+2EqqAib+fs4gEAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcECi5CyGVUrZaf6fstO8DwAAAKBcIlFyhhPxUvxQaeMo8/7GUeb9E/GujQsAAABAgUiUytqJeClhvHR6s+QZbJZ5BkvJCWY5yRIAAABQ7pAolSXDKu2ZJWWdlgKiJQ9/s9zDX/KPkrKTpT2zGYYHAAAAlDMkSmUpZYeUukvyjZAsFvtlFovkXUtK3WnWAwAAAFBukCiVpexkKTdLcvcteLmHr7k8O9m5cQEAAAAoEolSWfIKldy9pdzMgpefzzSXe4U6Ny4AAAAARSJRKkvBMVJQE+ncUckw7JcZhpR1VApqatYDAAAAUG6QKJUli5vUaKzZY5SeKOWkm+U56eZ9r1Cp0RizHgAAAFDZGFbpzHbp+GrztgJNYubh6gAqvfA4qeV0c/a7lD/NsvMpUmgrM0kKj3NtfAAAAEBZOBFvHgOn7vrvefve5mirRmMrxDEwiZIzhMdJYe2kU9uldfulNu9I1WPpSQIAAEDllHct0azT5gzQPr7meft51xJtOb3cJ0scqTuLxU0Kbmr+H9yUJAkAAACVU75riQZIFnfztgJdS5SjdQAAAAClp5JcS5RECQAAAEDpqSTXEiVRAgAAAFB6Ksm1REmUAAAAAJSeSnItURIlAAAAAKUn37VE0yQj17ytQNcSLd/RAQAAAKh48q4lGtpSOn9GSt9v3oa2qhBTg0tcRwkAAABAWci7lmjKDnPiBq9Qc7hdOe9JykOiBAAAAKBsWNykkFhXR3FJKkY6BwAAAABORKIEAAAAAA5IlAAAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQBQXIZVStlp/p+y07wPAKiUSJQAACiOE/FS/FBp4yjz/sZR5v0T8a6NCwBQJkiUAAC4mBPxUsJ46fRmyTPYLPMMlpITzHKSJQCodEiUAAAoimGV9sySsk5LAdGSh79Z7uEv+UdJ2cnSntkMwwOASoZECQCAoqTskFJ3Sb4RksViv8xikbxrSak7zXoAgEqDRAkAgKJkJ0u5WZK7b8HLPXzN5dnJzo0LAFCmSJQAACiKV6jk7i3lZha8/Hymudwr1LlxAQDKFIkSAABFCY6RgppI545KhmG/zDCkrKNSUFOzHgCg0iBRAgCgKBY3qdFYs8coPVHKSTfLc9LN+16hUqMxZj0AQKXBXh0AgIsJj5NaTpdCW0rnU8yy8ylSaCuzPDzOtfEBAEqdh6sDAACgQgiPk8LaSae2S+v2S23ekarH0pMEAJUUe3cAAIrL4iYFNzX/D25KkgQAlRh7eAAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcECiBAAAAAAOSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4IBECQAAAAAckCgBAAAAgAMSJQAAAABw4OHqAIByz7BKKTuk7GTJK1QKjpEs/MYAAABQmZEoAUU5ES/tmSWl7pJysyR3bymoidRorBQe5+roAAAAUEb4WRwozIl4KWG8dHqz5Bki+dczb5MTzPIT8S4OEAAAAGWFRAkoiGE1e5KyTksB0ZJHgGRxN2/9o8xheHtmm/UAAABQ6ZAoAQVJ2WEOt/ONkCwW+2UWi+RdS0rdadYDAABApUOiBBQkO/m/5yT5Frzcw9dcnp3s3LgAAADgFCRKQEG8Qs2JG3IzC15+PtNc7hXq3LgAAADgFCRKQEGCY8zZ7c4dlQzDfplhSFlHpaCmZj0AAABUOiRKQEEsbuYU4F6hUnqilJMmGbnmbXqiWd5oDNdTAgAAqKQ4ygMKEx4ntZwuhbaUzp+R0vebt6GtzHKuowQAAFBpccFZoCjhcVJYO3N2u+xksycpOIaeJAAAgEqORAm4GIubFBLr6igAAADgRCRKwEVYrdKOHVJyshQaKsXESG50KAEAAFRqLj3cW716tfr27avIyEhZLBYtXbrUtiwnJ0dPPPGEYmNj5e/vr8jISN199936+++/XRcwqpz4eGnoUOnuu6XRo83boUPNcgAAAFReLk2U0tPT1aJFC82ePTvfsoyMDG3evFnPPPOMNm/erC+++EK7d+/WzTff7IJIURXFx0vjx0ubN0shIVK9euZtQoJZTrIEAABQebl06F3Pnj3Vs2fPApcFBwdr+fLldmWzZs1SmzZtdPDgQdWpU8cZIaKKslqlWbOk06el6GjJYjHLAwKkqCgpMVGaPVtq145heAAAAJVRhTpHKSUlRRaLRSEhIYXWycrKUlZWlu1+amqqJHMoX05OTlmHWKS853d1HFXJpbb5zp3Sn39KdetKXl75l9epYyZL27dLTZuWRqSVB9u5c9HezkebOx9t7ly0t/PR5s5Tkja2GIZhlGEsxWaxWLRkyRL179+/wOXnzp3T9ddfr8aNG2v+/PmFrmfSpEmaPHlyvvIFCxbIz8+vtMIFAAAAUMFkZGRoyJAhSklJUVBQUJF1K0SilJOTowEDBujw4cNauXJlkS+qoB6l2rVr6+TJkxdtjLKWk5Oj5cuXq2vXrvL09HRpLFXFpbb5zp3SqFFScLDk759/eXq6lJIivfMOPUqO2M6di/Z2Ptrc+Whz56K9nY82d57U1FSFhYUVK1Eq90PvcnJyNHDgQB04cEA///zzRV+Qt7e3vL2985V7enqWmw2vPMVSVZS0zWNjpQYNzIkboqL+d46SJBmGdPCg1KqVWY9zlArGdu5ctLfz0ebOR5s7F+3tfLR52StJ+5brQ7y8JGnv3r368ccfVb16dVeHhCrCzU0aO9a8blJiopSWJuXmmreJiWb5mDEkSQAAAJWVS3uU0tLStG/fPtv9pKQkbdmyRdWqVVNERIRuu+02bd68WV9//bVyc3N19OhRSVK1atXkVdAZ9kApiouTpk83Z7/btUs6dkzy9jZ7ksaMMZcDAACgcnJporRp0yZ16tTJdv/RRx+VJA0bNkyTJk3Sf/7zH0nS1Vdfbfe4FStWqGPHjs4KE1VYXJw5BfiOHVJystmTFBNDTxIAAEBl59JEqWPHjipqLolyMs8Eqjg3N/NcJAAAAFQd/C4OAAAAAA5IlAAAAADAAYkSAAAAADggUQIAAAAAByRKAAAAAOCARAkAAAAAHJAoAQAAAIADEiUAAAAAcECiBAAAAAAOSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADgwMPVAZQ1wzAkSampqS6ORMrJyVFGRoZSU1Pl6enp6nCqBNrc+Whz56K9nY82dz7a3Llob+ejzZ0nLyfIyxGKUukTpbNnz0qSateu7eJIAAAAAJQHZ8+eVXBwcJF1LEZx0qkKzGq16u+//1ZgYKAsFotLY0lNTVXt2rV16NAhBQUFuTSWqoI2dz7a3Llob+ejzZ2PNncu2tv5aHPnMQxDZ8+eVWRkpNzcij4LqdL3KLm5uenKK690dRh2goKC+BA4GW3ufLS5c9HezkebOx9t7ly0t/PR5s5xsZ6kPEzmAAAAAAAOSJQAAAAAwAGJkhN5e3tr4sSJ8vb2dnUoVQZt7ny0uXPR3s5Hmzsfbe5ctLfz0eblU6WfzAEAAAAASooeJQAAAABwQKIEAAAAAA5IlAAAAADAAYkSAAAAADggUSpls2fPVr169eTj46O2bdtq48aNRdZfvHixGjduLB8fH8XGxurbb791UqQV39SpU3XttdcqMDBQNWrUUP/+/bV79+4iHzNv3jxZLBa7Px8fHydFXPFNmjQpX/s1bty4yMewjV+eevXq5Wtzi8WiMWPGFFifbbxkVq9erb59+yoyMlIWi0VLly61W24Yhp599llFRETI19dXXbp00d69ey+63pJ+F1QlRbV5Tk6OnnjiCcXGxsrf31+RkZG6++679ffffxe5zkvZN1UlF9vOhw8fnq/9evTocdH1sp0X7GLtXdA+3WKx6OWXXy50nWzjrkGiVIoWLVqkRx99VBMnTtTmzZvVokULde/eXcePHy+wfnx8vAYPHqyRI0cqISFB/fv3V//+/fX77787OfKKadWqVRozZozWr1+v5cuXKycnR926dVN6enqRjwsKCtKRI0dsfwcOHHBSxJVDTEyMXfutXbu20Lps45fv119/tWvv5cuXS5Juv/32Qh/DNl586enpatGihWbPnl3g8pdeekmvv/663n77bW3YsEH+/v7q3r27zp07V+g6S/pdUNUU1eYZGRnavHmznnnmGW3evFlffPGFdu/erZtvvvmi6y3Jvqmqudh2Lkk9evSwa7+FCxcWuU6288JdrL0vbOcjR47o/fffl8Vi0YABA4pcL9u4CxgoNW3atDHGjBlju5+bm2tERkYaU6dOLbD+wIEDjd69e9uVtW3b1hg1alSZxllZHT9+3JBkrFq1qtA6c+fONYKDg50XVCUzceJEo0WLFsWuzzZe+v7xj38YUVFRhtVqLXA52/ilk2QsWbLEdt9qtRq1atUyXn75ZVvZmTNnDG9vb2PhwoWFrqek3wVVmWObF2Tjxo2GJOPAgQOF1inpvqkqK6jNhw0bZvTr169E62E7L57ibOP9+vUzOnfuXGQdtnHXoEeplGRnZ+u3335Tly5dbGVubm7q0qWLfvnllwIf88svv9jVl6Tu3bsXWh9FS0lJkSRVq1atyHppaWmqW7euateurX79+mnHjh3OCK/S2Lt3ryIjI9WgQQPdeeedOnjwYKF12cZLV3Z2tj7++GONGDFCFoul0Hps46UjKSlJR48etduGg4OD1bZt20K34Uv5LkDRUlJSZLFYFBISUmS9kuybkN/KlStVo0YNXXXVVXrggQd06tSpQuuynZeeY8eO6ZtvvtHIkSMvWpdt3PlIlErJyZMnlZubq5o1a9qV16xZU0ePHi3wMUePHi1RfRTOarXq4Ycf1vXXX69mzZoVWu+qq67S+++/ry+//FIff/yxrFar4uLidPjwYSdGW3G1bdtW8+bN0/fff6+33npLSUlJuvHGG3X27NkC67ONl66lS5fqzJkzGj58eKF12MZLT952WpJt+FK+C1C4c+fO6YknntDgwYMVFBRUaL2S7ptgr0ePHvrwww/1008/adq0aVq1apV69uyp3NzcAuuznZeeDz74QIGBgbr11luLrMc27hoerg4AKA1jxozR77//ftHxutddd52uu+462/24uDg1adJE77zzjp577rmyDrPC69mzp+3/5s2bq23btqpbt64+/fTTYv0ahsszZ84c9ezZU5GRkYXWYRtHZZGTk6OBAwfKMAy99dZbRdZl33R57rjjDtv/sbGxat68uaKiorRy5UrddNNNLoys8nv//fd15513XnTSHbZx16BHqZSEhYXJ3d1dx44dsys/duyYatWqVeBjatWqVaL6KNjYsWP19ddfa8WKFbryyitL9FhPT0+1bNlS+/btK6PoKreQkBA1atSo0PZjGy89Bw4c0I8//qh77723RI9jG790edtpSbbhS/kuQH55SdKBAwe0fPnyInuTCnKxfROK1qBBA4WFhRXafmznpWPNmjXavXt3iffrEtu4s5AolRIvLy+1bt1aP/30k63MarXqp59+svt190LXXXedXX1JWr58eaH1Yc8wDI0dO1ZLlizRzz//rPr165d4Hbm5udq+fbsiIiLKIMLKLy0tTYmJiYW2H9t46Zk7d65q1Kih3r17l+hxbOOXrn79+qpVq5bdNpyamqoNGzYUug1fyncB7OUlSXv37tWPP/6o6tWrl3gdF9s3oWiHDx/WqVOnCm0/tvPSMWfOHLVu3VotWrQo8WPZxp3E1bNJVCaffPKJ4e3tbcybN8/YuXOncf/99xshISHG0aNHDcMwjLvuust48sknbfXXrVtneHh4GNOnTzd27dplTJw40fD09DS2b9/uqpdQoTzwwANGcHCwsXLlSuPIkSO2v4yMDFsdxzafPHmysWzZMiMxMdH47bffjDvuuMPw8fExduzY4YqXUOE89thjxsqVK42kpCRj3bp1RpcuXYywsDDj+PHjhmGwjZeV3Nxco06dOsYTTzyRbxnb+OU5e/askZCQYCQkJBiSjFdffdVISEiwzbD2r3/9ywgJCTG+/PJLY9u2bUa/fv2M+vXrG5mZmbZ1dO7c2XjjjTds9y/2XVDVFdXm2dnZxs0332xceeWVxpYtW+z27VlZWbZ1OLb5xfZNVV1RbX727Flj/Pjxxi+//GIkJSUZP/74o9GqVSujYcOGxrlz52zrYDsvvovtVwzDMFJSUgw/Pz/jrbfeKnAdbOPlA4lSKXvjjTeMOnXqGF5eXkabNm2M9evX25Z16NDBGDZsmF39Tz/91GjUqJHh5eVlxMTEGN98842TI664JBX4N3fuXFsdxzZ/+OGHbe9PzZo1jV69ehmbN292fvAV1KBBg4yIiAjDy8vLuOKKK4xBgwYZ+/btsy1nGy8by5YtMyQZu3fvzreMbfzyrFixosD9SF6bWq1W45lnnjFq1qxpeHt7GzfddFO+96Fu3brGxIkT7cqK+i6o6opq86SkpEL37StWrLCtw7HNL7ZvquqKavOMjAyjW7duRnh4uOHp6WnUrVvXuO+++/IlPGznxXex/YphGMY777xj+Pr6GmfOnClwHWzj5YPFMAyjTLusAAAAAKCC4RwlAAAAAHBAogQAAAAADkiUAAAAAMABiRIAAAAAOCBRAgAAAAAHJEoAAAAA4IBECQAAAAAckCgBAAAAgAMSJQBAlWCxWLR06VJXhwEAqCBIlAAA5d7w4cPVv39/V4cBAKhCSJQAAAAAwAGJEgCgQunYsaPGjRunxx9/XNWqVVOtWrU0adIkuzp79+5V+/bt5ePjo6ZNm2r58uX51nPo0CENHDhQISEhqlatmvr166f9+/dLkv744w/5+flpwYIFtvqffvqpfH19tXPnzrJ8eQCAcoJECQBQ4XzwwQfy9/fXhg0b9NJLL2nKlCm2ZMhqterWW2+Vl5eXNmzYoLfffltPPPGE3eNzcnLUvXt3BQYGas2aNVq3bp0CAgLUo0cPZWdnq3Hjxpo+fboefPBBHTx4UIcPH9bo0aM1bdo0NW3a1BUvGQDgZBbDMAxXBwEAQFGGDx+uM2fOaOnSperYsaNyc3O1Zs0a2/I2bdqoc+fO+te//qUffvhBvXv31oEDBxQZGSlJ+v7779WzZ08tWbJE/fv318cff6znn39eu3btksVikSRlZ2crJCRES5cuVbdu3SRJffr0UWpqqry8vOTu7q7vv//eVh8AULl5uDoAAABKqnnz5nb3IyIidPz4cUnSrl27VLt2bVuSJEnXXXedXf2tW7dq3759CgwMtCs/d+6cEhMTbffff/99NWrUSG5ubtqxYwdJEgBUISRKAIAKx9PT0+6+xWKR1Wot9uPT0tLUunVrzZ8/P9+y8PBw2/9bt25Venq63NzcdOTIEUVERFx60ACACoVECQBQqTRp0kSHDh2yS2zWr19vV6dVq1ZatGiRatSooaCgoALXc/r0aQ0fPlz//Oc/deTIEd15553avHmzfH19y/w1AABcj8kcAACVSpcuXdSoUSMNGzZMW7du1Zo1a/TPf/7Trs6dd96psLAw9evXT2vWrFFSUpJWrlypcePG6fDhw5Kk0aNHq3bt2vq///s/vfrqq8rNzdX48eNd8ZIAAC5AogQAqFTc3Ny0ZMkSZWZmqk2bNrr33nv1wgsv2NXx8/PT6tWrVadOHd16661q0qSJRo4cqXPnzikoKEgffvihvv32W3300Ufy8PCQv7+/Pv74Y7333nv67rvvXPTKAADOxKx3AAAAAOCAHiUAAAAAcECiBAAAAAAOSJQAAAAAwAGJEgAAAAA4IFECAAAAAAckSgAAAADggEQJAAAAAByQKAEAAACAAxIlAAAAAHBAogQAAAAADkiUAAAAAMDB/wMsgBHh8IOxqQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Membuat DataFrame untuk mempermudah visualisasi\n",
    "comparison_df = pd.DataFrame({'Actual': y_valid, 'Predicted': y_pred_valid})\n",
    "\n",
    "# Membatasi hanya pada 20 indeks pertama\n",
    "comparison_df_subset = comparison_df.iloc[:20]\n",
    "\n",
    "# Scatter plot untuk membandingkan prediksi dan nilai asli\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', alpha=0.7, color='blue')\n",
    "plt.scatter(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', alpha=0.7, color='orange')\n",
    "plt.title('Comparison of Actual vs Predicted Active Work Months (First 20)')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Active Work Months')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Line plot untuk membandingkan prediksi dan nilai asli\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Actual'], label='Actual Values', marker='o', linestyle='-', color='blue')\n",
    "plt.plot(range(len(comparison_df_subset)), comparison_df_subset['Predicted'], label='Predicted Values', marker='x', linestyle='-', color='orange')\n",
    "plt.title('Actual vs Predicted Active Work Months (First 20 - Line Plot)')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Active Work Months')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training RMSE: 0.29921646798140256\n",
      "Final Validation RMSE: 0.668770829713167\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ambil hasil evaluasi dari model\n",
    "evals_result = model.get_evals_result()\n",
    "\n",
    "# Menampilkan skor terakhir\n",
    "train_score = evals_result['learn']['RMSE'][-1]\n",
    "val_score = evals_result['validation']['RMSE'][-1]\n",
    "\n",
    "print(f\"Final Training RMSE: {train_score}\")\n",
    "print(f\"Final Validation RMSE: {val_score}\")\n",
    "\n",
    "# Import library untuk visualisasi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Ambil skor training dan validation dari evals_result\n",
    "train_rmse = evals_result['learn']['RMSE']\n",
    "val_rmse = evals_result['validation']['RMSE']\n",
    "\n",
    "# Plot learning curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_rmse, label='Training RMSE', color='blue')\n",
    "plt.plot(val_rmse, label='Validation RMSE', color='orange')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Learning Curve')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training RMSE: 0.3278307241334987\n",
      "Final Validation RMSE: 0.6157051131027085\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ambil hasil evaluasi dari model\n",
    "evals_result = final_model.get_evals_result()\n",
    "\n",
    "# Menampilkan skor terakhir\n",
    "train_score = evals_result['learn']['RMSE'][-1]\n",
    "val_score = evals_result['validation']['RMSE'][-1]\n",
    "\n",
    "print(f\"Final Training RMSE: {train_score}\")\n",
    "print(f\"Final Validation RMSE: {val_score}\")\n",
    "\n",
    "# Import library untuk visualisasi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Ambil skor training dan validation dari evals_result\n",
    "train_rmse = evals_result['learn']['RMSE']\n",
    "val_rmse = evals_result['validation']['RMSE']\n",
    "\n",
    "# Plot learning curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_rmse, label='Training RMSE', color='blue')\n",
    "plt.plot(val_rmse, label='Validation RMSE', color='orange')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Learning Curve')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CatBoost Regression model saved to 'regression_model.sav'\n",
      "CatBoost Regression model saved to 'regression_model.sav'\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "with open('D:/Tugas Akhir/Codingan/Development/App/model/regression_model.sav', 'wb') as f:\n",
    "    pickle.dump(model, f)\n",
    "print(\"CatBoost Regression model saved to 'regression_model.sav'\")\n",
    "\n",
    "with open('D:/Tugas Akhir/Codingan/Development/App/model/regression_model_final.sav', 'wb') as f:\n",
    "    pickle.dump(final_model, f)\n",
    "print(\"CatBoost Regression model saved to 'regression_model.sav'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengurutkan data berdasarkan waktu (join_date)\n",
    "df = df.sort_values('join_date')\n",
    "X = df.drop(columns=['active_work_months', 'churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "y = df['active_work_months']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 8.8750523\ttotal: 25.8ms\tremaining: 25.8s\n",
      "200:\tlearn: 1.8266624\ttotal: 6.35s\tremaining: 25.3s\n",
      "400:\tlearn: 0.6002046\ttotal: 12.6s\tremaining: 18.8s\n",
      "600:\tlearn: 0.4057507\ttotal: 19.2s\tremaining: 12.7s\n",
      "800:\tlearn: 0.3438624\ttotal: 26.1s\tremaining: 6.48s\n",
      "999:\tlearn: 0.3090144\ttotal: 32.8s\tremaining: 0us\n",
      "0:\tlearn: 10.0220337\ttotal: 31.5ms\tremaining: 31.5s\n",
      "200:\tlearn: 1.8546127\ttotal: 7.19s\tremaining: 28.6s\n",
      "400:\tlearn: 0.5945915\ttotal: 14.6s\tremaining: 21.8s\n",
      "600:\tlearn: 0.4005480\ttotal: 22.4s\tremaining: 14.9s\n",
      "800:\tlearn: 0.3411443\ttotal: 30.4s\tremaining: 7.56s\n",
      "999:\tlearn: 0.3122553\ttotal: 37.9s\tremaining: 0us\n",
      "0:\tlearn: 12.0372673\ttotal: 31.3ms\tremaining: 31.3s\n",
      "200:\tlearn: 2.0552654\ttotal: 7.95s\tremaining: 31.6s\n",
      "400:\tlearn: 0.5435496\ttotal: 15.5s\tremaining: 23.1s\n",
      "600:\tlearn: 0.3529030\ttotal: 24.1s\tremaining: 16s\n",
      "800:\tlearn: 0.3119731\ttotal: 35.3s\tremaining: 8.78s\n",
      "999:\tlearn: 0.2901350\ttotal: 44.6s\tremaining: 0us\n",
      "0:\tlearn: 13.6068194\ttotal: 33.1ms\tremaining: 33s\n",
      "200:\tlearn: 2.2563070\ttotal: 8.65s\tremaining: 34.4s\n",
      "400:\tlearn: 0.6022229\ttotal: 17.2s\tremaining: 25.7s\n",
      "600:\tlearn: 0.3770988\ttotal: 26.2s\tremaining: 17.4s\n",
      "800:\tlearn: 0.3261190\ttotal: 34.1s\tremaining: 8.47s\n",
      "999:\tlearn: 0.3030996\ttotal: 42.2s\tremaining: 0us\n",
      "0:\tlearn: 14.5501971\ttotal: 37.6ms\tremaining: 37.5s\n",
      "200:\tlearn: 2.3530705\ttotal: 8.74s\tremaining: 34.8s\n",
      "400:\tlearn: 0.5963977\ttotal: 17.7s\tremaining: 26.4s\n",
      "600:\tlearn: 0.3533729\ttotal: 28.7s\tremaining: 19s\n",
      "800:\tlearn: 0.3052574\ttotal: 41.7s\tremaining: 10.4s\n",
      "999:\tlearn: 0.2890980\ttotal: 51s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "image/png": 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rK507d+bXX381SVg+/vhj7O3tqVGjBmXLluX99983+bo7MTEx1Wul0+kAQxeKmJgYYx/NPXv2cOXKFWO/ZzC81mPHjjX2fXZ2dsbFxYWHDx8aX6PMunr1Ku7u7tjb25tsL1euXKqyDx48YPDgwbi6umJjY4OLiwulS5cGyPJ5nzx/Wud6ut5TPP0+SrlGnu6nnpbq1auzevVqwsPDOXDgAKNGjSIqKooOHToYk++LFy9iZmZGQEBAhjFD6jqytLSkTJkyqWL29PQ0SeTB8P44efJkqvdGyj8+Ke+PzFxvGVEymOJv1apVhISEsGzZMmrVqsWdO3dM/vEBQ7ev2NhYtm/fbvxJec2fPseT/9QKkdOkT7EQ+VhaUyJltD3lD03KH7+lS5fi5uaWqlxKK/OKFStStUZl9AcRoFGjRqxfv54DBw7QokULLl68SKNGjfD392f69Ol4e3tjaWnJhg0bmDFjhjEWjUbDb7/9xr59+/jjjz/YvHkzffr0Ydq0aezbtw97e3v0en2Giwak18c0q/z9/QFDv9uUVsNnsbKyeu5pwWxsbPjnn3/Ytm0bf/75J5s2bWLFihW89tprbNmyBa1WS/ny5Tl79izr169n06ZNrFq1iu+++46xY8cyYcIE4/RgT7p8+TI+Pj60bNkSR0dHli1bRteuXVm2bBlardZkntkPPviAxYsXM2TIEGrXro2joyMajYbOnTvn6HRrHTt2ZM+ePQwfPpxKlSoZX+fmzZvn2jSEz3q/ZIalpSXVq1enevXqvPTSS/Tu3ZuVK1cybty47ArTxNOJJhje1xUrVmT69Olp7pPyz3Jmrre0FCtWDI1Gk+E/C/Xr1zf2+W7VqhUVK1akW7duHD58OEvvj5RzpBxLiNwgSbEQhVDKyPESJUrQuHHjdMs1a9aMkJCQLB07ZRGI6OhoAP744w8SEhJYt26dSYtcel00atWqRa1atZg0aRLLli2jW7du/PLLL7zzzjv4+vry119/Ubdu3TSTghSlSpUCDC1nZcqUMW6/e/duplr/WrVqxeTJk/npp58ynRSnF8dff/1FVFSUSWvxmTNnTOIEMDMzo1GjRjRq1Ijp06fzxRdf8Mknn7Bt2zbja2RnZ0enTp3o1KkTiYmJtGvXjkmTJjFq1CgCAwNTvVYp//BYWVnRoUMHfvzxR27fvs3KlSt57bXXTP4h+u233+jZsyfTpk0zbouPj3+uxTJKlSrF1q1biY6ONmktPnv2rEm58PBwtm7dyoQJE0wWa0j5JuNJWWkxLFWqVKpzQdr1nhOqVasGwK1btwDD+02v13Pq1CkqVaqU5j4pMZ09e9bkmk1MTOTy5csZvk9T+Pr6cvz4cRo1avTM+srM9fY0c3NzfH19U3V1SI+9vT3jxo2jd+/e/Prrryb/hD3L5cuXMTMzM7ZyC5EbpPuEEIVQs2bNcHBw4Isvvkiz/25K1wd3d3dj94iUn2dZv349AIGBgcDjVrgnW90iIiJYvHixyX7h4eGpWuZSEoiULhQdO3ZEp9Px2WefpTpvcnKyMYFr3LgxFhYWzJkzx+SYM2fOfGb8ALVr16Z58+b873//M47Yf1JiYiIfffTRM48THByMTqfjm2++Mdk+Y8YMNBoNr7/+OkCa0+U9/dyfnKIMDC2TAQEBKIpCUlISRYsWTfVaPTnNVbdu3UhKSqJ///7cvXvXpOsEGF6np+t/zpw5xi4YWREcHExycrLJtHs6nS7VKm9pXRuQ9utkZ2cHkKkkPTg4mAMHDrB3717jtpiYGBYsWICPj0+G3RiyYtu2bWm2Jm/YsAF43BXijTfewMzMjIkTJ6Zq/U7Zv3HjxlhaWjJ79myTY37//fdERETQokWLZ8bTsWNHQkNDWbhwYarH4uLijH3KM3O9pad27docOnTombGk6NatG15eXnz11VeZ3gfg8OHDVKhQwWTMhBA5TVqKhSiEHBwcmDt3Lm+99RZVqlShc+fOuLi4cO3aNf7880/q1q2bKpFLy86dO4mPjwcMf2jXrVvHjh076Ny5s7ELQtOmTbG0tKRVq1b079+f6OhoFi5cSIkSJYwtaQBLlizhu+++o23btvj6+hIVFcXChQtxcHAgODgYMPQ77t+/P5MnT+bYsWM0bdoUCwsLzp8/z8qVK5k1axYdOnTAxcWFjz76iMmTJ9OyZUuCg4M5evQoGzduzPTXsT/++CNNmzalXbt2tGrVikaNGmFnZ8f58+f55ZdfuHXrlslcxWlp1aoVDRs25JNPPuHKlSsEBgayZcsWfv/9d4YMGWJssZ84cSL//PMPLVq0oFSpUty5c4fvvvsOLy8v42Cxpk2b4ubmZlyW+fTp03zzzTe0aNEiUwMCg4KC8PLy4vfff8fGxoZ27dqZPN6yZUuWLl2Ko6MjAQEB7N27l7/++ovixYtnqr6eft5169Zl5MiRXLlyhYCAAFavXp2qj7CDgwP169dnypQpJCUl4enpyZYtW9JsiaxatSoAn3zyCZ07d8bCwoJWrVoZk+UnjRw5kuXLl/P6668zaNAgihUrxpIlS7h8+TKrVq3KttXvPvjgA2JjY2nbti3+/v4kJiayZ88eVqxYgY+Pj7HrkZ+fH5988gmfffYZ9erVo127dlhZWXHw4EE8PDyYPHkyLi4ujBo1igkTJtC8eXNat27N2bNn+e6776hevTrdu3d/ZjxvvfUWv/76KwMGDGDbtm3UrVsXnU7HmTNn+PXXX9m8eTPVqlXL1PWWnjZt2rB06VLOnTuXqVZcCwsLBg8ezPDhw9m0aRPNmzd/5j5JSUns2LGD995775llhchWuT7fhRAiTc8zJdvXX39tUi69leYWL16c5nRn27ZtU5o1a6Y4Ojoq1tbWiq+vr9KrVy/l0KFDGcaa1pRslpaWir+/vzJp0qRUU1KtW7dOeeWVVxRra2vFx8dH+eqrr5RFixaZTLF15MgRpUuXLkrJkiUVKysrpUSJEkrLli3TjGXBggVK1apVFRsbG6VIkSJKxYoVlREjRig3b940ltHpdMqECRMUd3d3xcbGRmnQoIFy4sSJTK9opyiG6cKmTp2qVK9eXbG3t1csLS2VsmXLKh988IHJil49e/ZU7Ozs0jxGVFSUMnToUMXDw0OxsLBQypYtq3z99dcmU8Vt3bpVadOmjeLh4aFYWloqHh4eSpcuXZRz584Zy8yfP1+pX7++Urx4ccXKykrx9fVVhg8frkRERGTquSiKogwfPlwBlI4dO6Z6LDw8XOndu7fi7Oys2NvbK82aNVPOnDmTqr4yMyWboijK/fv3lbfeektxcHBQHB0dlbfeeks5evRoqinZbty4obRt21ZxcnJSHB0dlTfffFO5efOmAijjxo0zOeZnn32meHp6KmZmZibXTlqv6cWLF5UOHTooTk5OirW1tVKjRg1l/fr1JmXSe79kduW9jRs3Kn369FH8/f2N14efn5/ywQcfpLmi3aJFi5TKlSsrVlZWStGiRZWgoCAlJCTEpMw333yj+Pv7KxYWFoqrq6vy7rvvppoaMCgoKN3p+RITE5WvvvpKqVChgvE8VatWVSZMmGC8VjJzvaUnISFBcXZ2Vj777DOT7emtaKcoihIREaE4OjoqQUFBzzy+ohjqFVDOnz+fqfJCZBeNomRhJIEQQgghCrXPPvuMxYsXc/78+XQH5b2IN954A41Gw5o1a7L92EJkRJJiIYQQQmRadHQ0ZcqUYcaMGan6pr+o06dPU7FiRY4dO/bMVQKFyG6SFAshhBBCiEJPZp8QQgghhBCFniTFQgghhBCi0JOkWAghhBBCFHqSFAshhBBCiEJPFu94Tnq9nps3b1KkSJEsLT8qhBBCCCFyh6IoREVF4eHh8cyFeyQpfk43b97E29tb7TCEEEIIIcQzXL9+HS8vrwzLSFL8nFKWVb1+/ToODg45fr6kpCS2bNliXNZWPCZ1kzapl/RJ3aRN6iVtUi/pk7pJm9RL+nK7biIjI/H29jbmbRmRpPg5pXSZcHBwyLWk2NbWFgcHB3mDPUXqJm1SL+mTukmb1EvapF7SJ3WTNqmX9KlVN5np6ioD7YQQQgghRKEnSbEQQgghhCj0JCkWQgghhBCFniTFQgghhBCi0JOkWAghhBBCFHqSFAshhBBCiEJPkmIhhBBCCFHoSVIshBBCCCEKPUmKhRBCCCFEoSdJsRBCCHQ62LFDwz//eLJjhwadTu2IhBAid0lSLIQQhdzq1eDjA02amDN9ejWaNDHHx8ewXQghCgtJioUQohBbvRo6dIAbN0y3h4YatktiLIQoLCQpFkKIQkqng8GDQVFSP5aybcgQpCuFEKJQkKRYCCEKqZ07U7cQP0lR4Pp1QzkhhCjoJCkWQohC6tat7C0nhBD5mSTFQghRSLm7Z285IYTIzyQpFkKIQqpePfDyAo0m/TKWluDpmXsxCSGEWiQpFkKIQkqrhVmz0h5olyIxEapWheXLcy8uIYRQgyTFQghRiLVtCyVLpt7u7Q3z5sGrr0JUFHTtCn36QHR07scohBC5QfWk+Ntvv8XHxwdra2tq1qzJgQMH0i2blJTExIkT8fX1xdramsDAQDZt2mRSJioqiiFDhlCqVClsbGyoU6cOBw8eTHWs06dP07p1axwdHbGzs6N69epcu3Yt25+fEELkZfv2wbVrYGUFq1cnM2zYIUJCkrl8Gfr3h23bYOxYMDODxYuhWjU4dkztqIUQIvupmhSvWLGCYcOGMW7cOI4cOUJgYCDNmjXjzp07aZYfM2YM8+fPZ86cOZw6dYoBAwbQtm1bjh49aizzzjvvEBISwtKlS/nvv/9o2rQpjRs3JjQ01Fjm4sWLvPrqq/j7+7N9+3b+/fdfPv30U6ytrXP8OQshRF4yf77hd+fO0LKlQv36oQQFKWi1hu3m5jBhAvz9t6Fv8dmzULMmzJ6dcbcLIYTIb1RNiqdPn07fvn3p3bs3AQEBzJs3D1tbWxYtWpRm+aVLlzJ69GiCg4MpU6YM7777LsHBwUybNg2AuLg4Vq1axZQpU6hfvz5+fn6MHz8ePz8/5s6dazzOJ598QnBwMFOmTKFy5cr4+vrSunVrSpQokSvPWwgh8oLwcFixwnC7f/+MywYFwfHj0Lq1oZ/x4MHQpg3cu5fzcQohRG4wV+vEiYmJHD58mFGjRhm3mZmZ0bhxY/bu3ZvmPgkJCalac21sbNi1axcAycnJ6HS6DMvo9Xr+/PNPRowYQbNmzTh69CilS5dm1KhRvPHGG+nGm5CQQEJCgvF+ZGQkYOjSkZSUlPkn/pxSzpEb58pvpG7SJvWSPqkbgx9+MCM+XsvLLytUrZr8zHpxcICVK2HuXDM+/tiMP/7QEBiosGSJjqCggttsLNdL+qRu0ib1kr7crpusnEejKOp8AXbz5k08PT3Zs2cPtWvXNm4fMWIEO3bsYP/+/an26dq1K8ePH2ft2rX4+vqydetW2rRpg06nMyasderUwdLSkmXLluHq6sry5cvp2bMnfn5+nD17lrCwMNzd3bG1teXzzz+nYcOGbNq0idGjR7Nt2zaCgoLSjHf8+PFMmDAh1fZly5Zha2ubTbUihBC5Q1Fg0KCGXL/uQL9+/xIcfDlL+1++7MC0adW4caMIGo1Chw7n6Nz5LFptwU2OhRD5T2xsLF27diUiIgIHB4cMy+arpPju3bv07duXP/74A41Gg6+vL40bN2bRokXExcUBhv7Cffr04Z9//kGr1VKlShVeeuklDh8+zOnTp43n7dKlC8uWLTMeu3Xr1tjZ2bE8nXmH0mop9vb25t69e8+s5OyQlJRESEgITZo0wcLCIsfPl59I3aRN6iV9Ujewe7eGhg3NsbVVuHo1GUfHrNdLTAwMG6Zl8WJDT7w6dfQsWaKjVKmcjj53yfWSPqmbtEm9pC+36yYyMhJnZ+dMJcWqdZ9wdnZGq9Vy+/Ztk+23b9/Gzc0tzX1cXFxYu3Yt8fHx3L9/Hw8PD0aOHEmZMmWMZXx9fdmxYwcxMTFERkbi7u5Op06djGWcnZ0xNzcnICDA5Njly5c3drFIi5WVFVZWVqm2W1hY5OoFn9vny0+kbtIm9ZK+wlw3//uf4XfnzhqcnU3rILP14uQEixZB06aGPsl79phRvboZ//sftG+fA0GrrDBfL88idZM2qZf05VbdZOUcqg20s7S0pGrVqmzdutW4Ta/Xs3XrVpOW47RYW1vj6elJcnIyq1atok2bNqnK2NnZ4e7uTnh4OJs3bzaWsbS0pHr16pw9e9ak/Llz5yhV0Jo3hBAiDffvw2+/GW4PGPDix+vcGY4eNcxK8fAhdOhgOO6jL/CEECJfUK2lGGDYsGH07NmTatWqUaNGDWbOnElMTAy9e/cGoEePHnh6ejJ58mQA9u/fT2hoKJUqVSI0NJTx48ej1+sZMWKE8ZibN29GURTKlSvHhQsXGD58OP7+/sZjAgwfPpxOnTpRv359Y5/iP/74g+3bt+fq8xdCCDUsWQIJCVC5smHe4exQpgzs3AmffgpffWWY6m3XLvjlF3j55ew5hxBC5CRVk+JOnTpx9+5dxo4dS1hYGJUqVWLTpk24uroCcO3aNczMHjdmx8fHM2bMGC5duoS9vT3BwcEsXboUJycnY5mIiAhGjRrFjRs3KFasGO3bt2fSpEkmzedt27Zl3rx5TJ48mUGDBlGuXDlWrVrFq6++mmvPXQgh1KAosGCB4Xb//qDRZN+xLSzgyy+hUSPo0QNOnoTq1WHmTOjXL3vPJYQQ2U3VpBhg4MCBDBw4MM3Hnm65DQoK4tSpUxker2PHjnTs2PGZ5+3Tpw99+vTJdJxCCFEQ7NhhWIDD3t6wdHNOaNLEMKdxz56waZOhK0VICCxcCEWL5sw5hRDiRam+zLMQQojck7KCXdeuUKRIzp2nRAn480+YNs3QgrxqFVSqBLt359w5hRDiRUhSLIQQhcTdu4bkFJ69gl12MDODYcNgzx7w84Nr1wwr433+Oeh0OX9+IYTICkmKhRCikPjhB0hKMgyuq1Il985brRocOQLduxuS4U8/hcaNITQ092IQQohnkaRYCCEKAb3edIBdbitSBJYuNcx8YWcH27dDYCD88UfuxyKEEGmRpFgIIQqBbdvgwgVDctq5s3px9OhhaDWuXNkwX3Lr1jB4MMTHqxeTEEKAJMVCCFEopAyw697dMPOEml56CfbuhaFDDfdnz4batQ2zYgghhFokKRZCiALu9m1Ys8ZwW42uE2mxsoLp0w0zVDg7w7Fjhn7Oixcb5lIWQojcpvo8xSKL4uIMI2XEY8nJht9SN6akXtJXyOpm8XxzkpMtqVldR2DZBIhNp6AK9RLcAI7v0/DW25b8vUNLnz4QsjGZebMTcXDIlRCerZBdL1kidZM2qZf0pdRNHiRJcX6RmGj4vXu3LAv1tJRmJakbU1Iv6StEdaPXw8K59QFL+r96Cv7JYMoHlerFA9gyHKaULsOnP/qxfKU5+3cmsnzkcWqUi8i1ONJViK6XLJO6SZvUS/pS6iYx0TCJeR4iSXF+kTKpp6UlWFurG0teo9fDgweGjpJm0iPISOolfYWobv464MSlMFsc7ZPp1CIarDNYsUPFetECo965S4Na8XQZ/xKXwmyp+2FNJvW7xkddQtV9mQrR9ZJlUjdpk3pJX3y8ISHOg5OVS1Kc30hSnJpeb/htbS0fPk+SeklfIaqbeX94APBW8ANsnSwzLpwH6qV2tSSOLT9Dv0klWflXMT6e68PWo0VZMv4Kbs4qfe2aB+olz5K6SZvUS/pS6iYPkldKCCEKqJt3LVj3jxMA/dvdVTeYLHAqomPF5Mss+OQqNlZ6tuxzJLBrAJv35pVOxkKIgkiSYiGEKKAW/V4cnU5D3cBoXvbLXxMBazTQt+09Di09TUW/WO48sKD5B2UZPsuTxCTpoymEyH6SFAshRAGk08HCtc5A/molflpAmXj2/3CG9968A8DUpW68+nY5Lt54RlcQIYTIIkmKhRCiANq814FrYVYUdUimQ6NwtcN5ITbWCt9+fJ01Uy9Q1CGZg6fsqNwtgGWbiqodmhCiAJGkWAghCqD5q10A6NniPjbWBWM1jDcaRHB82SnqVY4iKkZLtzFl6D2hFNGx8qdMCPHi5JNECCEKmBu3LVi/yxGAfu3uqRxN9vJ2S+LvuecY3+8mZmYKP/zhTNXu5Tl6xkbt0IQQ+ZwkxUIIUcB8/7szer2G+lWiKF86fw2wywxzcxjX7xbb5p3DyzWRc9esqdXbn1nLS8gS0UKI5yZJsRBCFCDJyfC/3/P/ALvMqF8lmmM/n6JN0EMSk8wYMs2bVkN9uRsuU/ALIbJOkmIhhChANu5x5MZtS4o7JtP+tYdqh5PjijvpWDP1It+MuIaVpZ4/dzkR2KU82w7Zqx2aECKfkaRYCCEKkPmrDa3EvVrdw8qycPQl0Gjg/Y532f/DGfx94rh1z5JG777EmO88SFZpETwhRP4jSbEQQhQQ18Is2Ljn0QC7tgVrgF1mBL4Ux6GlZ3jnjbsoioZJi9wJ6leOq7dkTmMhxLNJUiyEEAXE/9YaBtg1rBbJS6US1A5HFXY2ehaOucYvX1zCwU7Hnn/tqdS1PKu2OqkdmhAij5OkWAghCoDkZMOsEwD9C9g0bM+jU9Nwji07Ra2K0TyMMqfDx770n1SS2HhZIloIkTZJioUQogBYv8uRm3ctcSmaRNuGD9UOJ08o7ZnIPwvPMqr3LTQahQVrXKjRozwnLlirHZoQIg+SpFgIIQqAeasMK9j1bnUfS4vCMcAuMyzM4Yv3bxLy7Xnciidx8pIN1XuWZ95vzjKnsRDChCTFQgiRz10OtWTLPgcA+hbCAXaZ0ahGFMeXn+L1OhHEJ5jx7pel6DCiDA8itGqHJoTIIyQpFkKIfG7hWmcURUOTmpH4eRfOAXaZUaJYMutnXmDakOtYmOtZva0olboGsOuYndqhCSHyAEmKhRAiH0tKhkXrCscKdtnBzAyGdb/D3sVn8fOO5/ptS4L6leOz/7mh06kdnRBCTZIUCyFEPvb7didu37fArXgSrYMeqh1OvlG1fCxHfjrNW8H30es1jJ3nSeP3XiL0joXaoQkhVCJJsRBC5GPzVxsG2PVpfQ8Lc5WDyWeK2On5ceIVfpxwGXtbHdsPF+GVLgGs2+GodmhCCBVIUiyEEPnUhetW/HXAAY1GkQF2L+CtFg848tNpqpaP4UGEOW0+9GPQ197EJ8icxkIUJpIUCyFEPrVwjaEvcbPakfh4JKocTf5WtmQCexadZVi32wDMWVGCWr39OXPFSuXIhBC5RZJiIYTIhxKTNCz+ozggA+yyi6WFwrShN9gw6zwuRZM4fs6Wqt3L88M6mdNYiMJAkmIhhMiH1mxz4m64BR4uibR8NULtcAqU1+tGcnz5KRrViCQ2Xku/z0szfXpVIqJlTmMhCjJJioUQIh+av9rQdeLtNvcwlwF22c7dOZkt35xn8sAbaLUKO3d6UaN7APtP2KodmhAih0hSLIQQ+cy5q1ZsO+SAmZnCO2/IALucYmYGI3vdZvuC05QoEcPlm9a8+rY/X/3gil6vdnRCiOwmSbEQQuQzCx4NsHu9TgQl3ZJUjqbgq1kxhunTt9Oh8QOSdRpGfuNF8w/KEnZPmuiFKEgkKRZCiHwkPkHDD3+krGAnrcS5xd4+mZ8nXeR/Y65gY6UnZL8DgV0D2LzXQe3QhBDZRJJiIYTIR1Zvc+J+hDlerom8XkcG2OUmjQbefuM+h386zStlY7nzwILmH5Rl+CxPEpNkTmMh8jtJioUQIh+Zt8qwgt07MsBONeVLx7P/hzMM7HgHgKlL3aj7djkuXJc5jYXIzyQpFkKIfOLUJWt2Hi2CmZnC222k64SarK0U5oy4ztqpFyjmmMyhU3ZU7laenzcWUzs0IcRzkqRYCCHyiZQBdq3qReDlKgPs8oI2DSI4vuwU9atEER2rpfunpek5zofoWPnzKkR+I+9aIYTIB+LiNSxZLyvY5UVerkn8Pfcc4/vdxMxM4cc/i1Ole3mOnLFROzQhRBZIUiyEEPnAyr+K8jDKnFLuCTStFal2OOIpWi2M63eL7fPP4eWayPlr1tTu7c/MZSVkiWgh8glJioUQIh+Yv9owwK7vG/fQymrDeVa9ytEcX3aKNxqEk5hkxtDp3rQa6svdcBkVKUReJ0mxEELkcScuWLPnX3vMtQp9ZIBdnlfMUcfqry/x7cfXsLLU8+cuJwK7lOfvg0XUDk0IkQFJioUQIo9LaSVuXf8h7s7JKkcjMkOjgffevMvBH09TvnQct+5Z0vi9snzyrQdJ8hIKkSdJUiyEEHlYbLyGpRsM03z1by8D7PKbin7xHFp6mr5t76IoGr5Y7E5Qv3JcuWmpdmhCiKdIUiyEEHnYii3FiIg2p4xnAo1rRKkdjngOttYKCz65xq9fXsTRPpm9/9pTqWt5Vv7lpHZoQognSFIshBB52PzVhrmJ+7a9i5l8YudrbzZ+yLFlp6n9SjQR0eZ0HOlLv0kliY2XJaKFyAvkI1YIIfKo4+ds2H/CMMCud6v7aocjsoGPRyI7FpxldO9baDQKC9e4UL1Hef67YK12aEIUepIUCyFEHpXSSty2YTiuxWV0VkFhYQ6T3r9JyLfncXdO5NQlG2r0LM/c35xlTmMhVCRJsRBC5EHRsWb8tDFlBTuZhq0galQjiuPLTxNcN4L4BDPe+7IU7UeU4UGETEQthBryRFL87bff4uPjg7W1NTVr1uTAgQPplk1KSmLixIn4+vpibW1NYGAgmzZtMikTFRXFkCFDKFWqFDY2NtSpU4eDBw+me8wBAwag0WiYOXNmdj0lIYR4Ib9sKUpUjBY/73gaVpMBdgWVS9Fk1s+8wIxh17Ew17NmW1EqdQ1g51F7tUMTotBRPSlesWIFw4YNY9y4cRw5coTAwECaNWvGnTt30iw/ZswY5s+fz5w5czh16hQDBgygbdu2HD161FjmnXfeISQkhKVLl/Lff//RtGlTGjduTGhoaKrjrVmzhn379uHh4ZFjz1EIIbJq3irD3MT92t6TAXYFnEYDQ7reYd8PZyhbMp7rty1p0P8lJi50R6dTOzohCg/VP2qnT59O37596d27NwEBAcybNw9bW1sWLVqUZvmlS5cyevRogoODKVOmDO+++y7BwcFMmzYNgLi4OFatWsWUKVOoX78+fn5+jB8/Hj8/P+bOnWtyrNDQUD744AN+/vlnLCwscvy5CiFEZhw+bcvh03ZYWujpJQPsCo0q/nEcXnqani3voddrGDffg9fefYkbt+XvkxC5QdXF2BMTEzl8+DCjRo0ybjMzM6Nx48bs3bs3zX0SEhKwtjYdpWtjY8OuXbsASE5ORqfTZVgGQK/X89ZbbzF8+HAqVKjwzFgTEhJISEgw3o+MjAQM3TmSkpKeuf+LSko2DLJJUhTQ63P8fPlJ0qP6SJJ6MSH1kr68XjdzVxn6ErdtGI6TYyJJuRRmXq8XteRmvVjb6Fk49jINa0Qw8Esf/jlShMAuASz49DKtgx7m+PmzSq6ZtEm9pC/p0WjSpORkyI38KQvnUDUpvnfvHjqdDldXV5Ptrq6unDlzJs19mjVrxvTp06lfvz6+vr5s3bqV1atXo3v0HVORIkWoXbs2n332GeXLl8fV1ZXly5ezd+9e/Pz8jMf56quvMDc3Z9CgQZmKdfLkyUyYMCHV9i1btmBra5vZp/zCQsLDITw8186Xn4SEhakdQp4k9ZK+vFg3sbHm/LypMgAV659mw83cbynOi/WSF+RmvTi9cpOvp11m6tRqXLzoRIfhZQkOvkSvXiextMx7iZZcM2mTeklfyM6duXKe2NjYTJdVNSl+HrNmzaJv3774+/uj0Wjw9fWld+/eJt0tli5dSp8+ffD09ESr1VKlShW6dOnC4cOHATh8+DCzZs3iyJEjaDSZmzR91KhRDBs2zHg/MjISb29vmjZtioODQ/Y+yTQkRUURsnMnTYoWxcLGJsfPl58k6fWEhIXRxM0NC+l8aST1kr68XDcLVrkQH29OOZ84hjexQqPJvfEOeble1KRavXjAWz9e4NPvvJjxsxsbNpTh+jk3fpp0kfKl43MvjgzINZM2qZf0JcXFERIeTpN69bAoUiTHz5fyzX5mqJoUOzs7o9VquX37tsn227dv4+bmluY+Li4urF27lvj4eO7fv4+HhwcjR46kTJkyxjK+vr7s2LGDmJgYIiMjcXd3p1OnTsYyO3fu5M6dO5QsWdK4j06n48MPP2TmzJlcuXIl1XmtrKywsrJKtd3CwiJ3+iObG14qC41G3mDpsDAzk7pJg9RL+vJa3SgK/G9NCQD6t72HpVad2PJaveQVatSLhRVMHxpKkxpR9Bzvw38XbKndM4DZH12nT5v7ZLJdJ8fJNZM2qZc0PLpoLczNcyV/yso5VH2lLC0tqVq1Klu3bjVu0+v1bN26ldq1a2e4r7W1NZ6eniQnJ7Nq1SratGmTqoydnR3u7u6Eh4ezefNmY5m33nqLf//9l2PHjhl/PDw8GD58OJs3b87eJymEEJl08KQtx87ZYmWpp2dLGWAnHnu9biT/Lj9F4xqRxMZreedzH7qMLk1EtCRcQmQX1btPDBs2jJ49e1KtWjVq1KjBzJkziYmJoXfv3gD06NEDT09PJk+eDMD+/fsJDQ2lUqVKhIaGMn78ePR6PSNGjDAec/PmzSiKQrly5bhw4QLDhw/H39/feMzixYtTvHhxkzgsLCxwc3OjXLlyufTMhRDC1PzVhmnY3mwUTjFHmYtLmHJzTmbzN+f5+kdXxsz1ZEVIMQ6csmP5pEvUfDnz/SaFEGlTPSnu1KkTd+/eZezYsYSFhVGpUiU2bdpkHHx37do1zJ746iE+Pp4xY8Zw6dIl7O3tCQ4OZunSpTg5ORnLREREMGrUKG7cuEGxYsVo3749kyZNkmnXhBB5VkS0Gb9sKQpA//Z3VY5G5FVmZvBxr9s0qBZFl0/KcDnUilff9uezd0MZ0eO2zGktxAtQPSkGGDhwIAMHDkzzse3bt5vcDwoK4tSpUxker2PHjnTs2DFLMaTVj1gIIXLLTxuKExuvJaBMHHUDY9QOR+RxNV+O5ejPpxjwRSl+2VKMUd94sfWAAz9OvIy7c7La4QmRL8n/lEIIoTJFgfmrnQHo3+5unhk8JfI2R3s9yyZd5vtPr2BrreOvAw4Edglg4+6cnxFJiIJIkmIhhFDZvv/s+O+CLdZWet4KfqB2OCIf0WigT5v7HP7pNK+UjeVuuAXBg8vy4QwvEpPkvyshskKSYiGEUFlKK3GnJg8o6iAD7ETW+fsksP+HM3zQ6Q4A0392pU6fcly4nnoqUSFE2iQpFkIIFYVHalkRUgyA/u3uqRyNyM+srRRmD7/O79MuUMwxmcOn7ajcrTw/bSimdmhC5AuSFAshhIqWbihGfIIZFf1iqVVRBtiJF9c6KILjy04RVCWK6Fgtb40tTc9xPkTFyJ98ITIi7xAhhFCJosC8VYa5ifu3uycD7ES28XJNYuvcc0wcEIqZmcKPfxan6lvlOXzaVu3QhMizJCkWQgiV7Dpmz+nLNtha6+geLCvYieyl1cKn74SxY8FZvF0TOX/Nmtq9yzHj5xIoitrRCZH3SFIshBAqSRlg16VZOI72epWjEQXVq5ViOLbsFG0bhpOUbMawGd60HOLHnQd5YqkCIfIMSYqFEEIF9x9q+W3roxXs2skKdiJnFXPUsWrKJeaOvIqVpZ4Nux0J7BLA1gNF1A5NiDxDkmIhhFDBkvXFSUg0o3K5WKoFxKodjigENBoY0OEeB388TUCZOMLuW9Dk/bKM/taDJFkETwhJioUQIrcpCixYkzLATlawE7mrol88B388Tb+2d1EUDZMXu1O/bzmu3LRUOzQhVCVJsRBC5LIdh+05e9Uae1sdXZvLCnYi99laK8z/5Bq/fnkRR/tk9v1nT6Wu5Vn5l5PaoQmhGkmKhRAil81fbWgl7trsAUXsZICdUM+bjR9yfPlp6rwSTUS0OR1H+tL385LExsvXF6LwkaRYCCFy0d1wc1b97QRA//YywE6or5R7IjsWnOWTPrfQaBT+t9aFam+V59/zNmqHJkSukqRYCCFy0Q9/FCcp2YxqATFU8Y9TOxwhADA3h8/fu8lf353H3TmR05dtqNHTn29/dZE5jUWhIUmxEELkEr0eFqwxzE0s07CJvOi16lH8+8spWtZ7SEKiGQOnlKTd8DI8iNCqHZoQOU6SYiGEyCXbDhXhwnVritjp6Nw0XO1whEiTs5OOddMvMvPD61ha6Fm7vSiBXQPYedRe7dCEyFGSFAshRC5JWcGu++v3sbeVAXYi79JoYHCXO+xdfIayJeO5cduSBv1fYsICd3Q6QxmdDnYcLsI//3iy43AR43Yh8itZ41EIIXLB7fvmrNmWsoLdPZWjESJzqvjHceSn0wyc4s2S9c6MX+DB34eK0K35fT77nwc37hjmNp4OeJVIZNZH12n32kNVYxbieUlLsRBC5ILFfxQnWaeh5svRBL4kA+xE/mFvq+eH8Vf56bPLFLHT8c+RIvT/ohQ37liYlAu9Y0GHEWVY/Wh2FSHyG0mKhRAihxkG2KWsYCetxCJ/6vb6Aw79eAoLcz2gefTzmPLo/pBp3tKVQuRLkhQLIUQOC9nvwOVQKxztk+nUVFawE/nXzbuWJCWnnzooaLh+21IG5Yl8SfoU5wM6HezYqeWffzyxK2VJw1pJaGV2HCHyjZQBdj1aPMDWWiZ9FfnXrXsWzy4EdPmkNI1rRFHz5RhqVIgh8KU4rCzl2hd5myTFedzq1TB4MNy4YQdUk8EMQuQzN+9asO4fJ0DmJhb5n7tzUqbKhd235KeNxflpY3EALC30VC4XS40KsdR8OYaaL8fg65WARlaTFnmIJMV52OrV0KEDqVYTShnM8NuUS5IYC5HHLfq9ODqdhrqB0VTwjVc7HCFeSL3K0XiVSCT0joWxD/GTNCh4uCQxb/RVDp2y48BJO/aftONBhDn7T9iz/4Q9c1YYyhZzTKZGhRhqVjC0Jtd4OQZnJ+mMLNQjSXEepdMZWojTWl5TQYMGhSHTvGkT9FC6UgiRR+l0sHCtrGAnCg6tFmZ9dJ0OI8qgQTFJjDUY/mDNHn6dlvUiaVkvEjD8HbsUasn+E3bsP2FIlI+cseVBhDmb9jiyaY+j8Ri+XvEmrcmVXorF2kq6XYjcIUlxHrVzJ9y4kf7jTw5maFAtOvcCE0Jk2ua9DlwLs6KoQzIdGskKdqJgaPfaQ36bconBU72N8xQDeLkmMfPD1F37NBrw9UrE1yuRrs0N74PEJA3Hz9kYWpIfJcvnrllz8YbhZ/nmYgBYmOsJfCnO2KJc8+UYypZMwEymCRA5QJLiPOrWrUyWy+SgByFE7pu/2jANW88W97GRAXaiAGn32kPaBD1k2xE7Np6P5vWy9jSsEpPpby4tLRSqV4ileoVY3u9o+BYlPFLLwVO2xtbk/SfsuBtuwaFTdhw6Zcd3Kw37OhVJpnpArHEQX82XYyhRLDmHnqkoTCQpzqPc3TNZLpODHoQQuevGbQvW7zJ8LdxP5iYWBZBWC0FVo4hxv0mQhwfaF2y+Leqgo2mtKJrWigIM3S6u3jLtdnH4jC0Po8wJ2e9AyH4H474+HgkmrcmV/WNlpheRZZIU51H16oGXF4SGpt2vWIOCl2sS9SpL1wkh8qLvf3dGr9dQv0oU5UvLADshskqjAR+PRHw8EunU1NDtIikZ/rtgw4EThgF8+0/YceaKNVduWnHlphW/hhi6XWi1Cq/4xZm0Jvv7xEu3C5EhSYrzKK0WZs0yzD6h0TydGBvuzPzwugyyEyIPSk6G//0uA+yEyG4W5lDFP44q/nEM6GD4BiYi2oxDp+xMul2E3bfg6Flbjp61Zd4qQzemInY6qgfEmCTK7s7S7UI8JklxHtauHfz2W8o8xU8+oqFcqTjaNnyoUmRCiIxs3OPIjduWFHdMpr1MmyhEjnK019OoRhSNajzudnH9toWxNfnASTsOnbIlKkbL3wcd+Pvg424X3q6JxgS55ssxVC0fi52NXq2nIlQmSXEe164dtGkD2zbFsPHv01RzsOXtyeU4c9WG5ZuLGkfyCiHyjpQV7Hq1uiereAmRyzQaKOmWREm3h3Ro/BAwfHtz8pLpbBcnL1lz/bYl129bsurvogCYmSm87Gva7SKgdLx8K1tISFKcD2i1EFRPR0xyKMHFinHpdhhj5noyYrYXbYIi5L9aIfKQa2EWbHw072q/tjLAToi8wNwcAl+KI/ClOPo+el9GxZhx+PQTs12ctCP0jiX/nrfl3/O2LFxj6HZhZ6OjWnnT2S68XGWQe0EkSXE+9GH323y/zpnLoVZMXuzG5+/dVDskIcQj/1trGGDXsFokL5VKUDscIUQ6itjpaVAt2mSu/9A7FsbW5AMnbTl4yo7oWC07jhRhx5EixnIeLqbdLqqVj6WInTRQ5XeSFOdD1lYK04dep+1Hfkz9yZU+re9RxitR7bCEKPSSkg1JMUB/mYZNiHzHs0QSbUs8NI7Z0eng9GXrx90uTtrx3wUbbt61ZO12S9ZuN3S70GgUAkrHG1uTq1SIQmeTehlskbdJUpxPtQmKoHGNSP464MCHM71YM/WS2iEJUeit3+nErXuWuBRNkoGwQhQAWi287BfPy37x9GlzH4CYODOOnLE1tibvP2HHtTArTl6y4eQlGxatM/xjbGnpT/XycdSqmDLjRSwl3RLRSK6cZ0lSnE9pNIb151/pEsDa7UX5a38RGteMUjssIQq1lAF2vVvdx9JCBtgJURDZ2eipVznaZJ2AsHvmJq3JB0/aEhljzu7jRdh9/HG3C9fiSdQIeKLbRUAsTkV0ajwNkQZJivOxgDLxvP/mHWb/4srgad4cW3YKC3lFhVDF5VBLtuwzTPXUT+YmFqJQcXNOpnVQBK2DIgBISNaz8GAkNndLc/iUPftP2PHveVtu37fgj51O/LHTybivv0+csSW55ssxvFI2Vv6Wq0SqPZ8b3+8WP28szqlLNsz9zYVBneWPsRBqWLjWGUXR0KRmJL7Sx1+IQs3MDLy9owmueZ+3WxumTo2L13D0rOlsF5dDrThzxYYzV2xYst6wr7WVnsrlYo1LVteoEENpT+l2kRskKc7nijro+OL9UPp/UYqx8zzo0iwcl6KyQo8QuSkpGWM/QlnBTgiRFhtrhTqBMdQJjDFuu/PAnIOnbI1zJx84acfDKHP2/mvP3n/tjeVciiZRo8Lj1uTqATEUc5RuF9lNkuIC4O0295i3yoWjZ20Z850H8z+5pnZIQhQqv2934vZ9C9yKJ9E66KHa4Qgh8okSxZJp8WokLV6NBAyr8Z2/ZmWyZPWxczbcDbfgz11O/LnLybhv2ZLx1KzweO7kwJfiZLGgFyRJcQGg1RoG3dXvW46Fa50Z0P4ulf3j1A5LiEJj/mrDJP99Wt+TvoBCiOem0cBLpRJ4qVQCb7V4AEBCooZj52xMEuUL1605f83w89PG4gBYWuip9FKccRBfjQox+HknSLeLLJCP7wKiXuVoOjd9wC9bijFoqjf/LDwnbwQhcsGF61b8dcABjUYxrpQlhBDZxcpSoebLsdR8ORYwdM+6/1DLwVN2Jt0u7kcYZsA4cNKOOSsM+xZzTH7U7SLG0Kr8cgzOTtLtIj2SFBcgUwbd4PcdTuw6VoQVW4rSuVm42iEJUeAtXGPoS9ysdiQ+HjLATgiR84o76WheJ5LmdR53u7gUamnSmnz0rC0PIszZtMeRTY+Wngco45lgsmR15XKxWFtJtwuQpLhA8XZLYnTvW3w6z5OPZnnRqn4Edjay7KQQOSUxScPiPwxfXcoAOyGEWjQa8PVKxNcrka7NDQ1iiUka/j1vY9KafPaqNZdCrbgUasXyzcUAMNcqBL5kGMCX0pr8UskEzMzUfEbqkKS4gPmw+22+X+fMlZtWfPmDG5+9e1PtkIQosNZsc+JuuAUeLom0fDVC7XCEEMLI0kKhWkAs1QJieb+j4Z/28Egth1Jmu3jUonw33ILDp+04fNqO71Ya9nW0T6ZGhVhja3LNl2MoUazgz2wlSXEBY2OtMG3IDdqP8OXrpa70aX2P0p7yla4QOSFlBbu329zDXD5NhRB5XFEHHU1qRdGklmEFXEWBq7dMu10cPmNLRLQ5IfsdCNnvYNy3lLtpt4sq/rHYWmet24VOBzuOOvLPVRvszLU0bG6YLCCvkI/xAqhtw4e8Vj2Svw868NFML1Z9fUntkIQocM5dtWLbIQfMzBTeeUMG2Akh8h+NBnw8EvHxSKRTU0O3i6RkOHHBxtiafOCkHacvW3P1lhVXb1nxa4ih24VWq1DR13S2i/Kl49PtdrH6bycGT/Xmxh1LAKZPBy8vmDUL2rXLlaf7TJIUF0AaDcz68DqVugWweltRth4oQqMaUWqHJUSBsuDRALvX60RQ0i1J5WiEECJ7WJhDZf84KvvHMaCD4R/+iGgzDp9+PNvF/hN2hN234Ng5W46dszVOS1nETkf1gBiTbhfuzsms/tuJDiPK8HS7cmgodOgAv/2WNxJjSYoLqJf94nmvw13mrCjB4KneHFt2Sr7eFSKbxCdoWPxHygp20koshCjYHO31vFY9iteqP+52ceO2hcmS1YdO2RIVo+Xvgw78ffBxtwvPEok8iDB/lBCbzhWrKIaGvCFDoE0b9btSSJpUgE3of5Nlm4px8pINc39z4YPOMjpeiOyw6u+iPIgwx8s1kdfryAA7IUThotEYZrzydntIh8YPAUhOhlOXn5ztwpaTl2wIfdRdIj2KAtevw86d0KBBzseekUI44UbhUdRBx+fvhgIwdr4H9x7mod7sQuRjKQPs3pEBdkIIAYC5ObxSNo6+be/xv0+v8u8vp3m47Rhj3s7cLFi3buVwgJkgSXEB17ftPQJfiuVhlDmfzvVUOxwh8r1Tl6zZebQIWq0MsBNCiIwUsdPTqHrmxjS5u+dwMJkgSXEBp9XC7I+uA4aBQcfO2qgckRD5W8oAu5avRuBZQgbYCSFERupVjsarRCKaVMPsDDQa8PaGevVyObA05Imk+Ntvv8XHxwdra2tq1qzJgQMH0i2blJTExIkT8fX1xdramsDAQDZt2mRSJioqiiFDhlCqVClsbGyoU6cOBw8eNDnGxx9/TMWKFbGzs8PDw4MePXpw82bBXOiifpVoOjV5gF6vYdBUbxRZzVGI5xIXr2HJelnBTgghMkurhVmPGueeTow1j8bdzZyp/iA7yAMD7VasWMGwYcOYN28eNWvWZObMmTRr1oyzZ89SokSJVOXHjBnDTz/9xMKFC/H392fz5s20bduWPXv2ULlyZQDeeecdTpw4wdKlS/Hw8OCnn36icePGnDp1Ck9PT2JjYzly5AiffvopgYGBhIeHM3jwYFq3bs2hQ4ey9gRiYtJ+JbVasLY2LZceMzOwscm4bEwM2vh4SEgAW9vH2+PjSTfL1WiMMXw9+AYhOyw5fNSMVX9a0aHRw3TLPvO4YBpvVsomJIA+g6Wns1LW2tr4jjJLSoK4ONKdIPGJsiQmGmYQT4+V1ePjJCUZRg9kR1lLy8fXSlbKJicbyqfHwgJjx9Yny+r1hmvmyXpJr2xWjvussjqdoY7TY25uKJ/Vsnq94ZrIjrKaJ0ZAK4rhGk6PVmt4PYCVIU4kRiXg7xpJ08DbEPdUWTMzwzWRIu7pAs9Z9un3Z1bKZvIzIqVsqmvmeY4Lee4z4pnv+4zKPv1eKiifEdlRNuW5JSdnXL/57TPiifd9Vj4jjGXT+vyFAvEZ8Txl29WOY83nsQyf5UXoXUNdxWKHl5chIW73ehzEZPBetrMzPW5G19rTZTPKv56mqKxGjRrK+++/b7yv0+kUDw8PZfLkyWmWd3d3V7755huTbe3atVO6deumKIqixMbGKlqtVlm/fr1JmSpVqiiffPJJunEcOHBAAZSrV69mKu6IiAgFUCIML3nqn+Bg0x1sbdMuB4oSFGRa1tk53bI6f39FOXTo8Y+7e/rHLVPGpOztoi+lX9bd3fS4AQHpl3VyMi1bpUr6Za2tTcvWrZt+WTAt26hRxmV37lSUQ4eUxAMHlKsNG2ZcNiTk8XHffDPjsuvWPS771lsZl12x4nHZvn0zLrtkyeOygwZlXHbevMdlR4zIuOzMmY/LjhuXcdkvv3xc9ssvMy47btzjsjNnZlx2xIjHZefNy7jsoEGPyy5ZknHZvn0fl12xIuOyb731uOy6dRmWTe7QQVm7dq2SeOCA4drI6LgtWxqP+9rLYRmXbdTI9BrOqGzduqZlra3TL1ulimlZJ6f0ywYEPPdnhL506fTL5uPPCOXQIcPrmFFZ+Yww/GTxMyLxwAFl7dq1StL06RmXzWefEcqbbz4um4XPCGXnzozL5vPPCKVMmfTLZuEzIsrGSQlZH60kJz/Ke4KC0j+ura1pjhQcnHG9PalDByUCFECJiIh4Zm6naktxYmIihw8fZtSoUcZtZmZmNG7cmL1796a5T0JCAtZP/icC2NjYsGvXLgCSk5PR6XQZlklLREQEGo0GJyendM+b8MR/lZGRkRk+N72ioHviP2xznp6d7/nKKopC0hMtI88qm/xE2eKOSRCeTlkwKatVlHT71qQqS/r9cLJSFjB5bhnFYCyr15vs86yyAGaKQkbf0uRU2WRFQcmJsnq9saxGUTL8+sekrF6fcdknYnhWWZ2ioM9kDM9bFr0ei2wqq1cU4PHr96yyOr2eExdt2HfCPoOSj8umyPC4YFI2w8+IrJR9KoasfEZoMypL/v2MyGpZ+YzI/GdEyuuRnEPve7U+I7L6eaJ7nrLkv88Ic0XJls8ICws9dWonoNdbotdn4vPkiRzpme/lJ8tmIj94kkZRHv11UMHNmzfx9PRkz5491K5d27h9xIgR7Nixg/3796fap2vXrhw/fpy1a9fi6+vL1q1badOmDTqdzpi01qlTB0tLS5YtW4arqyvLly+nZ8+e+Pn5cfbs2VTHjI+Pp27duvj7+/Pzzz+nGev48eOZMGFCqu0rFi3C9snuDI8oZmboLR/PzafN4KsXRaNB/8RXJFkqm5Bg+N8oLRoNuqfK7tvryoyZ1bC00DFt2nZKlIhLs6xZQgKaDC4N3RP/dGSpbGIimgwu0iyVtbIy6T6hyeDrlCyVtbQ0ft2lSUrCLLvKWlgYv+7MUtnkZMwy+BpVb2GB8jxldTpDt5P0ypqbozz6ujMrZdHp0GZUVqtFeeKr0UyX1evRZvA1albKKlot+pSyimJ4Hz2j7IIFFdmwoTQNql/kww8Pp102K+/7PPoZkdmy8hkhnxHyGfEcZeUzwii3PiPioqPp1KcPERERODg4pLsf5IE+xVk1a9Ys+vbti7+/PxqNBl9fX3r37s2iRYuMZZYuXUqfPn3w9PREq9VSpUoVunTpwuHDqf+QJSUl0bFjRxRFYe7cuemed9SoUQwbNsx4PzIyEm9vbxq1bv3MSs4OSVFRhOzcSZOiRbGwef4ZJJr6wO6dOrYfdmD92ldZ8eXF7AtSJUl6PSFhYTTx9sYivT7FhZCxXtzcpF6ektW6iY03o+c/JQENw9/S0aRMmZwPUgVyzaRN6iV9KXXT2NNT6uYJcs2kLykujpDwcJrUq4dFkSI5fr5nfbP/JFWTYmdnZ7RaLbdv3zbZfvv2bdzc3NLcx8XFhbVr1xIfH8/9+/fx8PBg5MiRlHnij5Svry87duwgJiaGyMhI3N3d6dSpk0kZeJwQX716lb///jvD5NbKygqrJzu8P2JhYYGFRUZfgGSTR/9hW2g0L/wGmz38OpW6BrDm72LsOnKXhtWisyNC1VmYmcmHTxqkXtKX2bpZ/VdxIqLNKeOZQPNa0ZgV8PqUayZtUi/pk7pJm9RLGh59I2Nhbp4r+VNWzqHqK2VpaUnVqlXZunWrcZter2fr1q0m3SnSYm1tjaenJ8nJyaxatYo2bdqkKmNnZ4e7uzvh4eFs3rzZpExKQnz+/Hn++usvihcvnn1PLI+r6BfPu+0N00kN+rpkhgOchRCPV7Dr2/ZuuhOcCCGEyN9U/3gfNmwYCxcuZMmSJZw+fZp3332XmJgYevfuDUCPHj1MBuLt37+f1atXc+nSJXbu3Enz5s3R6/WMGDHCWGbz5s1s2rSJy5cvExISQsOGDfH39zceMykpiQ4dOnDo0CF+/vlndDodYWFhhIWFkZjR1C8FyMQBNynmmMyJizbMX+2idjhC5FnHz9mw/4Q95lqF3q3uqx2OEEKIHKJ6n+JOnTpx9+5dxo4dS1hYGJUqVWLTpk24uroCcO3aNZOvKuPj4xkzZgyXLl3C3t6e4OBgli5dajJrREREBKNGjeLGjRsUK1aM9u3bM2nSJGMTemhoKOvWrQOgUqVKJvFs27aNBg0a5OhzzguKOer4/N1Q3vuyFJ/O86Bz0wcUd8pg3j8hCqmUVuK2DcNxLS5fqwghREGlelIMMHDgQAYOHJjmY9u3bze5HxQUxKlTpzI8XseOHenYsWO6j/v4+KDipBt5Rr+295i3yoV/z9vy6TwPvht5Xe2QhMhTomPN+Gljygp291SORgghRE5SvfuEUI9WC7MfLb04f7ULx889/6wWQhREyzcXIypGi593PA2rRakdjhBCiBz0XEnxxYsXGTNmDF26dOHOnTsAbNy4kZMnT2ZrcCLnBVWN5s3GD9DrNQye6p3u9INCFEYpXSf6tb0nA+yEEKKAy/LH/I4dO6hYsaJxwFt0tGE6r+PHjzNu3LhsD1DkvK8Hh2JtpWfHkSL8ttVJ7XCEyBMOn7bl8Gk7LC309JIBdkIIUeBlOSkeOXIkn3/+OSEhIVg+sdLKa6+9xr59+7I1OJE7SrknMrJnGAAfzvAmNj69RRyFKDxSWonbv/YQl6IywE4IIQq6LCfF//33H23btk21vUSJEty7JwNR8qvhPcIo6ZbA9duWTFmS9sIpQhQWkdFmLNtUDID+7e6qHI0QQojckOWk2MnJiVu3bqXafvToUTw9PbMlKJH7bK0Vpg65AcBXP7px9ZblM/YQouBatqkYMXFa/H3iqF+lYKz4KIQQImNZToo7d+7Mxx9/TFhYGBqNBr1ez+7du/noo4/o0aNHTsQockmHRg8JqhJFfIIZw2fJPziicFIUjAva9Gt7L2VFUiGEEAVclpPiL774An9/f7y9vYmOjiYgIID69etTp04dxowZkxMxilyi0cDs4dcxM1NY+Vcxth+yVzskIXLdwZO2HDtni5Wlnp4tZYCdEEIUFllKihVFISwsjNmzZ3Pp0iXWr1/PTz/9xJkzZ1i6dClarTan4hS55JWycQxob+hDOWiqN8kyvkgUMimtxG82CqeYo6zyKIQQhUWWVrRTFAU/Pz9OnjxJ2bJl8fb2zqm4hIom9r/J8s3F+O+CLQvWuPDemzLQSBQOEdFm/LKlKAD928t1L4QQhUmWWorNzMwoW7Ys9+/LV4oFWXEnHZ8NuAnAp/M8eBAh3wCIwuGnDcWJjdcSUCaOuoExaocjhBAiF2W5T/GXX37J8OHDOXHiRE7EI/KI/u3uUtEvlgcR5oyd56F2OELkOMMAO8PcxP3b3ZUBdkIIUchkOSnu0aMHBw4cIDAwEBsbG4oVK2byIwoGc3OY9dF1AOaucuG/C9YqRyREztr3nx3/XbDF2krPW8EP1A5HCCFELstSn2KAmTNn5kAYIi9qWC2aDo3C+W1rUQZ9XZK/552T1jNRYKW0Endq8oCiDjLATgghCpssJ8U9e/bMiThEHjV1yA3W73Jk++EirNrqRIfGD9UOSYhsFx6pZUVIygp2sjKnEEIURllOigF0Oh1r167l9OnTAFSoUIHWrVvLlGwFUCn3REb0CGPiQg8+muVFi1cjsLFW1A5LiGz145/FiU8wo6JfLLUqygA7IYQojLLcp/jChQuUL1+eHj16sHr1alavXk337t2pUKECFy9ezIkYhco+7hmGt2siV29Z8fVSN7XDESJbmQ6wkxXshBCisMpyUjxo0CB8fX25fv06R44c4ciRI1y7do3SpUszaNCgnIhRqMzWWmHqkBsAfPmDG9fCLFSOSIjss+uYPacv22BrraN7sEw3KYQQhVWWk+IdO3YwZcoUk5kmihcvzpdffsmOHTuyNTiRd7zZOJygKlHEJZgxfJaX2uEIkW1SWom7NAvH0V6vcjRCCCHUkuWk2MrKiqioqFTbo6OjsbS0zJagRN6j0RimaDMzU/g1pBg7DturHZIQL+z+Qy2/bX20gl07WcFOCCEKsywnxS1btqRfv37s378fRVFQFIV9+/YxYMAAWrdunRMxijwi8KU4+rU1jMwfPM0bncxaJfK5pX86k5BoRuVysVQLiFU7HCGEECrKclI8e/ZsfH19qV27NtbW1lhbW1O3bl38/PyYNWtWTsQo8pDP3g3FqUgyx8/ZsnCNs9rhCPHcFAX+t9YFkBXshBBCPMeUbE5OTvz+++9cuHDBOCVb+fLl8fPzy/bgRN7j7KTjswE3+eDrkoyZ60nHJuEUc5QmY5H/nDhRnHNXbbC31dG1uaxgJ4QQhd1zzVMM4OfnJ4lwITWg/V3mr3bhxEUbxs33YM6I62qHJESWbdniA0DXZg8oYicD7IQQorDLcveJ9u3b89VXX6XaPmXKFN58881sCUrkbebmhkF3AHNXuXDigrXKEQmRNXfDzdm71wOA/u1lgJ0QQojnSIr/+ecfgoODU21//fXX+eeff7IlKJH3vVY9inYNw9HpNAye5o0ii9yJfOTH9c4kJ5tRtXwMVfzj1A5HCCFEHpDlpDi9qdcsLCyIjIzMlqBE/jB1yA2sLPX8fdCBNduc1A5HiEzR6+H7RwPs+ra9o3I0Qggh8oosJ8UVK1ZkxYoVqbb/8ssvBAQEZEtQIn8o7ZnIiB5hAAyb4UVcvAzfF3nftkNFuHDdGhubJDo2lQF2QgghDLI80O7TTz+lXbt2XLx4kddeew2ArVu3snz5clauXJntAYq87eOet1n8hzNXb1kx9SdXPn0nTO2QhMhQygp2QUE3sLfV8xxtA0IIIQqgLP81aNWqFWvXruXChQu89957fPjhh9y4cYO//vqLN954IwdCFHmZnY2erwfdAGDyYneuh1moHJEQ6Qu7Z86abYYV7Jo1u6JuMEIIIfKU52oiadGiBbt37yYmJoZ79+7x999/ExQUlN2xiXyiU9Nw6lWOIi7BjBGzvdQOR4h0Lf7DmWSdhhovR1O6tIyBEEII8dgLfW8YHx/PkiVL+O677zh//nx2xSTyGY0GZn14HY1G4Zctxdh51F7tkIRIRa+HhWsNXSf6tpVp2IQQQpjKdFI8bNgwPvjgA+P9xMREatWqRd++fRk9ejSVK1dm7969ORKkyPsq+8fRr+09AD742hudLHIn8piQ/Q5cDrXC0T6ZN5vIADshhBCmMp0Ub9myhSZNmhjv//zzz1y7do3z588THh7Om2++yeeff54jQYr84fP3QnEqkszxc7b871GLnBB5RcoAux4tHmBrLSvYCSGEMJXppPjatWsmU65t2bKFDh06UKpUKTQaDYMHD+bo0aM5EqTIH5yddEzofxOAT77zJDxSq3JEQhjcvGvBun+cAOjfTrpOCCGESC3TSbGZmRnKE8uW7du3j1q1ahnvOzk5ER4enr3RiXzn3Q53CSgTx/0Ic8YvcFc7HCEAWPR7cXQ6DXUDo6ngG692OEIIIfKgTCfF5cuX548//gDg5MmTXLt2jYYNGxofv3r1Kq6urtkfochXLMwNg+4Avl1ZgpMXrVWOSBR2Ot3jAXbSSiyEECI9mU6KR4wYwahRo2jUqBGNGjUiODiY0qVLGx/fsGEDNWrUyJEgRf7SuGYUbRuGo9NpGDzNmye+YBAi123e68C1MCuKOiTToZF8myWEECJtmU6K27Zty4YNG3jllVcYOnRoqqWebW1tee+997I9QJE/TRtyAytLPVsPOLB2u5Pa4YhCbP5qFwB6triPjbX8hyaEECJtWVrmOaWVOC3jxo3LloBEwVDaM5GPut9m0iJ3Ppzpxet1IrC2koRE5K4bty1Yv8sRgH7t7qkcjRBCiLzshRbvECIjo3qH4VkikcuhVkz7Sfqbi9z3/e/O6PUa6leJonxpGWAnhBAifZIUixxjZ6Pn60E3APhisRs3bluoHJEoTJKT4X+/ywA7IYQQmSNJschRnZuF82qlKGLjtYyY7aV2OKIQ2bjHkRu3LSnumEz71x6qHY4QQog8TpJikaM0Gpj90XU0GoXlm4ux65id2iGJQiJlBbtere5hZSn92YUQQmQs00lxUlISZ8+eNd7fu3dvjgQkCp7K/nG884ZhkNOgr0ui06kckCjwrt6yZMPuRwPs2soAOyGEEM+W6aS4Z8+etGrVitGjRwPw4Ycf5lhQouCZ9N5NHO2TOXrWlkXrnNUORxRw/1vrjKJoaFgtkpdKJagdjhBCiHwg00nxiRMnOHfuHBYWFnz77bc5GZMogFyKJjOh/y0ARn/rwcMorcoRiYIqKRm+/704AP1lGjYhhBCZlOmk2N3dHYAJEyawe/duLl++nGNBiYLpvTfvEFAmjnsPLRi/wF3tcEQBtX6nE7fuWeJSNIm2DR+qHY4QQoh8ItNJcd26dUlOTgZg3rx51KxZM8eCEgWThTnMHHYdgG9+LcGpS9YqRyQKopQBdn1a38fSQgbYCSGEyJxMJ8Vjx47F3NywAJ6DgwNr165NVSYuLi7bAhMFU5NaUbQJeohOp2HING8UyVlENrocasmWfQ4A9G0rcxMLIYTIvGyZki0hIYFp06ZRunTp7DicKOCmDb2OpYWekP0OrNvhqHY4ogBZ+GiAXZOakfh6JaodjhBCiHwk00lxQkICo0aNolq1atSpU8fYUrx48WJKly7NzJkzGTp0aE7FKQoQX69EPup+G4BhM7yJT9CoHJEoCJKSMc5sIivYCSGEyKosdZ+YO3cuPj4+XLlyhTfffJN+/foxY8YMpk+fzpUrV/j4449zMlZRgIzqHYaHSyKXQq2Y/rOr2uGIAuD37U7cvm+BW/EkWgc9VDscIYQQ+Uymk+KVK1fy448/8ttvv7FlyxZ0Oh3JyckcP36czp07o9XKFFsi8+xt9UwZFArAF4vdCL1joXJEIr+bv9oFgD6t72FhrnIwQggh8p1MJ8U3btygatWqALz88stYWVkxdOhQNJoX/+r722+/xcfHB2tra2rWrMmBAwfSLZuUlMTEiRPx9fXF2tqawMBANm3aZFImKiqKIUOGUKpUKWxsbKhTpw4HDx40KaMoCmPHjsXd3R0bGxsaN27M+fPnX/i5iMzr2vwBdV6JJiZOy8dzPNUOR+RjF65b8dcBBzQahb6ygp0QQojnkOmkWKfTYWlpabxvbm6Ovb39CwewYsUKhg0bxrhx4zhy5AiBgYE0a9aMO3fupFl+zJgxzJ8/nzlz5nDq1CkGDBhA27ZtOXr0qLHMO++8Q0hICEuXLuW///6jadOmNG7cmNDQUGOZKVOmMHv2bObNm8f+/fuxs7OjWbNmxMfHv/BzEpmj0cDs4dfRaBR+3lic3cfs1A5J5FML1xj6EjerHYmPhwywE0IIkXWZTooVRaFXr160a9eOdu3aER8fz4ABA4z3U36yavr06fTt25fevXsTEBDAvHnzsLW1ZdGiRWmWX7p0KaNHjyY4OJgyZcrw7rvvEhwczLRp0wDDtHCrVq1iypQp1K9fHz8/P8aPH4+fnx9z5841PpeZM2cyZswY2rRpwyuvvMKPP/7IzZs305xqTuScquVjebuNoWVv0FRvdDqVAxL5TmKShsV/pKxgJwPshBBCPJ9M97zr2bOnyf3u3bu/8MkTExM5fPgwo0aNMm4zMzOjcePG7N27N819EhISsLY2XfTBxsaGXbt2AZCcnIxOp8uwzOXLlwkLC6Nx48bGxx0dHalZsyZ79+6lc+fOaZ43ISHBeD8yMhIwdOdISkrKytN+LkmPFk5JUhTQ63P8fLlp/IAbrPyrKEfO2PG/34vR542sff2d9Kg+kgpYvbyowlIvK/8uxt1wCzxcEmlWJ5ykTDzdwlI3WSX1kjapl/RJ3aRN6iV9SY8WKEhKTobcyJ+ycI5MJ8WLFy9+rmAycu/ePXQ6Ha6uprMPuLq6cubMmTT3adasGdOnT6d+/fr4+vqydetWVq9eje5RE2ORIkWoXbs2n332GeXLl8fV1ZXly5ezd+9e/Pz8AAgLCzOe5+nzpjz2tMmTJzNhwoRU27ds2YKtrW3WnvgLCAkPh/DwXDtfbmnf0ZxFiyry8bce2Aecwt4+OcvHCEnntSvsCnq9fPWLDwCvNrzMljs3s7RvQa+b5yX1kjapl/RJ3aRN6iV9ITt35sp5YmNjM102343RnjVrFn379sXf3x+NRoOvry+9e/c26W6xdOlS+vTpg6enJ1qtlipVqtClSxcOHz783OcdNWoUw4YNM96PjIzE29ubpk2b4uDg8ELPKTOSoqII2bmTJkWLYmFjk+Pny21N3klg999xnL1iw8E/q/L10OuZ3jdJryckLIwmbm5YmGXLejQFQmGol7NXrfnvPxfMzBS+6B5PSTePTO1XGOrmeUi9pE3qJX1SN2mTeklfUlwcIeHhNKlXD4siRXL8fCnf7GeGqkmxs7MzWq2W27dvm2y/ffs2bm5uae7j4uLC2rVriY+P5/79+3h4eDBy5EjKlCljLOPr68uOHTuIiYkhMjISd3d3OnXqZCyTcuzbt2/j7u5uct5KlSqleV4rKyusrKxSbbewsMDCIhemE3u0xLaFRlMg32AWljD7o+s0G/gS3/7qSv929ylfOmuDHi3MzApk3byoglwvi9eWAOD1OhH4eiST1UU6C3LdvAipl7RJvaRP6iZtUi9peDRrmYW5ea7kT1k5h6qvlKWlJVWrVmXr1q3GbXq9nq1bt1K7du0M97W2tsbT05Pk5GRWrVpFmzZtUpWxs7PD3d2d8PBwNm/ebCxTunRp3NzcTM4bGRnJ/v37n3lekXOa1oqidf2HJOs0DJnmxaNuR0KkKT5Bww/rUwbYyTRsQgghXozq/74MGzaMhQsXsmTJEk6fPs27775LTEwMvXv3BqBHjx4mA/H279/P6tWruXTpEjt37qR58+bo9XpGjBhhLLN582Y2bdrE5cuXCQkJoWHDhvj7+xuPqdFoGDJkCJ9//jnr1q3jv//+o0ePHnh4ePDGG2/k6vMXpqYPu46lhZ4t+xz54x9HtcMRediqv4vyIMIcL9dEXq8ToXY4Qggh8jnV+xR36tSJu3fvMnbsWMLCwqhUqRKbNm0yDoK7du0aZk989RAfH8+YMWO4dOkS9vb2BAcHs3TpUpycnIxlIiIiGDVqFDdu3KBYsWK0b9+eSZMmmTShjxgxgpiYGPr168fDhw959dVX2bRpU6pZK0Tu8vVKZFi323z5gzvDZnjRrHYkVpbSZCxSm7/aMDdx3zfupfQuEkIIIZ5bnvhTMnDgQAYOHJjmY9u3bze5HxQUxKlTpzI8XseOHenYsWOGZTQaDRMnTmTixIlZilXkvNG9w1iyvjgXb1gzY1kJRva6/eydRKFy6pI1O48WQatVjPNcCyGEEC9C9e4TQjytiJ2eKYMMqw9+/r07N+/mwkBGka8seLSCXctXI/AskfPzXAohhCj4JCkWeVK31x9Q+5VoYuK0fDzbU+1wRB4SF69hyXpZwU4IIUT2kqRY5EkajWGKNo1G4aeNxdn7r53aIYk8YuVfRXkYZU4p9wSa1sr8/JNCCCFERiQpFnlWtYBYere6D8Cgqd4FbXVr8Zzmr3YBDAPstFqVgxFCCFFgSFIs8rQv3g/FwU7HoVN2/PBHcbXDESo7ccGaPf/aY65V6CMD7IQQQmQjSYpFnuZaPJlxfW8CMOpbTyKi5ZItzFJaiVvXf4i7c7LK0QghhChIJMMQed7ATncpVyqeOw8smLjQQ+1whEpi4zUs3VAMgP7tZYCdEEKI7CVJscjzLC0UZn54HYDZv5TgzBUrlSMSalixpRgR0eaU8UygcY0otcMRQghRwEhSLPKF5nUiaVnvIck6DUOne6PIIneFjnEFu7Z3MZNPLiGEENlM/rSIfGP60BtYmOvZtMeRP3c5qh2OyEXHztqw/4RhgF3KjCRCCCFEdpKkWOQbZUsmMKzbHQCGTvciIVGjckQit6QMsGvbMBzX4jLATgghRPaTpFjkK5/0uYW7cyIXrlszc1kJtcMRuSA61oyfNz0aYNdOpmETQgiRMyQpFvlKETs9Xw4MBeDzRe7cumeuckQipy3fXIyoGC1+3vE0rCYD7IQQQuQMSYpFvtM9+AE1X44mOlbLyDleaocjcljKALv+7e7JADshhBA5Rv7EiHzHzAzmDDdM0fbjn8U5cMJO5YhETjl82pbDp+2wtNDTq5V0nRBCCJFzJCkW+VL1CrH0fpQkDZlaEr1e5YBEjkhpJW7/2kOcnXQqRyOEEKIgk6RY5FtfvB9KETsdh07Zs21bSbXDEdksMtqMZcYBdrKCnRBCiJwlSbHIt9yckxn7zi0Ali4tT2S0XM4FybJNxYiJ0+LvE0f9KtFqhyOEEKKAkyxC5GuDOt+hbMk4Hj605otFHmqHI7KJojyem7hf23toZEpqIYQQOUySYpGvWVooTBtmGHQ35xdXzl6xUjkikR0OnrTl2DlbrCz19GwpK9gJIYTIeZIUi3yveZ0IqlULIynZjKHTvdUOR2SDlFbiNxuFU8xRBtgJIYTIeZIUiwKhT58TWJjr2bjHkT93OagdjngBEdFm/LKlKAD928sAOyGEELlDkmJRIHh4xPBB59sADJ3uTWKSdELNr37aUJzYeC0BZeKoGxijdjhCCCEKCUmKRYExus9NXIsncf6aNbOWl1A7HPEcDAPsUlawuysD7IQQQuQaSYpFgeFgr+erD24AMPF/7ty6Z65yRCKr9v5rx38XbLG20vNW8AO1wxFCCFGISFIsCpS3gh9Qo0IM0bFaRn3jqXY4IotSBth1avKAog4ywE4IIUTukaRYFChmZjB7+DUAlqx3Zv8JW5UjEpkVHqnl178eDbBrd0/laIQQQhQ2khSLAqfmy7H0bGlIqgZ9XRK9XuWARKb8+Gdx4hPMqOgXS62KMsBOCCFE7pKkWBRIkweGYm+r48BJO5ZuKKZ2OOIZnhxgN6C9rGAnhBAi90lSLAokd+dkxr5zC4CRc7yIjJZLPS/bdcye05dtsLXW0e11WcFOCCFE7pNMQRRYg7vcoWzJeMLuW/D59+5qhyMykNJK3KVZOI720t9FCCFE7pOkWBRYlhYKM4ZdB2Dm8hKcu2qlckQiLfcfavlta8oAO1nBTgghhDokKRYFWotXI3m9TgRJyWYMm+GldjgiDUvWFych0YzK5WKpFhCrdjhCCCEKKUmKRYE3Y9h1zLUKf+5yYuNuB7XDEU9QFFiwxjA3saxgJ4QQQk2SFIsCr5xPAkO63gZgyDRvEpMk88ordhy25+xVa+xtdXRtLivYCSGEUI8kxaJQ+PTtW7gWT+LcNWtm/1JC7XDEIykr2HVt9oAidjLATgghhHokKRaFgoO9nsnvhwIw8X/uhN0zVzkicTfcnFV/OwHQv70MsBNCCKEuSYpFodGz5X2qB8QQFaNl9LeeaodT6P3wR3GSks2oFhBDFf84tcMRQghRyElSLAoNMzOYPdwwRdviP5w5eNJW5YgKL70eFqwxzE0s07AJIYTICyQpFoVKrYox9GhhWDFt0FRv9NKNVRV/HyzChevWFLHT0blpuNrhCCGEEJIUi8Lnyw9uYG+rY99/9vy0oZja4RRKKQPsur9+H3tb+c9ECCGE+mS0UX6TmGjoByAeS2nujY/PVN2428OYHtcZOc+Hj+d40rbObYrY6nI4SBVksV5yS9h9C9ZudwSgf4tQQ3y5LY/WjeqkXtIm9ZI+qZu0Sb2kLzFR7QjSJUlxfqHVGn4nJkJSkrqx5DWKYvgdHU1mV38Y8vpZ/rfOhQs37Zi0sARf9jmXgwGq5DnqJTcsXlOGZJ0ZNcs9JNDtNkSpEEQerRvVSb2kTeolfVI3aZN6SV9K3aTkNXmIJMX5haWl4XfdumAuL5uJ5GTYti1LdWMFzJhtRqsOMOP30rzzqTt+vkrOxpnbnqNecppeDwvftQZgwFAbqF9fnUDyYN3kCVIvaZN6SZ/UTdqkXtKXUjcpeU0eIq9UfmNjAxYWakeRt6S0nGexblq0g+bNYdMmDcM+sWHduhyKTy3PWS85KWQzXL4Cjo7Q8S0rUGsCkDxYN3mC1EvapF7SJ3WTNqmX9OXhb7ulo4sotDQamDHD8E/8H3/Apk1qR1TwzZ9v+N2jB9jKjHhCCCHyEEmKRaHm7w+DBhluDxmSp/v/53s3b2Jsje/fX91YhBBCiKdJUiwKvbFjoUQJOHsWvvlG7WgKrkWLQKczdLGrUEHtaIQQQghTkhSLQs/RESZPNtyeMAFu31Y3noJIp4OFCw23pZVYCCFEXiRJsRBAr15QrRpERsLo0WpHU/Bs3gzXrkHRotChg9rRCCGEEKlJUiwEhrnVZ8823F68GA4dUjeegiZlgF3PnobB2EIIIUReI0mxEI/Urg3duxvmFR806PH84uLF3LgB69cbbvfrp24sQgghRHokKRbiCV99BXZ2sHcv/Pyz2tEUDN9/b1i0o359KF9e7WiEEEKItElSLMQTPDxgzBjD7REjIEqNJYgLkORk+N//DLdlgJ0QQoi8TPWk+Ntvv8XHxwdra2tq1qzJgQMH0i2blJTExIkT8fX1xdramsDAQDY9teKCTqfj008/pXTp0tjY2ODr68tnn32G8sR34dHR0QwcOBAvLy9sbGwICAhg3rx5OfYcRf4ydCj4+sKtW/DFF2pHk79t2GDoPlG8OLRvr3Y0QgghRPpUTYpXrFjBsGHDGDduHEeOHCEwMJBmzZpx586dNMuPGTOG+fPnM2fOHE6dOsWAAQNo27YtR48eNZb56quvmDt3Lt988w2nT5/mq6++YsqUKcyZM8dYZtiwYWzatImffvqJ06dPM2TIEAYOHMi6ArfOr3geVlYwfbrh9vTpcOGCuvHkZykD7Hr1MtSrEEIIkVepmhRPnz6dvn370rt3b2Nrra2tLYsWLUqz/NKlSxk9ejTBwcGUKVOGd999l+DgYKZNm2Yss2fPHtq0aUOLFi3w8fGhQ4cONG3a1KQFes+ePfTs2ZMGDRrg4+NDv379CAwMzLCVWhQurVpB06aGFe4+/FDtaPKnq1dh40bDbRlgJ4QQIq8zV+vEiYmJHD58mFGjRhm3mZmZ0bhxY/bu3ZvmPgkJCVhbW5tss7GxYdeuXcb7derUYcGCBZw7d46XXnqJ48ePs2vXLqanNP09KrNu3Tr69OmDh4cH27dv59y5c8yYMSPdeBMSEkhISDDej4yMBAxdOpKSkrL25J9Dyjly41z5TU7Vzddfw99/m7NunYYNG5Jp0iR/TUeh9jUzf74ZiqKlQQM9pUvryEuXrtp1k1dJvaRN6iV9Ujdpk3pJX27XTVbOo1EUdSaeunnzJp6enuzZs4fatWsbt48YMYIdO3awf//+VPt07dqV48ePs3btWnx9fdm6dStt2rRBp9MZE1a9Xs/o0aOZMmUKWq0WnU7HpEmTTJLvhIQE+vXrx48//oi5uTlmZmYsXLiQHj16pBvv+PHjmTBhQqrty5Ytw9bW9kWqQuRhixZVYN06P7y8opg5cxvm5vkrMVZLcrKGvn2bEh5uzUcfHeTVV2+qHZIQQohCKDY2lq5duxIREYGDg0OGZVVrKX4es2bNom/fvvj7+6PRaPD19aV3794m3S1+/fVXfv75Z5YtW0aFChU4duwYQ4YMwcPDg549ewIwZ84c9u3bx7p16yhVqhT//PMP77//Ph4eHjRu3DjNc48aNYphw4YZ70dGRuLt7U3Tpk2fWcnZISkpiZCQEJo0aYKFhUWOny8/ycm6qVMHKlRQuHGjCJcvt2DwYH22Hj8nqXnNrF2rITzcHBcXhfHjK2FpWSlXz/8s8n5Km9RL2qRe0id1kzapl/Tldt2kfLOfGaolxc7Ozmi1Wm7fvm2y/fbt27i5uaW5j4uLC2vXriU+Pp779+/j4eHByJEjKVOmjLHM8OHDGTlyJJ07dwagYsWKXL16lcmTJ9OzZ0/i4uIYPXo0a9asoUWLFgC88sorHDt2jKlTp6abFFtZWWGVxkghCwuLXL3gc/t8+UlO1I2Li2EGir594bPPtPTooaVEiWw9RY5T45r5/nvD7z59NNjZ5d3rVd5PaZN6SZvUS/qkbtIm9ZK+3KqbrJxDtYF2lpaWVK1ala1btxq36fV6tm7datKdIi3W1tZ4enqSnJzMqlWraNOmjfGx2NhYzMxMn5ZWq0WvN7TwpfQBzqiMEE/q3RuqVIHISPjkE7WjyfsuX4YtWwy3+/ZVNxYhhBAis1TtPjFs2DB69uxJtWrVqFGjBjNnziQmJobevXsD0KNHDzw9PZk8eTIA+/fvJzQ0lEqVKhEaGsr48ePR6/WMGDHCeMxWrVoxadIkSpYsSYUKFTh69CjTp0+nT58+ADg4OBAUFMTw4cOxsbGhVKlS7Nixgx9//NFkMJ4QKbRamD0bXn3V0AI6YABUrap2VHnXwoWGJbKbNDHM9yyEEELkB6omxZ06deLu3buMHTuWsLAwKlWqxKZNm3B1dQXg2rVrJi268fHxjBkzhkuXLmFvb09wcDBLly7FycnJWGbOnDl8+umnvPfee9y5cwcPDw/69+/P2LFjjWV++eUXRo0aRbdu3Xjw4AGlSpVi0qRJDBgwINeeu8hf6taFbt0MSz8PGgS7doFGo3ZUeU9SEqR08ZcV7IQQQuQnqg+0GzhwIAMHDkzzse3bt5vcDwoK4tSpUxker0iRIsycOZOZM2emW8bNzY3FixdnNVRRyH31FaxdC3v2wLJlhiRZmPr9d7h9G9zcoHVrtaMRQgghMk/1ZZ6FyC88PWH0aMPtESMgOlrdePKilBXs+vQBGVsihBAiP5GkWIgsGDYMypSBmzfhUVd38ciFC/DXX4ZuJTLATgghRH4jSbEQWWBtDSnjMadNg0uX1I0nL1m40PC7WTPw8VE1FCGEECLLJCkWIotatzbMrJCQAB9+qHY0eUNCAqR005cBdkIIIfIjSYqFyCKNBmbONEzVtnYthISoHZH61qyBu3fBwwNatlQ7GiGEECLrJCkW4jkEBEDKpCmDBxumIivMUgbYvf02mKs+p40QQgiRdZIUC/Gcxo8HZ2c4fRq++07taNRz9ixs3w5mZvDOO2pHI4QQQjwfSYqFeE5OTvDFF4bb48YZug8URgsWGH6//jqULKluLEIIIcTzkqRYiBfQpw9UrgwRETBmjNrR5L74ePjhB8NtGWAnhBAiP5OkWIgXoNXC7NmG2wsXwpEj6saT21atggcPwNsbgoPVjkYIIYR4fpIUC/GCXn0VunQBRYFBgwy/C4uUAXbvvGP4B0EIIYTIryQpFiIbTJkCtrawezf88ova0eSOU6dg505DMvz222pHI4QQQrwYSYqFyAZeXjB6tOH28OEQE6NuPLkhZYBdy5bg6aluLEIIIcSLkqRYiGzy4YdQujSEhsKXX6odTc6Ki4MlSwy3ZYCdEEKIgkCSYiGyibU1TJtmuP3113Dpkrrx5KSVK+HhQyhVCpo2VTsaIYQQ4sVJUixENnrjDWjUCBIS4KOP1I4m56QMsOvbVwbYCSGEKBhkQVYhspFGA7NmQWAgrFkDW7cakuSC5MQJ2LPHsJxznz5qRyOEKAh0Oh1JSUlqh5FtkpKSMDc3Jz4+Hp1Op3Y4eUp2142FhQXabGqdkaRYiGxWoQK8/75h/uLBg+HoUbCwUDuq7JPSSty6Nbi7qxuLECJ/UxSFsLAwHj58qHYo2UpRFNzc3Lh+/ToajUbtcPKUnKgbJycn3NzcXvh4khQLkQPGj4eff4aTJ2HuXMP8xQVBTAwsXWq4LQPshBAvKiUhLlGiBLa2tgUmgdTr9URHR2Nvb4+ZmfRUfVJ21o2iKMTGxnLnzh0A3F+wpUaSYiFyQNGiMGkSDBgA48YZFvdwcVE7qhe3YoVhSesyZaBxY7WjEULkZzqdzpgQFy9eXO1wspVerycxMRFra2tJip+S3XVjY2MDwJ07dyhRosQLdaWQV0qIHPLOO1CpkmGWhk8/VTua7PHkADv5nBdCvIiUPsS2trYqRyLyu5Rr6EX7pcufNSFyiFZr6FcMhoUujh1TNZwXduwYHDhgGGDXu7fa0QghCoqC0mVCqCe7riFJioXIQfXqQefOoCiGfsWKonZEzy+llbhtW3B1VTcWIYQQIrtJUixEDpsyBWxsYOdOQ5/c/Cg62jBwEGSAnRAib9HpYPt2WL7c8Duvz4DWoEEDhgwZkmEZHx8fZs6cmSvxiMckKRYih3l7w6hRhtvDhxtmcMhvli+HqCgoWxYaNlQ7GiGEMFi9Gnx8DJ9LXbsafvv4GLbnlF69eqHRaFL9XLhwIedO+pTY2FhGjRqFr68v1tbWuLi4EBQUxO+//55rMRREkhQLkQs++sjwQX3jBnz1ldrRZF1K14l+/WSAnRAib1i9Gjp0MHyuPik01LA9JxPj5s2bc+vWLZOf0qVL59wJnzJgwABWr17NnDlzOHPmDJs2baJDhw7cv38/x86ZmJiYY8fOK+TPmxC5wMYGpk0z3P76a7hyRdVwsuTwYcOPpSX06qV2NEKIgkpRDN+kZeYnMjL9cRop2wYPNpTLzPGyOt7DysoKNzc3k5+UqcB27NhBrVq1cHV1xdPTk5EjR5KcnJzuse7cuUOrVq2wsbGhdOnS/JzSVy0D69atY/To0QQHB+Pj40PVqlX54IMP6PPEMqMJCQl8/PHHeHt7Y2VlhZ+fH99//73x8R07dlCjRg2srKxwd3dPFWeDBg0YOHAgQ4YMwdnZmWbNmgFw4sQJXn/9dezt7XF1deWtt97i3r17WavAPEqSYiFySdu28NprEB9vaDnOL1Jaidu3B2dndWMRQhRcsbFgb5+5H0dHQ4twehTF0ILs6Ji548XGZs9zCA0NJTg4mGrVqrFz506+/fZbvv/+ez7//PN09+nVqxfXr19n27Zt/Pbbb3z33XfGxSjS4+bmxoYNG4iKikq3TI8ePVi+fDmzZ8/m9OnTzJ8/H3t7e5M4q1evzvHjx5k7d26acS5ZsgRLS0t2797NvHnzePjwIa+99hqVK1fm0KFDbNq0idu3b9OxY8cs1FLeJYt3CJFLNBqYNcswd/GqVfD334YkOS+LjIRlywy3ZYCdEEIYrF+/3phgArz++uusXLmS7777Dm9vb+bMmUNUVBTVqlUjLCyMjz/+mLFjx6ZarOLcuXNs3LiRAwcOUL16dQC+//57ypcvn+H5FyxYQLdu3ShevDiBgYG8+uqrdOjQgbp16xqP++uvvxISEkLjRystlSlTxrh/SpzffPMNGo0Gf39/bt68mSrOsmXLMmXKFON+n3/+OZUrV+aLL74wblu0aBHe3t6cO3eOl1566XmqM8+QlmIhctHLL8O77xpuDx4MGXyjlicsW2b4atHfH+rXVzsaIURBZmtrmOkmMz8bNmTumBs2ZO54WV0/pGHDhhw7dsz4M/vRpPSnT5+mdu3aJvPm1q1bl+joaG483fn5UXlzc3OqVq1q3Obv74+Tk1OG569fvz6XLl1i69atdOjQgZMnT1KvXj0+++wzAI4dO4ZWqyUoKCjN/TMb55NxARw/fpxt27Zhb29v/PH39wfg4sWLGcacH0hLsRC5bMIEw2wOJ07AvHkwcKDaEaVNUUwH2Mn8+kKInKTRgJ1d5so2bQpeXoYuFGn1B9ZoDI83bWpYSCm72dnZ4efnl/0HzgILCwvq1atHvXr1+Pjjj/n888+ZOHEiH3/8sXHp4xdl99QLEh0dTatWrfgqjRHj7u7u2XJONUlLsRC5rFgxSOm2NXYs5OBg4Rdy8KBhFTsrK+jZU+1ohBDiMa3W0B0NUv/DnnJ/5sycSYgzUr58efbu3YvyRKa+e/duihQpgpeXV6ry/v7+JCcnc/jwYeO2s2fP8vDhwyyfOyAggOTkZOLj46lYsSJ6vZ4dO3ZkS5wpqlSpwsmTJ/Hx8cHPz8/k5+kEOj+SpFgIFfTtC4GBEB4On36qdjRpS2klfvNNQyIvhBB5Sbt28Ntv4Olput3Ly7C9Xbvcj+m9997j+vXrDBo0iHPnzvH7778zbtw4hg0blqo/MUC5cuVo3rw5/fv3Z//+/Rw+fJh33nnnmS29DRo0YP78+Rw+fJgrV66wYcMGRo8eTcOGDXFwcMDHx4eePXvSp08f1q5dy+XLl9m+fTu//vqrSZwffPABZ86ceWacKd5//30ePHhAly5dOHjwIBcvXmTz5s307t0bXV5fNSUTJCkWQgVaLTzqgsb8+XD8uLrxPO3hQ/jlF8NtGWAnhMir2rUzTHG5bZthDMS2bXD5sjoJMYCnpycbNmzg4MGD1KtXj/fee4+3336bMWPGpLvP4sWL8fDwICgoiHbt2tGvXz9KlCiR4XmaNWvGkiVLaNq0KeXLl+eDDz6gWbNmxqQXYO7cuXTo0IH33nsPf39/+vbtS8yj1aNS4jxw4ACBgYEMGDDgmXECeHh4sHv3bnQ6HU2bNqVixYoMGTIEJyenDJPp/EL6FAuhkvr1oWNH+PVXw3yb27fnnX67P/1kmKIoIAAeDWYWQog8SauFBg1y73w//PBDho8HBQWxb98+IiMjcXBwSJUsbt++3eS+m5sb69evN9n21ltvZXiOUaNGMSplqdR0WFtbM336dKZPn55unAcOHEh3/6fjTFG2bFlW5+TKKCrK/2m9EPnY118bFvb45x9YuVLtaAyeHGDXv3/eSdSFEEKInCRJsRAqKlkSRo403P7oo+ybQP5F7N1rmBnD2hqe0VghhBBCFBiSFAuhsuHDoVQpuH4dnpgjXTUprcSdOkHRourGIoQQQuQWSYqFUJmNDUydarj91Vdw9ap6sYSHG/o4gwywE0IIUbhIUixEHtC+vWGgSHy8oRuFWn780RDDK69ArVrqxSGEEELkNkmKhcgDNBrDRPRmZob5Nbdty/0YZICdEEKIwkySYiHyiFdegXffNdwePBiSk3P3/Lt2wenTYGsL3brl7rmFEEIItUlSLEQeMnGiYfW4//6DBQty99wprcRduoCjY+6eWwghhFCbJMVC5CHFisFnnxlujxkD9+/nznnv3zd02wAZYCeEEKJwkqRYiDymXz+oWNEwE8TYsblzziVLICEBKleGatVy55xCCCFEXiJJsRB5jLk5zJ5tuD1vHvz7b86eT1Eed9WQAXZCCJGxXr16odFoGDBgQKrH3n//fTQaDb1791YhMlM//PADGo0GjUaDmZkZ7u7udOrUiWvXrhnL/P777zRp0oSaNWtSp04dLl++nKVz/Pvvv9SrVw9ra2u8vb2ZkonJ9rdu3UrTpk1xdHTEzc2Njz/+mOSnBtH8+uuvVKpUCVtbW0qVKsXXX3+dpbielyTFQuRBDRrAm2+CXm8YdKcoOXeuHTvg7Fmwt4euXXPuPEIIUVB4e3vzyy+/EBcXZ9wWHx/PsmXLKFmypIqRmXJwcODWrVuEhoayatUqzp49y5tvvml8/PXXXyckJIT9+/cTEBDApk2bMn3syMhImjZtSqlSpTh8+DBff/0148ePZ0EGA2KOHz9Oy5Ytady4MYcPH2bFihWsW7eOkSlLuwIbN26kW7duDBgwgBMnTvDdd98xY8YMvvnmm+erhCyQpFiIPOrrrw1LLW/f/ri/b05IGWDXtSsUKZJz5xFCiEyJiUn/Jz4+82WfSFgzLPscqlSpgre3N6tXrzZuW716NSVLlqRy5comZfV6PZMnT6Z06dLY2NgQGBjIb098qOt0Ot5++23j4+XKlWPWrFkmx+jVqxdvvPEGU6dOxd3dneLFi/P++++TlJSUYZwajQY3Nzfc3d2pU6cOb7/9NgcOHCAyMhIAS0tLAP78809u3LiRpRbun3/+mcTERBYtWkSFChXo3LkzgwYNYvr06enus2LFCl555RVGjBiBn58fQUFBTJkyhW+//ZaoqCgAli5dyhtvvMGAAQMoU6YMLVq0YNSoUXz11VcoOdlChCTFQuRZpUrBxx8bbn/0EcTGZv857tyBVasMt2WAnRAiT7C3T/+nfXvTsiVKpF/29ddNy/r4pF3uOfXp04fFixcb7y9atCjNpHLy5Mn8+OOPzJs3j5MnTzJ06FC6d+/Ojh07AEPS7OXlxcqVKzl16hRjx45l9OjR/JqyvOgj27Zt4+LFi2zbto0lS5bwww8/8MMPP2Q63jt37rBmzRq0Wi1ardZ47s8//5w1a9awdu1arK2tjeU1Gk2Gx9+7dy/169c3JtYAzZo14+zZs4SHh6e5T0JCgsk5AGxsbIiPj+fw4cMZlrlx4wZXc3jJV0mKhcjDRowAb2+4ds3QcpzdfvgBkpIMg+uqVMn+4wshREHVvXt3du3axdWrV7l69Sq7d++me/fuJmUSEhL44osvWLRoEc2aNaNMmTL06tWL7t27M//R13QWFhZMmDCBatWqUbp0abp160bv3r1TJcVFixblm2++wd/fn5YtW9KiRQu2bt2aYYwRERHY29tjZ2eHq6sr27Zt4/3338fOzg6AWbNmMWnSJE6cOEGDBg2YM2eOcd9y5crhmMH8nGFhYbi6uppsS7kfFhaW5j7NmjVjz549/Pbbb+h0OkJDQ5k4cSIAt27dMpZZvXo1W7duRa/Xc+7cOaZNm2ZSJqeY5+jRhRAvxNYWpk2Djh3hq6+gd2/Iru5qer3pADshhMgToqPTf+xRC6fRnTvplzV7qt3vypXnDiktLi4utGjRgh9++AFFUWjRogXOzs4mZS5cuEBsbCxNmjQx2Z6YmGjSzeLbb79l0aJFXLt2jbi4OBITE6lUqZLJPhUqVDC28AK4u7vz33//ZRhjkSJFOHLkCElJSWzcuJGff/6ZSZMmGR8fOnQoQ4cOTXPfM2fOZHjs59G0aVOmTJnCsGHDGDBgAFZWVnz66afs3LkTs0evV9++fbl48SItW7YkKSkJBwcHBg8ezPjx441lcookxULkcR06QFCQYUDc8OGwYkX2HPfvv+HiRUM/4s6ds+eYQgjxwh61YqpaNpP69OnDwIEDAUNi+7ToRwn+n3/+iaenp8ljVlZWAPzyyy989NFHTJs2jdq1a1OkSBG+/vpr9u/fb1LewsLC5L5Go0Gv12cYn5mZGX5+fgCUL1+eixcv8u6777J06dIsPMu0ubm5cfv2bZNtKffd3NzS3W/o0KH06dOHmJgYihcvzpUrVxg1ahRlypQBDM/rq6++4osvviAsLAwXFxdji3hKmZwi3SeEyOM0GsMUbWZm8OuvhoF32SFlgF337i/UrU4IIQqt5s2bk5iYSFJSEs2aNUv1eEBAAFZWVly7dg0/Pz+TH29vbwB2795NnTp1eO+996hcuTJ+fn5cvHgxR+IdOXIkK1as4MiRIy98rNq1a/PPP/+YDPYLCQmhXLlyFC1aNMN9NRoNHh4e2NjYsHz5cry9vanyVB8+rVaLp6cnlpaWLF++nNq1a+Pi4vLCcWdEkmIh8oFXXnncxWHwYHhqSscsCwuDtWsNt6XrhBBCPB+tVsvp06c5deqUSdeGFEWKFOGjjz5i6NChLFmyhIsXL3LkyBHmzJnDkiVLAChbtiyHDh1i8+bNnDt3jk8//ZSDBw/mSLze3t60/X97dx4VVd3/Afw9LMMimwsIiAtoMkgoJWJoipZbeHxQnp6sPIZL5ZogHQkXQFxCzcytxPJRy6NHbcF+j6aFJpjLo0ZQioiFpD4CUWqAKNvw/f0xMTkww6azwH2/zplzmDvfO/dzP370fLzc+/1OmIC4JqwMpVAokJycrPPzl19+GXK5HNOnT0dWVhb27duHDRs2ICoqSj0mOTkZCoVCY7+1a9ciKysLWVlZWL58OVatWoWNGzeq8/fHH38gKSkJly9fRmZmJiIiIvDpp59i/fr1LTvpZmBTTNRKLF8OtG+vWszjo48e7rt27FA11k89BfTr92jiIyKSIgcHBzg4OOj8fPny5YiNjUViYiJ8fHwwZswYHDp0CJ6engCAGTNmICwsDBMnTsTAgQNx69YtzJ49W2/xzp8/H4cOHcK5c+caHJeTk4Pi4mKdnzs6OuKbb75BXl4e+vfvjzfffBNxcXF4/fXX1WOKi4uRk5Ojsd+RI0cQEhKCwMBAHDp0CF9++SXGjx+vMebjjz9GQEAABg8ejKysLKSmpiIwMLD5J9tcwsg2b94sunfvLqysrERgYKA4e/aszrGVlZUiISFBeHl5CSsrK9G3b19x+PBhjTHV1dViyZIlokePHsLa2lp4eXmJZcuWiZqaGo1xly5dEuPGjRMODg7C1tZWBAQEiGvXrjU57uLiYgFAFBcXN++EW6iyslIcOHBAVFZWGuR4rYmUcrN5sxCAEB06CHHrVsNjdeVFqRTC01P1PTt26C9WUyalmmkO5kU75kW3h8nN/fv3xaVLl8T9+/f1EJlxKZVKcefOHaFUKo0disnRR24aqqXm9GtGvVK8b98+REVFIT4+Hj/88AP69euH0aNHo0jH06RLlizB1q1bsWnTJly6dAkzZ87EhAkTkJGRoR6zevVqbNmyBZs3b0Z2djZWr16NNWvWaEwzkpubi6effhoKhQKpqan46aefEBsbW29ePCJTM2MG4OcH3L4NxMe37DtSUoC8PMDRUTWrBRERERn59ol169bhtddew9SpU9GnTx8kJSXB1tYW27dv1zp+165dWLRoEUJCQuDl5YVZs2YhJCREPX8dAJw+fRqhoaEYO3YsevTogeeffx6jRo3S+DXB4sWLERISgjVr1uCJJ55Az5498Y9//AMuLi56P2eih2FhAdQudPTBB0Ajs/FoVfuA3SuvqKZ8IyIiIiNOyVZZWYn09HQsXLhQvc3MzAwjRozAmTNntO6ja5WTkydPqt8PGjQIH374Ia5cuYLevXvjxx9/xMmTJ9XLDtbU1ODQoUOIjo7G6NGjkZGRAU9PTyxcuLDePS11j11RUaF+X7tEYlVVVaPLLD4KtccwxLFaG6nl5umngQkTzJGcbIZ582rw9ddKyGT1x2nLS34+8H//ZwFAhmnTqiCRlNUjtZppKuZFO+ZFt4fJTVVVFYQQqKmpaXRqsdZG/LUcce350d/0kZuamhoIIVBVVVXvgcfm1KZMCD0vJK1Dfn4+unTpgtOnTyMoKEi9PTo6GmlpafXm5wNUTzr++OOPOHDgAHr27Iljx44hNDQUSqVS3bDW1NRg0aJFWLNmDczNzaFUKrFy5Up1811YWAg3NzfY2tpixYoVGD58OI4cOYJFixbh+PHjCA4O1hrv0qVLkZCQUG/7nj17YMvLbWRgv/1mgzfeeBaVleaIjj6HQYOatsrP/v29sWePD3x8biEx8WTjOxAR6YmFhQVcXV3RtWtXjaWCiZqrsrISN27cQGFhIarrTM907949vPzyyyguLm7wgUiglS3esWHDBrz22mtQKBSQyWTo2bMnpk6dqnG7xf79+7F7927s2bMHvr6+yMzMRGRkJNzd3REeHq7+X0loaKh6FRd/f3+cPn0aSUlJOpvihQsXakwzUlJSgq5du2LUqFGNJvlRqKqqQkpKCkaOHFlvAm+pk2purl8HVq4E9u0bgMWLq2Fjo/l53bwolcC8eaq/8gsWOCIkJMQIUZsGqdZMY5gX7ZgX3R4mN+Xl5bhx4wbs7Oza3DM9QgiUlpbC3t4eMm2/ypMwfeSmvLwcNjY2GDp0aL1aqv3NflMYrSnu1KkTzM3Nta6GomslFGdnZxw4cADl5eW4desW3N3dERMTo7HCyYIFCxATE4MX/1qiy8/PD9euXUNiYiLCw8PRqVMnWFhYoE+fPhrf7ePjo3EbRl1WVlbq1WceZGlpadB/JA19vNZEarlZtAj45BPg2jUZNmywRGys9nG1eUlJUTXS7dsDL75oAQmlSiep1UxTMS/aMS+6tSQ3SqUSMpkMZmZmel++19BqL8DVnh/9TR+5MTMzg0wm01qHzalLo/1JyeVy9O/fX710H6BK1LFjxzRup9DG2toaXbp0QXV1NT7//HOEhoaqP7t37169JJubm6v/EORyOQYMGFBv3rwrV66ge/fuD3taRAZjawu8847q58REVcPbkNoH7MLDUe+qMhERkdQZ9faJqKgohIeHIyAgAIGBgVi/fj3KysowdepUAMArr7yCLl26IDExEQBw9uxZ3Lx5E/7+/rh58yaWLl2KmpoaREdHq79z3LhxWLlyJbp16wZfX19kZGRg3bp1mDZtmnrMggULMHHiRAwdOlR9T/F//vMfpD6q9XOJDOSFF1SzUJw4AURHA3v3ah/3v/8BBw+qfn5gXnUiIiL6i1Gb4okTJ+L3339HXFwcCgsL4e/vjyNHjqBz584AgOvXr2tc9S0vL8eSJUtw9epV2NnZISQkBLt27YKTk5N6zKZNmxAbG4vZs2ejqKgI7u7umDFjhsaShhMmTEBSUhISExMxb948eHt74/PPP8fTTz9tsHMnehRkMtUUbf37A/v2AbNnA0OH1h+3bRtQU6P6zMfH8HESERGZOqM/aDd37lzMnTtX62d1r9wGBwfj0qVLDX6fvb091q9f3+ga2dOmTdO4ekzUWvn7q67+JiUB8+YB6enAgzPSVFermmJAtfgHEZFJq6xU/cNlKBYWQBue/WLKlCn4888/ceDAAWOHYvKM3hQT0cNbvlx168SPP6oa4Aeb38OHZbh5E+jYEfjnP40XIxFRoyorgXPngLt3DXdMOzsgMFBvjfHKlStx6NAhZGZmQi6X488//2x0n7y8PCxevBipqam4ffs2OnXqhP79+2P16tVQKBT49ddf4enpiYyMDPj7+z9UfKmpqRg+fDgA1cNv9vb28PLywsiRIzF//ny4ubk91Pe3JnwkkqgN6NQJWLZM9fPixcCdO39/tm2b6q/5lCmAlglUiIhMR3W1qiGWywF7e/2/5HLV8R7iyvSwYcOwc+dOnZ9XVlbiX//6F2bNmtWk76uqqsLIkSNRXFyML774Ajk5Odi3bx/8/Pya1FC3VE5ODvLz83H+/Hm89dZbOHr0KB5//HFcaMnSqa0Um2KiNmLWLMDXF7h1C4iNBdLSZDh40BOHD6vmgeQDdkTUalhZAdbW+n8Z4EpBQkIC5s+fDz8/vyaNz8rKQm5uLj744AM89dRT6N69OwYPHowVK1bgqaeeAgB4enoCAJ544gnIZDIMGzYMgGqau6ioKDg5OaFjx46Ijo5GU9doc3FxgaurK3r37o0XX3wRp06dgrOzc71mftu2bfDx8YG1tTUUCgU++OAD9WeDBg3CW2+9pTH+999/h6WlJU6cONGkOIyJTTFRG2FhoXroDgDefx8YOdIC27b1BSCDlRVw8aJRwyMioiZwdnaGmZkZPvvsMyiVSq1jzp07BwA4evQoCgoK8MUXXwAA3n33XezcuRPbt2/HyZMncfv2bSQnJ7coDhsbG8ycOROnTp1CUVERAGD37t2Ii4vDypUrkZ2djbfffhuxsbH4+OOPAQCTJk3C3r17NRrxffv2wd3dHUOGDGlRHIbEppioDSku1r69ogJ4/nngr383iYiohd5++23Y2dmpX9999x1mzpypse16YxPHN6BLly7YuHEj4uLi0L59ezzzzDNYvnw5rl69qh7j7OwMAOjYsSNcXV3RoUMHAMD69euxcOFChIWFwcfHB0lJSXB0dGxxLAqFAgDw66+/AgDi4+Px7rvvIiwsDJ6enggLC8P8+fOx9a+J8F944QXk5+drLIa2Z88evPTSS61iZT82xURthFIJREQ0PCYyUjWOiIhaZubMmcjMzFS/AgICsGzZMo1t7u7uD3WMOXPmoLCwELt370ZQUBA+/fRT+Pr6IiUlRec+xcXFKCgowMCBA9XbLCwsEBAQ0OI4aq/4ymQylJWVITc3F9OnT9f4D8CKFSuQm5sLQNWsjxo1Crt37wagemDwzJkzmDRpUotjMCTOPkHURnz3nWqRDl2EAG7cUI376/YzIiJqpg4dOqivzAKq2wxcXFzQq1cv9bbaVXQfhr29PcaNG4dx48ZhxYoVGD16NFasWIGRI0c+9Hc3VXZ2NgCgR48euPvXjCAfffSRRuMNqFYOrjVp0iTMmzcPmzZtwp49e+Dn59fk+6mNjVeKidqIgoJHO46IiEyDTCaDQqFAWVkZAED+1/RxD95z7OjoCDc3N5w9e1a9rbq6Gunp6S065v379/Hhhx9i6NChcHZ2RufOneHu7o6rV6+iV69eGq/aB/8AIDQ0FOXl5Thy5Aj27NnTaq4SA7xSTNRmNHUqSQlNOUlErVVFhcke5+7du+qrpgCwd+9eAEBhYaF6W8eOHdU/X79+Hbdv38b169ehVCqRmZkJAOjVqxfs7OzqfX9mZibi4+MxefJk9OnTB3K5HGlpadi+fbt6ZgcXFxfY2NjgyJEj8PDwgLW1NRwdHREREYFVq1bhscceg0KhwLp165o8jVtRURHKy8tRWlqK9PR0rFmzBn/88Yf6IT5ANZPGvHnz4OjoiDFjxqCiogLff/897ty5g6ioKABAu3btMH78eMTGxiI7OxsvvfRS0xJrAtgUE7URQ4YAHh7AzZuqWyXqkslUn7eCB4CJSKosLFSLady9q1rIwxDs7FTHbaK1a9ciISGhwTG5ubnqWyzi4uLUszMAqmnUAOD48ePqqdQe5OHhgR49eiAhIQG//vorZDKZ+v38+fMBqO4V3rhxI5YtW4a4uDgMGTIEqampePPNN1FQUIDw8HCYmZlh2rRpmDBhAop1PYX9AG9vb8hkMtjZ2cHLywujRo1CVFQUXF1d1WNeffVV2Nra4p133sGCBQvQrl07+Pn5ITIyUuO7Jk2ahJCQEAwdOhTdunVr9NimQiaaOoEdaSgpKYGjoyOKi4vh4OCg9+NVVVXhq6++QkhICCwtLfV+vNaEufnbF1+oZpkANBvj2od+P/sMCAszfFymhjWjHfOiHfOi28Pkpry8HHl5efD09IS1tfXfH7SBZZ5rampQUlICBwcHmJnxTtUH6SM3OmsJzevXeKWYqA0JC1M1vhERmg/deXgA69ezISaiVkAu19uSy0QNYVNM1MaEhQGhocDx49U4fDgTzz3nj+HDLfDAw8FERERUB5tiojbI3BwIDhYoK7uJ4OB+bIiJiIgawRtdiIiIiEjy2BQTERGR0fB5f3pYj6qG2BQTERGRwdXOVnHv3j0jR0KtXW0NPezsMLynmIiIiAzO3NwcTk5OKCoqAgDY2tpCVjt/ZCtXU1ODyspKlJeXc0q2Oh5lboQQuHfvHoqKiuDk5KSx3HRLsCkmIiIio6hdGKK2MW4rhBC4f/8+bGxs2kyj/6joIzdOTk4ai4y0FJtiIiIiMgqZTAY3Nze4uLigqqrK2OE8MlVVVThx4gSGDh3KBV/qeNS5sbS0fOgrxLXYFBMREZFRmZubP7LGxhSYm5ujuroa1tbWbIrrMOXc8EYXIiIiIpI8NsVEREREJHlsiomIiIhI8nhPcQvVThRdUlJikONVVVXh3r17KCkpMbl7cIyNudGOedGNudGOedGOedGNudGOedHN0Lmp7dOassAHm+IWKi0tBQB07drVyJEQERERUUNKS0vh6OjY4BiZ4PqKLVJTU4P8/HzY29sbZA7CkpISdO3aFTdu3ICDg4Pej9eaMDfaMS+6MTfaMS/aMS+6MTfaMS+6GTo3QgiUlpbC3d290cVCeKW4hczMzODh4WHw4zo4OPAvmA7MjXbMi27MjXbMi3bMi27MjXbMi26GzE1jV4hr8UE7IiIiIpI8NsVEREREJHlsilsJKysrxMfHw8rKytihmBzmRjvmRTfmRjvmRTvmRTfmRjvmRTdTzg0ftCMiIiIiyeOVYiIiIiKSPDbFRERERCR5bIqJiIiISPLYFBMRERGR5LEpNhEnTpzAuHHj4O7uDplMhgMHDjS6T2pqKp588klYWVmhV69e2Llzp97jNLTm5iU1NRUymazeq7Cw0DABG0hiYiIGDBgAe3t7uLi4YPz48cjJyWl0v08//RQKhQLW1tbw8/PDV199ZYBoDasludm5c2e9mrG2tjZQxIaxZcsW9O3bVz1hflBQEA4fPtzgPlKoF6D5uZFCvWizatUqyGQyREZGNjhOKnVTqyl5kUrNLF26tN55KhSKBvcxpXphU2wiysrK0K9fP7z//vtNGp+Xl4exY8di+PDhyMzMRGRkJF599VV8/fXXeo7UsJqbl1o5OTkoKChQv1xcXPQUoXGkpaVhzpw5+O9//4uUlBRUVVVh1KhRKCsr07nP6dOn8dJLL2H69OnIyMjA+PHjMX78eFy8eNGAketfS3IDqFZXerBmrl27ZqCIDcPDwwOrVq1Ceno6vv/+ezzzzDMIDQ1FVlaW1vFSqReg+bkB2n691HX+/Hls3boVffv2bXCclOoGaHpeAOnUjK+vr8Z5njx5UudYk6sXQSYHgEhOTm5wTHR0tPD19dXYNnHiRDF69Gg9RmZcTcnL8ePHBQBx584dg8RkKoqKigQAkZaWpnPMCy+8IMaOHauxbeDAgWLGjBn6Ds+ompKbHTt2CEdHR8MFZSLat28vtm3bpvUzqdZLrYZyI7V6KS0tFY899phISUkRwcHBIiIiQudYKdVNc/IilZqJj48X/fr1a/J4U6sXXilupc6cOYMRI0ZobBs9ejTOnDljpIhMi7+/P9zc3DBy5EicOnXK2OHoXXFxMQCgQ4cOOsdItWaakhsAuHv3Lrp3746uXbs2epWwtVMqldi7dy/KysoQFBSkdYxU66UpuQGkVS9z5szB2LFj69WDNlKqm+bkBZBOzfz8889wd3eHl5cXJk2ahOvXr+sca2r1YmGUo9JDKywsROfOnTW2de7cGSUlJbh//z5sbGyMFJlxubm5ISkpCQEBAaioqMC2bdswbNgwnD17Fk8++aSxw9OLmpoaREZGYvDgwXj88cd1jtNVM23tfusHNTU33t7e2L59O/r27Yvi4mKsXbsWgwYNQlZWFjw8PAwYsX5duHABQUFBKC8vh52dHZKTk9GnTx+tY6VWL83JjVTqBQD27t2LH374AefPn2/SeKnUTXPzIpWaGThwIHbu3Alvb28UFBQgISEBQ4YMwcWLF2Fvb19vvKnVC5tialO8vb3h7e2tfj9o0CDk5ubivffew65du4wYmf7MmTMHFy9ebPC+Lalqam6CgoI0rgoOGjQIPj4+2Lp1K5YvX67vMA3G29sbmZmZKC4uxmeffYbw8HCkpaXpbP6kpDm5kUq93LhxAxEREUhJSWmTD4W1VEvyIpWaee6559Q/9+3bFwMHDkT37t2xf/9+TJ8+3YiRNQ2b4lbK1dUVv/32m8a23377DQ4ODpK9SqxLYGBgm20Y586di4MHD+LEiRONXm3QVTOurq76DNFompObuiwtLfHEE0/gl19+0VN0xiGXy9GrVy8AQP/+/XH+/Hls2LABW7durTdWavXSnNzU1VbrJT09HUVFRRq/ZVMqlThx4gQ2b96MiooKmJuba+wjhbppSV7qaqs1U5eTkxN69+6t8zxNrV54T3ErFRQUhGPHjmlsS0lJafAeOKnKzMyEm5ubscN4pIQQmDt3LpKTk/Htt9/C09Oz0X2kUjMtyU1dSqUSFy5caHN1U1dNTQ0qKiq0fiaVetGlodzU1Vbr5dlnn8WFCxeQmZmpfgUEBGDSpEnIzMzU2vhJoW5akpe62mrN1HX37l3k5ubqPE+TqxejPN5H9ZSWloqMjAyRkZEhAIh169aJjIwMce3aNSGEEDExMWLy5Mnq8VevXhW2trZiwYIFIjs7W7z//vvC3NxcHDlyxFinoBfNzct7770nDhw4IH7++Wdx4cIFERERIczMzMTRo0eNdQp6MWvWLOHo6ChSU1NFQUGB+nXv3j31mMmTJ4uYmBj1+1OnTgkLCwuxdu1akZ2dLeLj44WlpaW4cOGCMU5Bb1qSm4SEBPH111+L3NxckZ6eLl588UVhbW0tsrKyjHEKehETEyPS0tJEXl6e+Omnn0RMTIyQyWTim2++EUJIt16EaH5upFAvutSdZUHKdfOgxvIilZp58803RWpqqsjLyxOnTp0SI0aMEJ06dRJFRUVCCNOvFzbFJqJ2KrG6r/DwcCGEEOHh4SI4OLjePv7+/kIulwsvLy+xY8cOg8etb83Ny+rVq0XPnj2FtbW16NChgxg2bJj49ttvjRO8HmnLCQCNGggODlbnqdb+/ftF7969hVwuF76+vuLQoUOGDdwAWpKbyMhI0a1bNyGXy0Xnzp1FSEiI+OGHHwwfvB5NmzZNdO/eXcjlcuHs7CyeffZZddMnhHTrRYjm50YK9aJL3eZPynXzoMbyIpWamThxonBzcxNyuVx06dJFTJw4Ufzyyy/qz029XmRCCGG469JERERERKaH9xQTERERkeSxKSYiIiIiyWNTTERERESSx6aYiIiIiCSPTTERERERSR6bYiIiIiKSPDbFRERERCR5bIqJiIiISPLYFBMRUT3Dhg1DZGRkg2N69OiB9evXGyQeIiJ9Y1NMRNRGTZkyBTKZrN7rl19+MXZoREQmx8LYARARkf6MGTMGO3bs0Njm7OxspGiIiEwXrxQTEbVhVlZWcHV11XiZm5sjLS0NgYGBsLKygpubG2JiYlBdXa3ze4qKijBu3DjY2NjA09MTu3fvNuBZEBHpH68UExFJzM2bNxESEoIpU6bgk08+weXLl/Haa6/B2toaS5cu1brPlClTkJ+fj+PHj8PS0hLz5s1DUVGRYQMnItIjNsVERG3YwYMHYWdnp37/3HPPoXfv3ujatSs2b94MmUwGhUKB/Px8vPXWW4iLi4OZmeYvEa9cuYLDhw/j3LlzGDBgAADg3//+N3x8fAx6LkRE+sSmmIioDRs+fDi2bNmift+uXTvMmTMHQUFBkMlk6u2DBw/G3bt38b///Q/dunXT+I7s7GxYWFigf//+6m0KhQJOTk56j5+IyFDYFBMRtWHt2rVDr169jB0GEZHJ44N2REQS4+PjgzNnzkAIod526tQp2Nvbw8PDo954hUKB6upqpKenq7fl5OTgzz//NES4REQGwaaYiEhiZs+ejRs3buCNN97A5cuX8eWXXyI+Ph5RUVH17icGAG9vb4wZMwYzZszA2bNnkZ6ejldffRU2NjZGiJ6ISD/YFBMRSUyXLl3w1Vdf4dy5c+jXrx9mzpyJ6dOnY8mSJTr32bFjB9zd3REcHIywsDC8/vrrcHFxMWDURET6JRMP/v6MiIiIiEiCeKWYiIiIiCSPTTERERERSR6bYiIiIiKSPDbFRERERCR5bIqJiIiISPLYFBMRERGR5LEpJiIiIiLJY1NMRERERJLHppiIiIiIJI9NMRERERFJHptiIiIiIpK8/wf7Pxr2TdA+5wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Scores for each fold: [0.99295281 0.98555108 0.99626666 0.99311907 0.99219813]\n",
      "Mean R²: 0.99\n",
      "Standard Deviation: 0.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit, cross_val_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fungsi untuk menghitung skor cross-validation dengan TimeSeriesSplit\n",
    "def time_series_cross_validate_and_visualize_r2(model, X, y, n_splits=5):\n",
    "    # TimeSeriesSplit untuk data terkait waktu\n",
    "    tscv = TimeSeriesSplit(n_splits=n_splits)\n",
    "\n",
    "    # Hitung skor cross-validation dengan metrik R²\n",
    "    scores = cross_val_score(model, X, y, scoring='r2', cv=tscv)\n",
    "\n",
    "    # Rata-rata dan standar deviasi\n",
    "    mean_score = np.mean(scores)\n",
    "    std_score = np.std(scores)\n",
    "\n",
    "    # Visualisasi hasil cross-validation\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    plt.plot(range(1, n_splits + 1), scores, marker='o', linestyle='-', color='b', label='Fold Score')\n",
    "    plt.axhline(y=mean_score, color='r', linestyle='--', label=f'Mean R²: {mean_score:.2f}')\n",
    "    plt.fill_between(range(1, n_splits + 1), mean_score - std_score, mean_score + std_score, color='r', alpha=0.2, label='±1 Std Dev')\n",
    "    plt.title('Time-Based Cross-Validation Scores (R²)')\n",
    "    plt.xlabel('Fold')\n",
    "    plt.ylabel('R² Score')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "    # Cetak hasil skor\n",
    "    print(f'R² Scores for each fold: {scores}')\n",
    "    print(f'Mean R²: {mean_score:.2f}')\n",
    "    print(f'Standard Deviation: {std_score:.2f}')\n",
    "\n",
    "# Contoh penggunaan\n",
    "time_series_cross_validate_and_visualize_r2(model, X, y, n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "200:\tlearn: 0.5107908\ttotal: 4.03s\tremaining: 6.06s\n",
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      "200:\tlearn: 0.4678309\ttotal: 6.53s\tremaining: 9.82s\n",
      "400:\tlearn: 0.3400938\ttotal: 13.5s\tremaining: 3.42s\n",
      "502:\tlearn: 0.3059784\ttotal: 16.9s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Scores for each fold: [0.98420943 0.98122294 0.99538229 0.99392864 0.98656069]\n",
      "Mean R²: 0.99\n",
      "Standard Deviation: 0.01\n"
     ]
    }
   ],
   "source": [
    "time_series_cross_validate_and_visualize_r2(final_model, X, y, n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>domisili</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>join_date</th>\n",
       "      <th>resign_date</th>\n",
       "      <th>marriage_stat</th>\n",
       "      <th>dependant</th>\n",
       "      <th>education</th>\n",
       "      <th>absent_90D</th>\n",
       "      <th>...</th>\n",
       "      <th>total_income_work</th>\n",
       "      <th>income_dependant_ratio</th>\n",
       "      <th>work_efficiency</th>\n",
       "      <th>active_work_category</th>\n",
       "      <th>work_stability_score</th>\n",
       "      <th>position_score</th>\n",
       "      <th>job_income_position_score</th>\n",
       "      <th>education_score</th>\n",
       "      <th>education_income_ratio</th>\n",
       "      <th>weighted_satisfaction_performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EM6407</td>\n",
       "      <td>Kota Jakarta Selatan</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1981-03-05</td>\n",
       "      <td>2022-03-13</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>D3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.169413e+08</td>\n",
       "      <td>1.719725e+06</td>\n",
       "      <td>1.22500</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.719725e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>1.719725e+06</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EM6881</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1974-04-26</td>\n",
       "      <td>2022-04-11</td>\n",
       "      <td>2023-05-31</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>D3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.369110e+08</td>\n",
       "      <td>1.053162e+07</td>\n",
       "      <td>1.17375</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>4</td>\n",
       "      <td>2.632904e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>2.632904e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EM9588</td>\n",
       "      <td>Kota Depok</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1980-01-08</td>\n",
       "      <td>2022-02-22</td>\n",
       "      <td>2023-08-30</td>\n",
       "      <td>Married</td>\n",
       "      <td>3</td>\n",
       "      <td>D1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.408170e+08</td>\n",
       "      <td>1.955791e+06</td>\n",
       "      <td>1.18625</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.955791e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>3.911582e+06</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EM6817</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Perempuan</td>\n",
       "      <td>1985-06-15</td>\n",
       "      <td>2021-09-04</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.969525e+07</td>\n",
       "      <td>8.269843e+05</td>\n",
       "      <td>1.13125</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>1</td>\n",
       "      <td>2.480953e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.480953e+06</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EM0933</td>\n",
       "      <td>Kota Jakarta Timur</td>\n",
       "      <td>Laki-laki</td>\n",
       "      <td>1981-10-31</td>\n",
       "      <td>2022-03-20</td>\n",
       "      <td>2024-09-08</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>SLTA</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.918537e+08</td>\n",
       "      <td>4.864228e+06</td>\n",
       "      <td>1.14125</td>\n",
       "      <td>Mid-term</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.432114e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>9.728456e+06</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee_id              domisili jenis_kelamin date_of_birth   join_date  \\\n",
       "0      EM6407  Kota Jakarta Selatan     Laki-laki    1981-03-05  2022-03-13   \n",
       "1      EM6881             Tangerang     Laki-laki    1974-04-26  2022-04-11   \n",
       "2      EM9588            Kota Depok     Perempuan    1980-01-08  2022-02-22   \n",
       "3      EM6817    Kota Jakarta Timur     Perempuan    1985-06-15  2021-09-04   \n",
       "4      EM0933    Kota Jakarta Timur     Laki-laki    1981-10-31  2022-03-20   \n",
       "\n",
       "  resign_date marriage_stat  dependant education  absent_90D  ...  \\\n",
       "0  2023-08-08       Married          3        D3         3.0  ...   \n",
       "1  2023-05-31       Married          0        D3         2.0  ...   \n",
       "2  2023-08-30       Married          3        D1         4.0  ...   \n",
       "3  2023-01-13       Married          2      SLTA        10.0  ...   \n",
       "4  2024-09-08       Married          1      SLTA         7.0  ...   \n",
       "\n",
       "   total_income_work income_dependant_ratio work_efficiency  \\\n",
       "0       1.169413e+08           1.719725e+06         1.22500   \n",
       "1       1.369110e+08           1.053162e+07         1.17375   \n",
       "2       1.408170e+08           1.955791e+06         1.18625   \n",
       "3       3.969525e+07           8.269843e+05         1.13125   \n",
       "4       2.918537e+08           4.864228e+06         1.14125   \n",
       "\n",
       "   active_work_category  work_stability_score position_score  \\\n",
       "0              Mid-term              4.250000              4   \n",
       "1              Mid-term              4.333333              4   \n",
       "2              Mid-term              3.600000              4   \n",
       "3              Mid-term              1.454545              1   \n",
       "4              Mid-term              3.750000              4   \n",
       "\n",
       "  job_income_position_score  education_score  education_income_ratio  \\\n",
       "0              1.719725e+06                4            1.719725e+06   \n",
       "1              2.632904e+06                4            2.632904e+06   \n",
       "2              1.955791e+06                2            3.911582e+06   \n",
       "3              2.480953e+06                1            2.480953e+06   \n",
       "4              2.432114e+06                1            9.728456e+06   \n",
       "\n",
       "   weighted_satisfaction_performance  \n",
       "0                                3.4  \n",
       "1                                4.0  \n",
       "2                                3.6  \n",
       "3                                1.0  \n",
       "4                                4.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('D:/Tugas Akhir/Codingan/Development/App/data/df_test_YESUSFIX.csv')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Score: 0.9979140808292275\n",
      "Mean Absolute Error (MAE): 0.24215619984624856\n",
      "Mean Squared Error (MSE): 0.09893091309450355\n",
      "Root Mean Squared Error (RMSE): 0.31453284899117223\n",
      "Mean Absolute Percentage Error (MAPE): 0.014547198355724301\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import numpy as np\n",
    "\n",
    "# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
    "X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "\n",
    "# Lakukan prediksi menggunakan model final\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Tambahkan prediksi ke DataFrame\n",
    "X_test['predicted_active_work'] = y_pred\n",
    "\n",
    "# Hitung metrik evaluasi\n",
    "r2 = r2_score(df_test['active_work_months'], y_pred)\n",
    "mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
    "mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mape = mean_absolute_percentage_error(df_test['active_work_months'], y_pred)\n",
    "\n",
    "# Cetak hasil\n",
    "print(\"R² Score:\", r2)\n",
    "print(\"Mean Absolute Error (MAE):\", mae)\n",
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
    "print(\"Mean Absolute Percentage Error (MAPE):\", mape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R² Score: 0.9977667403012139\n",
      "Mean Absolute Error (MAE): 0.2474148687203165\n",
      "Mean Squared Error (MSE): 0.10591897532455063\n",
      "Root Mean Squared Error (RMSE): 0.3254519554781483\n",
      "Mean Absolute Percentage Error (MAPE): 0.014723133752014913\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import numpy as np\n",
    "\n",
    "# Menghapus kolom yang tidak diperlukan untuk prediksi\n",
    "X_test = df_test.drop(columns=['active_work_months','churn_status', 'employee_id', 'date_of_birth', 'join_date', 'resign_date'])\n",
    "\n",
    "# Lakukan prediksi menggunakan model final\n",
    "y_pred = final_model.predict(X_test)\n",
    "\n",
    "# Tambahkan prediksi ke DataFrame\n",
    "X_test['predicted_active_work'] = y_pred\n",
    "\n",
    "# Hitung metrik evaluasi\n",
    "r2 = r2_score(df_test['active_work_months'], y_pred)\n",
    "mae = mean_absolute_error(df_test['active_work_months'], y_pred)\n",
    "mse = mean_squared_error(df_test['active_work_months'], y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "mape = mean_absolute_percentage_error(df_test['active_work_months'], y_pred)\n",
    "\n",
    "# Cetak hasil\n",
    "print(\"R² Score:\", r2)\n",
    "print(\"Mean Absolute Error (MAE):\", mae)\n",
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
    "print(\"Mean Absolute Percentage Error (MAPE):\", mape)"
   ]
  }
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