DeepLearning V2[Merged Class].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48a4b739",
   "metadata": {},
   "source": [
    "# 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d9a99d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, multilabel_confusion_matrix\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6206691b",
   "metadata": {},
   "source": [
    "# 2. Check the coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61b22970",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bench = pd.read_csv('./labeling/benchpress_coords_merged.csv')\n",
    "df_dead = pd.read_csv('./labeling/deadlift_coords_merged.csv')\n",
    "df_squat = pd.read_csv('./labeling/squat_coords_merged.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "098732e0",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>class</th>\n",
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       "      <td>0.290651</td>\n",
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       "      <td>0.494922</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.325614</td>\n",
       "      <td>0.295111</td>\n",
       "      <td>0.539178</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.326345</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.764271</td>\n",
       "      <td>0.978585</td>\n",
       "      <td>0.978824</td>\n",
       "      <td>0.608172</td>\n",
       "      <td>-0.101540</td>\n",
       "      <td>0.941800</td>\n",
       "      <td>0.723257</td>\n",
       "      <td>0.737576</td>\n",
       "      <td>-0.921502</td>\n",
       "      <td>0.993139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b_correct</td>\n",
       "      <td>0.342315</td>\n",
       "      <td>0.286979</td>\n",
       "      <td>0.466467</td>\n",
       "      <td>0.999992</td>\n",
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       "      <td>0.981234</td>\n",
       "      <td>0.604804</td>\n",
       "      <td>-0.016468</td>\n",
       "      <td>0.940290</td>\n",
       "      <td>0.719281</td>\n",
       "      <td>0.736312</td>\n",
       "      <td>-0.785139</td>\n",
       "      <td>0.994317</td>\n",
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       "      <th>4</th>\n",
       "      <td>b_correct</td>\n",
       "      <td>0.337070</td>\n",
       "      <td>0.292075</td>\n",
       "      <td>0.581475</td>\n",
       "      <td>0.999998</td>\n",
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       "      <td>0.999999</td>\n",
       "      <td>0.331256</td>\n",
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       "       class        x1        y1        z1        v1        x2        y2  \\\n",
       "0  b_correct  0.342405  0.293187  0.615661  0.999998  0.333918  0.293168   \n",
       "1  b_correct  0.347209  0.290651  0.494753  0.999993  0.336074  0.285710   \n",
       "2  b_correct  0.338909  0.297234  0.494922  0.999994  0.325614  0.295111   \n",
       "3  b_correct  0.342315  0.286979  0.466467  0.999992  0.325819  0.285868   \n",
       "4  b_correct  0.337070  0.292075  0.581475  0.999998  0.329630  0.290616   \n",
       "\n",
       "         z2        v2        x3  ...       z31       v31       x32       y32  \\\n",
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       "4  0.630095  0.999999  0.331256  ... -0.825383  0.985708  0.987015  0.609234   \n",
       "\n",
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       "\n",
       "[5 rows x 133 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_bench.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ca142bf",
   "metadata": {},
   "outputs": [
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       "      <td>0.289000</td>\n",
       "      <td>0.876913</td>\n",
       "      <td>0.977582</td>\n",
       "      <td>0.599490</td>\n",
       "      <td>0.280128</td>\n",
       "      <td>0.851040</td>\n",
       "      <td>0.982493</td>\n",
       "      <td>0.597362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.369821</td>\n",
       "      <td>0.913276</td>\n",
       "      <td>0.565828</td>\n",
       "      <td>0.635022</td>\n",
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       "      <td>0.331195</td>\n",
       "      <td>0.832618</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d_correct</td>\n",
       "      <td>0.704687</td>\n",
       "      <td>0.148774</td>\n",
       "      <td>0.440767</td>\n",
       "      <td>0.995209</td>\n",
       "      <td>0.700908</td>\n",
       "      <td>0.139350</td>\n",
       "      <td>0.365987</td>\n",
       "      <td>0.995613</td>\n",
       "      <td>0.695389</td>\n",
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       "      <td>0.370875</td>\n",
       "      <td>0.964271</td>\n",
       "      <td>0.582262</td>\n",
       "      <td>0.644630</td>\n",
       "      <td>0.042322</td>\n",
       "      <td>0.923103</td>\n",
       "      <td>0.750335</td>\n",
       "      <td>0.640705</td>\n",
       "      <td>0.323895</td>\n",
       "      <td>0.838903</td>\n",
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       "      <th>4</th>\n",
       "      <td>d_correct</td>\n",
       "      <td>0.716225</td>\n",
       "      <td>0.146869</td>\n",
       "      <td>0.293682</td>\n",
       "      <td>0.998649</td>\n",
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       "      <td>0.709841</td>\n",
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       "      <td>0.365578</td>\n",
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       "       class        x1        y1        z1        v1        x2        y2  \\\n",
       "0  d_correct  0.592055  0.387002  0.761125  0.976802  0.585143  0.381295   \n",
       "1  d_correct  0.587373  0.376647  0.683136  0.983089  0.581771  0.369854   \n",
       "2  d_correct  0.603379  0.289000  0.876913  0.977582  0.599490  0.280128   \n",
       "3  d_correct  0.704687  0.148774  0.440767  0.995209  0.700908  0.139350   \n",
       "4  d_correct  0.716225  0.146869  0.293682  0.998649  0.713821  0.136261   \n",
       "\n",
       "         z2        v2        x3  ...       z31       v31       x32       y32  \\\n",
       "0  0.735319  0.980862  0.582941  ...  0.491055  0.909689  0.588401  0.644509   \n",
       "1  0.658460  0.985313  0.579841  ...  0.409666  0.857568  0.582093  0.645119   \n",
       "2  0.851040  0.982493  0.597362  ...  0.369821  0.913276  0.565828  0.635022   \n",
       "3  0.365987  0.995613  0.695389  ...  0.370875  0.964271  0.582262  0.644630   \n",
       "4  0.216470  0.998652  0.709841  ...  0.414867  0.955359  0.579698  0.642973   \n",
       "\n",
       "        z32       v32       x33       y33       z33       v33  \n",
       "0  0.169380  0.901291  0.728158  0.618136  0.480577  0.840292  \n",
       "1  0.139664  0.876760  0.694696  0.614433  0.398222  0.798012  \n",
       "2  0.151700  0.870137  0.743687  0.628615  0.331195  0.832618  \n",
       "3  0.042322  0.923103  0.750335  0.640705  0.323895  0.838903  \n",
       "4  0.006126  0.938232  0.745315  0.636252  0.365578  0.844092  \n",
       "\n",
       "[5 rows x 133 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dead.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe058cdc",
   "metadata": {},
   "outputs": [
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       "      <th>y32</th>\n",
       "      <th>z32</th>\n",
       "      <th>v32</th>\n",
       "      <th>x33</th>\n",
       "      <th>y33</th>\n",
       "      <th>z33</th>\n",
       "      <th>v33</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s_spine_neutral</td>\n",
       "      <td>0.578854</td>\n",
       "      <td>0.459936</td>\n",
       "      <td>-0.178820</td>\n",
       "      <td>0.999752</td>\n",
       "      <td>0.594528</td>\n",
       "      <td>0.448642</td>\n",
       "      <td>-0.173299</td>\n",
       "      <td>0.999830</td>\n",
       "      <td>0.594286</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.313403</td>\n",
       "      <td>0.976089</td>\n",
       "      <td>0.302474</td>\n",
       "      <td>0.597096</td>\n",
       "      <td>0.656582</td>\n",
       "      <td>0.806852</td>\n",
       "      <td>0.332788</td>\n",
       "      <td>0.694722</td>\n",
       "      <td>-0.373114</td>\n",
       "      <td>0.985341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s_spine_neutral</td>\n",
       "      <td>0.590740</td>\n",
       "      <td>0.410155</td>\n",
       "      <td>-0.341654</td>\n",
       "      <td>0.999727</td>\n",
       "      <td>0.595270</td>\n",
       "      <td>0.397050</td>\n",
       "      <td>-0.327615</td>\n",
       "      <td>0.999847</td>\n",
       "      <td>0.594844</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.305247</td>\n",
       "      <td>0.975016</td>\n",
       "      <td>0.359256</td>\n",
       "      <td>0.611075</td>\n",
       "      <td>0.285869</td>\n",
       "      <td>0.773935</td>\n",
       "      <td>0.334483</td>\n",
       "      <td>0.695383</td>\n",
       "      <td>-0.373157</td>\n",
       "      <td>0.984979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s_spine_neutral</td>\n",
       "      <td>0.572588</td>\n",
       "      <td>0.146920</td>\n",
       "      <td>-0.468999</td>\n",
       "      <td>0.999881</td>\n",
       "      <td>0.573966</td>\n",
       "      <td>0.125410</td>\n",
       "      <td>-0.442385</td>\n",
       "      <td>0.999909</td>\n",
       "      <td>0.573949</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.462458</td>\n",
       "      <td>0.976983</td>\n",
       "      <td>0.493459</td>\n",
       "      <td>0.641891</td>\n",
       "      <td>0.653005</td>\n",
       "      <td>0.840647</td>\n",
       "      <td>0.369003</td>\n",
       "      <td>0.695396</td>\n",
       "      <td>-0.618573</td>\n",
       "      <td>0.986593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s_spine_neutral</td>\n",
       "      <td>0.563577</td>\n",
       "      <td>0.157547</td>\n",
       "      <td>-0.483738</td>\n",
       "      <td>0.999883</td>\n",
       "      <td>0.564866</td>\n",
       "      <td>0.136710</td>\n",
       "      <td>-0.457015</td>\n",
       "      <td>0.999882</td>\n",
       "      <td>0.564586</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.401422</td>\n",
       "      <td>0.968094</td>\n",
       "      <td>0.488726</td>\n",
       "      <td>0.641720</td>\n",
       "      <td>0.591863</td>\n",
       "      <td>0.822999</td>\n",
       "      <td>0.332679</td>\n",
       "      <td>0.700715</td>\n",
       "      <td>-0.569745</td>\n",
       "      <td>0.981765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s_spine_neutral</td>\n",
       "      <td>0.579107</td>\n",
       "      <td>0.414149</td>\n",
       "      <td>-0.166554</td>\n",
       "      <td>0.999601</td>\n",
       "      <td>0.593653</td>\n",
       "      <td>0.400882</td>\n",
       "      <td>-0.152463</td>\n",
       "      <td>0.999702</td>\n",
       "      <td>0.593684</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.601359</td>\n",
       "      <td>0.977251</td>\n",
       "      <td>0.393079</td>\n",
       "      <td>0.617481</td>\n",
       "      <td>0.678957</td>\n",
       "      <td>0.802303</td>\n",
       "      <td>0.347247</td>\n",
       "      <td>0.690384</td>\n",
       "      <td>-0.761584</td>\n",
       "      <td>0.985078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             class        x1        y1        z1        v1        x2  \\\n",
       "0  s_spine_neutral  0.578854  0.459936 -0.178820  0.999752  0.594528   \n",
       "1  s_spine_neutral  0.590740  0.410155 -0.341654  0.999727  0.595270   \n",
       "2  s_spine_neutral  0.572588  0.146920 -0.468999  0.999881  0.573966   \n",
       "3  s_spine_neutral  0.563577  0.157547 -0.483738  0.999883  0.564866   \n",
       "4  s_spine_neutral  0.579107  0.414149 -0.166554  0.999601  0.593653   \n",
       "\n",
       "         y2        z2        v2        x3  ...       z31       v31       x32  \\\n",
       "0  0.448642 -0.173299  0.999830  0.594286  ... -0.313403  0.976089  0.302474   \n",
       "1  0.397050 -0.327615  0.999847  0.594844  ... -0.305247  0.975016  0.359256   \n",
       "2  0.125410 -0.442385  0.999909  0.573949  ... -0.462458  0.976983  0.493459   \n",
       "3  0.136710 -0.457015  0.999882  0.564586  ... -0.401422  0.968094  0.488726   \n",
       "4  0.400882 -0.152463  0.999702  0.593684  ... -0.601359  0.977251  0.393079   \n",
       "\n",
       "        y32       z32       v32       x33       y33       z33       v33  \n",
       "0  0.597096  0.656582  0.806852  0.332788  0.694722 -0.373114  0.985341  \n",
       "1  0.611075  0.285869  0.773935  0.334483  0.695383 -0.373157  0.984979  \n",
       "2  0.641891  0.653005  0.840647  0.369003  0.695396 -0.618573  0.986593  \n",
       "3  0.641720  0.591863  0.822999  0.332679  0.700715 -0.569745  0.981765  \n",
       "4  0.617481  0.678957  0.802303  0.347247  0.690384 -0.761584  0.985078  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_squat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03291a0",
   "metadata": {},
   "source": [
    "# 3. Data Preprocessing & Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4debbf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "bench_data = np.loadtxt(\"./labeling/benchpress_coords_merged.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels = bench_data[:, 0]\n",
    "bench_data = bench_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels = encoder.fit_transform(labels.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "bench_data = np.nan_to_num(bench_data)\n",
    "\n",
    "bench_data = bench_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "bench_X_train, bench_X_test, bench_y_train, bench_y_test = train_test_split(bench_data, labels, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "bench_X_train = torch.tensor(bench_X_train)\n",
    "bench_X_test = torch.tensor(bench_X_test)\n",
    "bench_y_train = torch.tensor(bench_y_train)\n",
    "bench_y_test = torch.tensor(bench_y_test)\n",
    "\n",
    "# float 변환\n",
    "bench_X_train = bench_X_train.float()\n",
    "bench_X_test = bench_X_test.float()\n",
    "bench_y_train = bench_y_train.float()\n",
    "bench_y_test = bench_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e36cffe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "squat_data = np.loadtxt(\"./labeling/squat_coords_merged.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_squat = squat_data[:, 0]\n",
    "squat_data = squat_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_squat = encoder.fit_transform(labels_squat.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "squat_data = np.nan_to_num(squat_data)\n",
    "\n",
    "squat_data = squat_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "squat_X_train, squat_X_test, squat_y_train, squat_y_test = train_test_split(squat_data, labels_squat, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "squat_X_train = torch.tensor(squat_X_train)\n",
    "squat_X_test = torch.tensor(squat_X_test)\n",
    "squat_y_train = torch.tensor(squat_y_train)\n",
    "squat_y_test = torch.tensor(squat_y_test)\n",
    "\n",
    "# float 변환\n",
    "squat_X_train = squat_X_train.float()\n",
    "squat_X_test = squat_X_test.float()\n",
    "squat_y_train = squat_y_train.float()\n",
    "squat_y_test = squat_y_test.float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "daf4f7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 로드\n",
    "deadlift_data = np.loadtxt(\"./labeling/deadlift_coords_merged.csv\", delimiter=\",\", dtype=str, skiprows=1)\n",
    "labels_deadlift = deadlift_data[:, 0]\n",
    "deadlift_data = deadlift_data[:, 1:]  # 첫 번째 열을 제외한 모든 열 선택\n",
    "\n",
    "# One-Hot Encoding\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "labels_deadlift = encoder.fit_transform(labels_deadlift.reshape(-1, 1))\n",
    "\n",
    "# 결측값 처리\n",
    "deadlift_data = np.nan_to_num(deadlift_data)\n",
    "\n",
    "deadlift_data = deadlift_data.astype(float)\n",
    "\n",
    "# 데이터 분할\n",
    "deadlift_X_train, deadlift_X_test, deadlift_y_train, deadlift_y_test = train_test_split(deadlift_data, labels_deadlift, test_size=0.25)\n",
    "\n",
    "# 텐서 변환\n",
    "deadlift_X_train = torch.tensor(deadlift_X_train)\n",
    "deadlift_X_test = torch.tensor(deadlift_X_test)\n",
    "deadlift_y_train = torch.tensor(deadlift_y_train)\n",
    "deadlift_y_test = torch.tensor(deadlift_y_test)\n",
    "\n",
    "# float 변환\n",
    "deadlift_X_train = deadlift_X_train.float()\n",
    "deadlift_X_test = deadlift_X_test.float()\n",
    "deadlift_y_train = deadlift_y_train.float()\n",
    "deadlift_y_test = deadlift_y_test.float()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3122dfa3",
   "metadata": {},
   "source": [
    "# 4. Construct Deep Learning Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d56f3fb",
   "metadata": {},
   "source": [
    "### Bench Press"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85728780",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepClassifier(nn.Module):\n",
    "    def __init__(self, input_size, output_size):\n",
    "        super(DeepClassifier, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, 128)\n",
    "        self.fc2 = nn.Linear(128, 64)\n",
    "        self.fc3 = nn.Linear(64, 32)\n",
    "        self.fc4 = nn.Linear(32, output_size)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.dropout(self.relu(self.fc2(x)))\n",
    "        x = self.dropout(self.relu(self.fc3(x)))\n",
    "        x = self.fc4(x)\n",
    "        x = self.softmax(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "30ab9554",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 3\n",
    "bench_model = DeepClassifier(input_size, output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "afdac53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bench_criterion = nn.BCELoss()\n",
    "bench_optimizer = optim.Adam(bench_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a7e3c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bench_train_losses = []\n",
    "bench_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    bench_model.train()\n",
    "    bench_optimizer.zero_grad()\n",
    "    bench_outputs = bench_model(bench_X_train)\n",
    "    loss = bench_criterion(bench_outputs, bench_y_train)\n",
    "    loss.backward()\n",
    "    bench_optimizer.step()\n",
    "    bench_train_losses.append(loss.item())\n",
    "    \n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        bench_test_outputs = bench_model(bench_X_test)\n",
    "        bench_test_loss = bench_criterion(bench_test_outputs, bench_y_test)\n",
    "        bench_test_losses.append(bench_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), bench_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), bench_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fbca1495",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.04435794800519943\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    test_outputs = bench_model(bench_X_test)\n",
    "    test_loss = bench_criterion(test_outputs, bench_y_test)\n",
    "    print(f'Test Loss: {test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8bcb0bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.3179,  0.3038,  0.4711,  1.0000,  0.3071,  0.3031,  0.5278,  1.0000,\n",
      "         0.3087,  0.3038,  0.5278,  1.0000,  0.3102,  0.3047,  0.5276,  1.0000,\n",
      "         0.2993,  0.3047,  0.4848,  1.0000,  0.2959,  0.3065,  0.4847,  1.0000,\n",
      "         0.2923,  0.3088,  0.4846,  1.0000,  0.3120,  0.3182,  0.7329,  1.0000,\n",
      "         0.2876,  0.3250,  0.5397,  1.0000,  0.3328,  0.3119,  0.5462,  1.0000,\n",
      "         0.3270,  0.3130,  0.4893,  1.0000,  0.4535,  0.3443,  0.7954,  1.0000,\n",
      "         0.3080,  0.3665,  0.3657,  1.0000,  0.5954,  0.3631,  0.8200,  0.4401,\n",
      "         0.2575,  0.4051, -0.0720,  0.9903,  0.6399,  0.2903,  0.6974,  0.6307,\n",
      "         0.2436,  0.3381, -0.3529,  0.9885,  0.6486,  0.2752,  0.6897,  0.5819,\n",
      "         0.2432,  0.3306, -0.4133,  0.9628,  0.6277,  0.2647,  0.6417,  0.5923,\n",
      "         0.2487,  0.3257, -0.3453,  0.9550,  0.6234,  0.2694,  0.6665,  0.6047,\n",
      "         0.2510,  0.3284, -0.3322,  0.9453,  0.6970,  0.4062,  0.1216,  1.0000,\n",
      "         0.5732,  0.4213, -0.1214,  1.0000,  0.9593,  0.4316, -0.2124,  0.9908,\n",
      "         0.6704,  0.4963, -0.5832,  0.9983,  0.8750,  0.5648,  0.0165,  0.9446,\n",
      "         0.6654,  0.6590, -0.7695,  0.9951,  0.8430,  0.5805,  0.0308,  0.8892,\n",
      "         0.6415,  0.6670, -0.7893,  0.9771,  0.9766,  0.6075, -0.0873,  0.9313,\n",
      "         0.7121,  0.7351, -0.9693,  0.9934])\n",
      "실제 타겟 데이터: tensor([0., 1., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0040', '0.9958', '0.0002']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.4912,  0.3415,  0.8235,  1.0000,  0.5037,  0.3392,  0.8771,  1.0000,\n",
      "         0.5116,  0.3398,  0.8774,  1.0000,  0.5196,  0.3407,  0.8768,  1.0000,\n",
      "         0.4835,  0.3387,  0.8779,  1.0000,  0.4784,  0.3389,  0.8780,  1.0000,\n",
      "         0.4715,  0.3392,  0.8781,  1.0000,  0.5339,  0.3491,  0.9386,  1.0000,\n",
      "         0.4339,  0.3548,  0.9466,  1.0000,  0.5072,  0.3499,  0.8267,  1.0000,\n",
      "         0.4818,  0.3490,  0.8270,  1.0000,  0.6088,  0.3959,  0.7975,  1.0000,\n",
      "         0.3863,  0.3860,  0.7769,  1.0000,  0.7509,  0.4169,  0.8379,  0.9850,\n",
      "         0.2418,  0.4130,  0.7115,  0.9913,  0.8093,  0.3457,  0.8691,  0.9946,\n",
      "         0.1766,  0.3511,  0.5821,  0.9987,  0.8193,  0.3272,  0.8866,  0.9869,\n",
      "         0.1597,  0.3293,  0.5868,  0.9969,  0.7967,  0.3276,  0.8975,  0.9857,\n",
      "         0.1710,  0.3274,  0.5779,  0.9957,  0.7879,  0.3356,  0.8688,  0.9884,\n",
      "         0.1819,  0.3352,  0.5734,  0.9963,  0.5780,  0.4764,  0.0160,  1.0000,\n",
      "         0.3904,  0.4775, -0.0171,  1.0000,  0.7792,  0.5726, -0.4736,  0.9985,\n",
      "         0.1443,  0.5505, -0.4982,  0.9989,  0.6933,  0.6545,  0.0728,  0.9294,\n",
      "         0.2415,  0.6480,  0.0260,  0.9826,  0.6550,  0.6687,  0.1295,  0.9298,\n",
      "         0.2823,  0.6629,  0.0848,  0.9484,  0.7773,  0.7160,  0.0501,  0.9213,\n",
      "         0.1584,  0.7084, -0.0103,  0.9547])\n",
      "실제 타겟 데이터: tensor([1., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.9923', '0.0077', '0.0000']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.7366,  0.3394,  0.4846,  0.9987,  0.7649,  0.3389,  0.5242,  0.9985,\n",
      "         0.7720,  0.3406,  0.5239,  0.9985,  0.7829,  0.3392,  0.5235,  0.9984,\n",
      "         0.7400,  0.3415,  0.5450,  0.9978,  0.7340,  0.3421,  0.5450,  0.9972,\n",
      "         0.7301,  0.3422,  0.5448,  0.9983,  0.7872,  0.3490,  0.6226,  0.9986,\n",
      "         0.7255,  0.3472,  0.7186,  0.9988,  0.7369,  0.3489,  0.5066,  0.9967,\n",
      "         0.7201,  0.3454,  0.5378,  0.9960,  0.7944,  0.3843,  0.4751,  0.9999,\n",
      "         0.6058,  0.3536,  0.6545,  0.9992,  0.8385,  0.3275,  0.2541,  0.9554,\n",
      "         0.4899,  0.3081,  0.6944,  0.5887,  0.8268,  0.2485,  0.0681,  0.9331,\n",
      "         0.5232,  0.2228,  0.5245,  0.8478,  0.8125,  0.2293,  0.0053,  0.8910,\n",
      "         0.5093,  0.2031,  0.4827,  0.8611,  0.8122,  0.2273,  0.0277,  0.8592,\n",
      "         0.5234,  0.1996,  0.4473,  0.8625,  0.8139,  0.2338,  0.0704,  0.8300,\n",
      "         0.5277,  0.2061,  0.5019,  0.8454,  0.5726,  0.4343, -0.0218,  1.0000,\n",
      "         0.4454,  0.4157,  0.0211,  1.0000,  0.5143,  0.5435, -0.4087,  0.9933,\n",
      "         0.1934,  0.4841, -0.2816,  0.9792,  0.5706,  0.5780, -0.0448,  0.8792,\n",
      "         0.4073,  0.5319,  0.3546,  0.5987,  0.5877,  0.5738,  0.0077,  0.9110,\n",
      "         0.4351,  0.5381,  0.4320,  0.6652,  0.5511,  0.6278,  0.0242,  0.9051,\n",
      "         0.3788,  0.5809,  0.4770,  0.7093])\n",
      "실제 타겟 데이터: tensor([0., 0., 1.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.0000', '0.0002', '0.9998']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.5088,  0.3917,  0.8348,  1.0000,  0.5204,  0.3919,  0.8674,  1.0000,\n",
      "         0.5279,  0.3924,  0.8678,  1.0000,  0.5353,  0.3926,  0.8672,  1.0000,\n",
      "         0.5024,  0.3918,  0.8645,  1.0000,  0.4972,  0.3920,  0.8644,  1.0000,\n",
      "         0.4920,  0.3921,  0.8646,  1.0000,  0.5498,  0.3997,  0.8570,  1.0000,\n",
      "         0.4636,  0.3985,  0.8485,  1.0000,  0.5218,  0.3972,  0.8179,  1.0000,\n",
      "         0.4990,  0.3977,  0.8115,  1.0000,  0.6192,  0.4403,  0.7467,  1.0000,\n",
      "         0.4099,  0.4405,  0.7009,  1.0000,  0.7173,  0.4785,  0.9910,  0.9711,\n",
      "         0.2925,  0.4854,  0.6812,  0.9932,  0.8012,  0.4240,  1.2126,  0.9876,\n",
      "         0.2447,  0.4133,  0.6508,  0.9987,  0.8272,  0.4071,  1.2808,  0.9715,\n",
      "         0.2290,  0.3913,  0.6772,  0.9952,  0.8141,  0.4002,  1.2754,  0.9676,\n",
      "         0.2429,  0.3880,  0.6893,  0.9932,  0.8009,  0.4061,  1.2137,  0.9735,\n",
      "         0.2534,  0.3932,  0.6525,  0.9940,  0.6005,  0.5207,  0.0308,  1.0000,\n",
      "         0.4289,  0.5160, -0.0315,  1.0000,  0.8055,  0.6039, -0.5347,  0.9971,\n",
      "         0.2207,  0.5841, -0.5757,  0.9978,  0.7740,  0.7278, -0.1184,  0.9497,\n",
      "         0.2213,  0.7209, -0.2467,  0.9906,  0.7322,  0.7402, -0.0815,  0.9297,\n",
      "         0.2522,  0.7341, -0.2077,  0.9502,  0.8368,  0.7997, -0.2342,  0.9489,\n",
      "         0.1637,  0.7962, -0.3843,  0.9809])\n",
      "실제 타겟 데이터: tensor([1., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.9922', '0.0078', '0.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.5147,  0.3722,  0.5258,  1.0000,  0.5240,  0.3684,  0.5779,  1.0000,\n",
      "         0.5318,  0.3691,  0.5781,  1.0000,  0.5395,  0.3700,  0.5777,  1.0000,\n",
      "         0.5033,  0.3683,  0.5854,  1.0000,  0.4963,  0.3689,  0.5855,  1.0000,\n",
      "         0.4897,  0.3697,  0.5856,  1.0000,  0.5491,  0.3770,  0.7101,  1.0000,\n",
      "         0.4811,  0.3787,  0.7444,  1.0000,  0.5308,  0.3816,  0.5618,  1.0000,\n",
      "         0.5024,  0.3810,  0.5704,  1.0000,  0.6268,  0.4239,  0.6500,  1.0000,\n",
      "         0.4159,  0.4318,  0.7434,  1.0000,  0.7307,  0.4731,  0.6841,  0.9372,\n",
      "         0.3089,  0.4832,  0.6885,  0.9758,  0.7362,  0.4095,  0.7565,  0.9210,\n",
      "         0.2883,  0.4158,  0.5941,  0.9886,  0.7416,  0.3932,  0.7689,  0.8560,\n",
      "         0.2835,  0.3967,  0.5799,  0.9603,  0.7303,  0.3873,  0.7898,  0.8303,\n",
      "         0.2986,  0.3927,  0.6197,  0.9494,  0.7243,  0.3922,  0.7646,  0.8210,\n",
      "         0.3042,  0.3974,  0.5995,  0.9436,  0.6129,  0.5260, -0.0203,  0.9998,\n",
      "         0.4424,  0.5292,  0.0197,  0.9998,  0.7862,  0.6029, -0.4649,  0.9956,\n",
      "         0.2551,  0.5957, -0.3930,  0.9973,  0.7390,  0.7503, -0.1590,  0.9454,\n",
      "         0.2862,  0.7439, -0.1681,  0.9951,  0.7100,  0.7653, -0.1304,  0.8408,\n",
      "         0.3190,  0.7600, -0.1432,  0.9675,  0.7912,  0.8326, -0.2914,  0.9256,\n",
      "         0.2320,  0.8272, -0.3036,  0.9825])\n",
      "실제 타겟 데이터: tensor([0., 1., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0162', '0.9837', '0.0001']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_70819/658835650.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = bench_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = bench_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    bench_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = bench_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9cacd475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.971\n",
      "정밀도: 0.991\n",
      "재현율: 0.971\n",
      "F1 점수: 0.981\n"
     ]
    }
   ],
   "source": [
    "bench_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = bench_model(bench_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(bench_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(bench_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4414bf86",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TeXBHGABudvjwYaN///5GuXLlDB8fH6NYsWJG48aNjfnz5xtpaWmOeTltlzhq1CijdOnShp+fn9G4cWMjLi4u21Z/b775ptG0aVOjRIkSht1uNypWrGiMGTPGuHDhgmEYhpGenm6MGTPGqFOnjlGsWDHD39/fqFOnjvHGG2/kqv5r2+Ll9Dh+/HiuzrFnzx7j8ccfN8LDw42iRYsaxYsXN1q2bGksW7bMaUvIP/74wxgxYoRjXuXKlY2XX37ZyMrKcjqfpBy3f/z7PXS1XaJhGMa7775rVKhQwfDx8THq1q1rfP7559m2S1yzZo3RqlUrIzQ01PDx8THKli1rPPPMM8aZM2eyXePvWwq+//77Rr169Qy73W6EhIQY0dHRxi+//OI0JyYmxvD3989W26RJk4zc/F/BX7dLdCWne3D+/HmjT58+xp133mkEBAQYrVu3Ng4dOpTjNoeLFi0yKlSoYHh7ezu9z4iICKN9+/Y5XvOv5/n555+NoKAgo2PHjtnmPfzww4a/v79x7NixG75XAPAUm2Hwb3sAAACAp7HGHAAAALAAGnMAAADAAmjMAQAAAAugMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBAAAACyiQ3/zpV2+wp0sAAJ3f9ZqnSwAA+Vqs2zOrT7v8/e33dzCJOQAAAGABFvsZCgAAAAWajVzYFe4MAAAAYAEk5gAAADCPzebpCiyLxBwAAACwABJzAAAAmIc15i5xZwAAAAALIDEHAACAeVhj7hKJOQAAAGABNOYAAAAwj83LnEcebN++XR07dlR4eLhsNpvWrl3rcu6AAQNks9k0Z84cp/GkpCRFR0crMDBQwcHB6tu3r1JSUvJUB405AAAACrXU1FTVqVNHr7/++nXnffzxx/ruu+8UHh6e7Vh0dLQOHDigTZs2af369dq+fbuefvrpPNXBGnMAAACYx4JrzNu2bau2bdted87p06c1ZMgQff7552rfvr3Tsfj4eG3cuFG7du1SgwYNJEnz589Xu3btNGvWrBwb+ZyQmAMAAADXkZWVpSeeeEJjxoxRjRo1sh2Pi4tTcHCwoymXpKioKHl5eWnnzp25vg6JOQAAAMxj0j7m6enpSk9Pdxqz2+2y2+15PtdLL72kIkWKaOjQoTkeT0xMVGhoqNNYkSJFFBISosTExFxfh8QcAAAABU5sbKyCgoKcHrGxsXk+z549ezR37lwtXbpUtnxehkNiDgAAAPOYtMZ8/PjxGjlypNPYzaTlX3/9tc6dO6eyZcs6xjIzMzVq1CjNmTNHJ06cUFhYmM6dO+f0uqtXryopKUlhYWG5vhaNOQAAAAqcm1228ndPPPGEoqKinMZat26tJ554Qn369JEkRUZGKjk5WXv27FH9+vUlSVu2bFFWVpYaNmyY62vRmAMAAMA8Jq0xz4uUlBQdOXLE8fz48ePat2+fQkJCVLZsWZUoUcJpftGiRRUWFqYqVapIkqpVq6Y2bdqof//+WrhwoTIyMjR48GD16NEj1zuySKwxBwAAQCG3e/du1atXT/Xq1ZMkjRw5UvXq1dPEiRNzfY6VK1eqatWqatmypdq1a6cmTZrorbfeylMdJOYAAAAwjwX3MW/evLkMw8j1/BMnTmQbCwkJ0apVq26pDhJzAAAAwAJIzAEAAGAeC64xtwruDAAAAGABJOYAAAAwjwXXmFsFiTkAAABgASTmAAAAMA9rzF3izgAAAAAWQGIOAAAA85CYu8SdAQAAACyAxBwAAADm8WJXFldIzAEAAAALIDEHAACAeVhj7hJ3BgAAALAAEnMAAACYh2/+dInEHAAAALAAGnMAAADAAljKAgAAAPPw4U+XuDMAAACABZCYAwAAwDx8+NMlEnMAAADAAkjMAQAAYB7WmLvEnQEAAAAsgMQcAAAA5mGNuUsk5gAAAIAFkJgDAADAPKwxd4k7AwAAAFgAiTkAAADMwxpzl0jMAQAAAAsgMQcAAIB5WGPuEncGAAAAsAAScwAAAJiHNeYukZgDAAAAFkBiDgAAAPOwxtwl7gwAAABgASTmAAAAMA+JuUvcGQAAAMACSMwBAABgHnZlcYnEHAAAALAAEnMAAACYhzXmLnFnAAAAAAsgMQcAAIB5WGPuEok5AAAAYAEk5gAAADAPa8xd4s4AAAAAFkBiDgAAAPOwxtwlEnMAAADAAkjMAQAAYBobiblLJOYAAACABZCYAwAAwDQk5q6RmAMAAAAWQGIOAAAA8xCYu0RiDgAAAFgAiTkAAABMwxpz10jMAQAAAAsgMQcAAIBpSMxdIzEHAAAALIDEHAAAAKYhMXeNxBwAAACwABJzAAAAmIbE3DUScwAAAMACSMwBAABgHgJzl0jMAQAAUKht375dHTt2VHh4uGw2m9auXes4lpGRoWeffVa1atWSv7+/wsPD9eSTT+rXX391OkdSUpKio6MVGBio4OBg9e3bVykpKXmqg8YcAAAAprHZbKY88iI1NVV16tTR66+/nu3YpUuXtHfvXk2YMEF79+7VRx99pISEBHXq1MlpXnR0tA4cOKBNmzZp/fr12r59u55++um83RvDMIw8veI24FdvsKdLAACd3/Wap0sAAPlabOFycPS7plwneWWvm3qdzWbTxx9/rC5durics2vXLt1///06efKkypYtq/j4eFWvXl27du1SgwYNJEkbN25Uu3bt9Msvvyg8PDxX1yYxBwAAgGmsmJjn1YULF2Sz2RQcHCxJiouLU3BwsKMpl6SoqCh5eXlp586duT6vxX6GAgAAAG5denq60tPTncbsdrvsdvstnTctLU3PPvusevbsqcDAQElSYmKiQkNDneYVKVJEISEhSkxMzPW5ScwBAABgGrMS89jYWAUFBTk9YmNjb6n2jIwMde/eXYZhaMGCBW66I/8PiTkAAAAKnPHjx2vkyJFOY7eSll9ryk+ePKktW7Y40nJJCgsL07lz55zmX716VUlJSQoLC8v1NWjMAQAAYBqzvvnTHctWrrnWlP/000/aunWrSpQo4XQ8MjJSycnJ2rNnj+rXry9J2rJli7KystSwYcNcX4fGHAAAAIVaSkqKjhw54nh+/Phx7du3TyEhISpdurS6deumvXv3av369crMzHSsGw8JCZGPj4+qVaumNm3aqH///lq4cKEyMjI0ePBg9ejRI9c7skg05gAAADCTBb/5c/fu3WrRooXj+bUlMDExMZo8ebI++eQTSVLdunWdXrd161Y1b95ckrRy5UoNHjxYLVu2lJeXl7p27ap58+blqQ4acwAAABRqzZs31/W+2ic3X/sTEhKiVatW3VIdNOYAAAAwjVlrzG9HbJcIAAAAWACJOQAAAExDYu4aiTkAAABgASTmAAAAMA2JuWsk5gAAAIAFkJgDAADAPATmLpGYAwAAABZAYg4AAADTsMbcNRJzAAAAwAJIzAEAAGAaEnPXSMwBAAAACyAxBwAAgGlIzF0jMQcAAAAsgMQcAAAApiExd43EHAAAALAAEnMAAACYh8DcJRJzAAAAwAJIzAEAAGAa1pi7RmIOAAAAWACJOQAAAExDYu4aiTkAAABgASTmAAAAMA2JuWuWSMy//vpr9erVS5GRkTp9+rQkacWKFdqxY4eHKwMAAADM4fHG/MMPP1Tr1q3l5+en77//Xunp6ZKkCxcuaMaMGR6uDgAAAG5lM+lxG/J4Yz5t2jQtXLhQixYtUtGiRR3jjRs31t69ez1YGQAAAGAej68xT0hIUNOmTbONBwUFKTk52fyCAAAAkG9YY+6axxPzsLAwHTlyJNv4jh07VKFCBQ9UBAAAAJjP4415//79NWzYMO3cuVM2m02//vqrVq5cqdGjR+uf//ynp8sDAACAG9lsNlMetyOPL2UZN26csrKy1LJlS126dElNmzaV3W7X6NGjNWTIEE+XBwAAAJjC44m5zWbTc889p6SkJP3444/67rvv9Ntvv+mFF17wdGkoYBrfW1Fr5jyjY19M1+XvX1PH5rVdzp33XA9d/v41DX68eY7HfYoW0XfvjdPl719T7XvuyqeKARRGe3bv0pCBAxTVvInq1KiiLZu/9HRJgFuRmLvm8cb8Gh8fH1WvXl3333+/AgICPF0OCiB/P7t+OHxaw2Pfv+68Ti1q6/5a5fTruWSXc2YM76wzv11wc4UAIF2+fElVqlTR+OcneboUACbz+FKWFi1aXPenmi1btphYDQqyL745qC++OXjdOeElg/Tqs4+q48DX9fH8nD/j0KpxdbX8RzX1HPO22jSpkR+lAijEmjzQTE0eaObpMoB8c7um2WbweGNet25dp+cZGRnat2+ffvzxR8XExHimKBRKNptNi6c9qdnLNiv+WGKOc0JDiumNCT3VfeQiXbp8xeQKAQBAQebxxnz27Nk5jk+ePFkpKSkmV4PCbFSfh3Q1M0uv/3ubyzlvTe2lRWt2aO/BUypbOsS84gAAKCgIzF2yzBrzv+vVq5feeeedG85LT0/XxYsXnR5GVqYJFaIgqVetjAb1bK6nJ73rcs7Ans1U7A5fvfzOFyZWBgAACguPJ+auxMXFydfX94bzYmNjNWXKFKcx71L3qWjp+/OrNBRAjetVVGhIgA5/OtUxVqSIt14c+YgGR7dQ1faT1Py+e9Swdnld2DnH6bXfrByr9z7brf4TV5hcNQAAtx/WmLvm8cb8kUcecXpuGIbOnDmj3bt3a8KECTd8/fjx4zVy5EinsdAHnnVrjSj4Vm3YpS07E5zG1r0xSKs2/FfL//OdJGnUzDWa/Pp6x/HSJYO0fsFgPTFuiXb9cMLMcgEAQAHk8cY8KCjI6bmXl5eqVKmiqVOnqlWrVjd8vd1ul91udxqzeXm7tUYUDP5+PqpYpqTjebm7Sqj2PXfp/MVL+jnxvJIupDrNz7iaqbP/d1E/nTwnSfo58bzT8ZRL6ZKkYz//ptPX2VoRAPLiUmqqTp065Xh++pdfdCg+XkFBQSodHu7BygD3IDF3zaONeWZmpvr06aNatWqpePHiniwFhcC91SP0xdvDHM9nju4qSVrxyXfXXVsOAGY6cOBH9evzpOP5rJmxkqROnR/WCzNe9FRZAExgMwzD8GQBvr6+io+PV/ny5d12Tr96g912LgC4Wed3vebpEgBAvh5fH+Gs0ujPTLnOkVltTbmOO3l8V5aaNWvq2LFjni4DAAAA8CiPN+bTpk3T6NGjtX79ep05cybb1ocAAAAoOGw2mymP25HH/nFj6tSpGjVqlNq1aydJ6tSpk9NNNAxDNptNmZnsSQ4AAICCz2ON+ZQpUzRgwABt3brVUyUAAADAZLdpmG0KjzXm1z5z2qxZM0+VAAAAAFiGRz+ne7uu/wEAAMDNof9zzaON+T333HPD35ykpCSTqgEAAAA8x6ON+ZQpU7J98ycAAAAKLgJz1zzamPfo0UOhoaGeLAEAAACwBI815qwvAgAAKHy8vOgBXfHYFwxd25UFAAAAgAcT86ysLE9dGgAAAB7CognXPJaYAwAAAPh/PPrhTwAAABQufM7QNRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgGlYY+4aiTkAAABgASTmAAAAMA2JuWsk5gAAACjUtm/fro4dOyo8PFw2m01r1651Om4YhiZOnKjSpUvLz89PUVFR+umnn5zmJCUlKTo6WoGBgQoODlbfvn2VkpKSpzpozAEAAGAam82cR16kpqaqTp06ev3113M8PnPmTM2bN08LFy7Uzp075e/vr9atWystLc0xJzo6WgcOHNCmTZu0fv16bd++XU8//XSe6mApCwAAAAq1tm3bqm3btjkeMwxDc+bM0fPPP6/OnTtLkpYvX65SpUpp7dq16tGjh+Lj47Vx40bt2rVLDRo0kCTNnz9f7dq106xZsxQeHp6rOkjMAQAAYBqbzWbKw12OHz+uxMRERUVFOcaCgoLUsGFDxcXFSZLi4uIUHBzsaMolKSoqSl5eXtq5c2eur0ViDgAAgAInPT1d6enpTmN2u112uz1P50lMTJQklSpVymm8VKlSjmOJiYkKDQ11Ol6kSBGFhIQ45uQGiTkAAABMY9Ya89jYWAUFBTk9YmNjPf32r4vEHAAAAAXO+PHjNXLkSKexvKblkhQWFiZJOnv2rEqXLu0YP3v2rOrWreuYc+7cOafXXb16VUlJSY7X5waJOQAAAExj1hpzu92uwMBAp8fNNObly5dXWFiYNm/e7Bi7ePGidu7cqcjISElSZGSkkpOTtWfPHsecLVu2KCsrSw0bNsz1tUjMAQAAUKilpKToyJEjjufHjx/Xvn37FBISorJly2r48OGaNm2aKleurPLly2vChAkKDw9Xly5dJEnVqlVTmzZt1L9/fy1cuFAZGRkaPHiwevTokesdWSQacwAAAJjIil/8uXv3brVo0cLx/NoSmJiYGC1dulRjx45Vamqqnn76aSUnJ6tJkybauHGjfH19Ha9ZuXKlBg8erJYtW8rLy0tdu3bVvHnz8lSHzTAMwz1vyTr86g32dAkAoPO7XvN0CQAgX4vFsA2mbTXlOrufb3HjSRZjsd8qAAAAFGTu3GO8oOHDnwAAAIAFkJgDAADANATmrpGYAwAAABZAYg4AAADTsMbcNRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgGlYY+4aiTkAAABgASTmAAAAMA2BuWsk5gAAAIAFkJgDAADANKwxd43EHAAAALAAEnMAAACYhsDcNRJzAAAAwAJIzAEAAGAa1pi7RmIOAAAAWACJOQAAAExDYu4aiTkAAABgASTmAAAAMA2BuWsk5gAAAIAFkJgDAADANKwxd43EHAAAALAAEnMAAACYhsDcNRJzAAAAwAJIzAEAAGAa1pi7RmIOAAAAWACJOQAAAExDYO4aiTkAAABgASTmAAAAMI0XkblLJOYAAACABZCYAwAAwDQE5q6RmAMAAAAWQGIOAAAA07CPuWsk5gAAAIAFkJgDAADANF4E5i6RmAMAAAAWQGIOAAAA07DG3DUScwAAAMACSMwBAABgGgJz10jMAQAAAAsgMQcAAIBpbCIyd4XEHAAAALAAEnMAAACYhn3MXSMxBwAAACyAxBwAAACmYR9z10jMAQAAAAsgMQcAAIBpCMxdIzEHAAAALIDEHAAAAKbxIjJ3icQcAAAAsAAScwAAAJiGwNw1EnMAAADAAkjMAQAAYBr2MXeNxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgGvYxd43EHAAAALCAXCXmn3zySa5P2KlTp5suBgAAAAUbeblruWrMu3TpkquT2Ww2ZWZm3ko9AAAAgGkyMzM1efJkvfvuu0pMTFR4eLh69+6t559/3rG1o2EYmjRpkhYtWqTk5GQ1btxYCxYsUOXKld1aS64a86ysLLdeFAAAAIWT1fYxf+mll7RgwQItW7ZMNWrU0O7du9WnTx8FBQVp6NChkqSZM2dq3rx5WrZsmcqXL68JEyaodevWOnjwoHx9fd1Wyy19+DMtLc2txQAAAABm+vbbb9W5c2e1b99eklSuXDn9+9//1n//+19Jf6blc+bM0fPPP6/OnTtLkpYvX65SpUpp7dq16tGjh9tqyfOHPzMzM/XCCy/orrvuUkBAgI4dOyZJmjBhghYvXuy2wgAAAFDweNnMeeRWo0aNtHnzZh0+fFiStH//fu3YsUNt27aVJB0/flyJiYmKiopyvCYoKEgNGzZUXFyce+9NXl8wffp0LV26VDNnzpSPj49jvGbNmnr77bfdWhwAAABwM9LT03Xx4kWnR3p6erZ548aNU48ePVS1alUVLVpU9erV0/DhwxUdHS1JSkxMlCSVKlXK6XWlSpVyHHOXPDfmy5cv11tvvaXo6Gh5e3s7xuvUqaNDhw65tTgAAAAULDabzZRHbGysgoKCnB6xsbHZ6vnggw+0cuVKrVq1Snv37tWyZcs0a9YsLVu2zPR7k+c15qdPn1alSpWyjWdlZSkjI8MtRQEAAAC3Yvz48Ro5cqTTmN1uzzZvzJgxjtRckmrVqqWTJ08qNjZWMTExCgsLkySdPXtWpUuXdrzu7Nmzqlu3rltrznNiXr16dX399dfZxtesWaN69eq5pSgAAAAUTDabOQ+73a7AwECnR06N+aVLl+Tl5dwSe3t7O3YlLF++vMLCwrR582bH8YsXL2rnzp2KjIx0673Jc2I+ceJExcTE6PTp08rKytJHH32khIQELV++XOvXr3drcQAAAEB+6tixo6ZPn66yZcuqRo0a+v777/Xqq6/qqaeekvTn0pvhw4dr2rRpqly5smO7xPDw8Fx/109u5bkx79y5s9atW6epU6fK399fEydO1L333qt169bpoYcecmtxAAAAKFisto/5/PnzNWHCBA0cOFDnzp1TeHi4nnnmGU2cONExZ+zYsUpNTdXTTz+t5ORkNWnSRBs3bnT7tuE2wzAMt57RAvzqDfZ0CQCg87te83QJACDfW/rWGvd7ctX/TLnO8sdrm3Idd7rp36rdu3crPj5e0p/rzuvXr++2ogAAAFAw5WWP8cImz435L7/8op49e+qbb75RcHCwJCk5OVmNGjXSe++9p7vvvtvdNQIAAAAFXp53ZenXr58yMjIUHx+vpKQkJSUlKT4+XllZWerXr19+1AgAAIACwqx9zG9HeU7Mv/rqK3377beqUqWKY6xKlSqaP3++HnjgAbcWBwAAABQWeW7My5Qpk+MXCWVmZio8PNwtRQEAAKBguj2zbHPkeSnLyy+/rCFDhmj37t2Osd27d2vYsGGaNWuWW4sDAAAACotcJebFixd3WquTmpqqhg0bqkiRP19+9epVFSlSRE899ZTbN1oHAABAweF1m67/NkOuGvM5c+bkcxkAAABA4ZarxjwmJia/6wAAAEAhQGDu2i19F1RaWpquXLniNBYYGHhLBQEAAACFUZ4b89TUVD377LP64IMP9Pvvv2c7npmZ6ZbCAAAAUPDcrnuMmyHPu7KMHTtWW7Zs0YIFC2S32/X2229rypQpCg8P1/Lly/OjRgAAAKDAy3Nivm7dOi1fvlzNmzdXnz599MADD6hSpUqKiIjQypUrFR0dnR91AgAAoAAgMHctz4l5UlKSKlSoIOnP9eRJSUmSpCZNmmj79u3urQ4AAAAoJPLcmFeoUEHHjx+XJFWtWlUffPCBpD+T9ODgYLcWBwAAgILFy2Yz5XE7ynNj3qdPH+3fv1+SNG7cOL3++uvy9fXViBEjNGbMGLcXCAAAABQGeV5jPmLECMevo6KidOjQIe3Zs0eVKlVS7dq13VocAAAAUFjc0j7mkhQREaGIiAh31AIAAIAC7jZdZWKKXDXm8+bNy/UJhw4detPFAAAAAIVVrhrz2bNn5+pkNpuNxhwAAAAu8QVDruWqMb+2CwsAAACA/HHLa8yt6Pyu1zxdAgCo1bxvPF0CAGj7yMaeLsFJnrcELES4NwAAAIAFFMjEHAAAANbEGnPXSMwBAAAACyAxBwAAgGm8CMxduqnE/Ouvv1avXr0UGRmp06dPS5JWrFihHTt2uLU4AAAAoLDIc2P+4YcfqnXr1vLz89P333+v9PR0SdKFCxc0Y8YMtxcIAACAgsPLZs7jdpTnxnzatGlauHChFi1apKJFizrGGzdurL1797q1OAAAAKCwyPMa84SEBDVt2jTbeFBQkJKTk91REwAAAAoodmVxLc+JeVhYmI4cOZJtfMeOHapQoYJbigIAAAAKmzw35v3799ewYcO0c+dO2Ww2/frrr1q5cqVGjx6tf/7zn/lRIwAAAAoI1pi7luelLOPGjVNWVpZatmypS5cuqWnTprLb7Ro9erSGDBmSHzUCAAAABV6eG3ObzabnnntOY8aM0ZEjR5SSkqLq1asrICAgP+oDAABAAcISc9du+guGfHx8VL16dXfWAgAAABRaeW7MW7Rocd1P027ZsuWWCgIAAEDB5UVk7lKeG/O6des6Pc/IyNC+ffv0448/KiYmxl11AQAAAIVKnhvz2bNn5zg+efJkpaSk3HJBAAAAKLjyvCVgIeK2e9OrVy+988477jodAAAAUKjc9Ic//y4uLk6+vr7uOh0AAAAKIJaYu5bnxvyRRx5xem4Yhs6cOaPdu3drwoQJbisMAAAAKEzy3JgHBQU5Pffy8lKVKlU0depUtWrVym2FAQAAoOBhVxbX8tSYZ2Zmqk+fPqpVq5aKFy+eXzUBAAAAhU6ePvzp7e2tVq1aKTk5OZ/KAQAAQEFms5nzuB3leVeWmjVr6tixY/lRCwAAAFBo5bkxnzZtmkaPHq3169frzJkzunjxotMDAAAAcMXLZs7jdpTrNeZTp07VqFGj1K5dO0lSp06dZPvLvxMYhiGbzabMzEz3VwkAAAAUcLluzKdMmaIBAwZo69at+VkPAAAACjB2ZXEt1425YRiSpGbNmuVbMQAAAEBhlaftEm38hAMAAIBbQDvpWp4a83vuueeGzXlSUtItFQQAAAAURnlqzKdMmZLtmz8BAACA3Lpdd0wxQ54a8x49eig0NDS/agEAAAAKrVw35qwvBwAAwK2yiZ7SlVx/wdC1XVkAAAAAuF+uE/OsrKz8rAMAAACFAGvMXct1Yg4AAAAg/+Tpw58AAADArSAxd43EHAAAALAAEnMAAACYhp3+XCMxBwAAACyAxhwAAACm8bKZ88iL06dPq1evXipRooT8/PxUq1Yt7d6923HcMAxNnDhRpUuXlp+fn6KiovTTTz+5+c7QmAMAAKAQO3/+vBo3bqyiRYvqs88+08GDB/XKK6+oePHijjkzZ87UvHnztHDhQu3cuVP+/v5q3bq10tLS3FoLa8wBAABgGqstMX/ppZdUpkwZLVmyxDFWvnx5x68Nw9CcOXP0/PPPq3PnzpKk5cuXq1SpUlq7dq169OjhtlpIzAEAAFDgpKen6+LFi06P9PT0bPM++eQTNWjQQI8++qhCQ0NVr149LVq0yHH8+PHjSkxMVFRUlGMsKChIDRs2VFxcnFtrpjEHAACAabxsNlMesbGxCgoKcnrExsZmq+fYsWNasGCBKleurM8//1z//Oc/NXToUC1btkySlJiYKEkqVaqU0+tKlSrlOOYuLGUBAABAgTN+/HiNHDnSacxut2ebl5WVpQYNGmjGjBmSpHr16unHH3/UwoULFRMTY0qt15CYAwAAwDRm7cpit9sVGBjo9MipMS9durSqV6/uNFatWjWdOnVKkhQWFiZJOnv2rNOcs2fPOo657d649WwAAADAbaRx48ZKSEhwGjt8+LAiIiIk/flB0LCwMG3evNlx/OLFi9q5c6ciIyPdWgtLWQAAAGAaq+3KMmLECDVq1EgzZsxQ9+7d9d///ldvvfWW3nrrLUl/flPp8OHDNW3aNFWuXFnly5fXhAkTFB4eri5duri1FhpzAAAAFFr33XefPv74Y40fP15Tp05V+fLlNWfOHEVHRzvmjB07VqmpqXr66aeVnJysJk2aaOPGjfL19XVrLTbDMAy3ntEC0q56ugIAkFrN+8bTJQCAto9s7OkSnLz+zQlTrjOocTlTruNOrDEHAAAALIClLAAAADCN1daYWwmJOQAAAGABJOYAAAAwjReJuUsk5gAAAIAFkJgDAADANF4sMneJxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgGtaYu0ZiDgAAAFgAiTkAAABMQ2DuGok5AAAAYAEk5gAAADANqbBr3BsAAADAAkjMAQAAYBobi8xdIjEHAAAALIDEHAAAAKYhL3eNxBwAAACwABJzAAAAmIZv/nSNxBwAAACwABJzAAAAmIa83DUScwAAAMACSMwBAABgGpaYu0ZiDgAAAFgAiTkAAABMwzd/ukZiDgAAAFgAiTkAAABMQyrsGvcGAAAAsAAScwAAAJiGNeaukZgDAAAAFkBiDgAAANOQl7tGYg4AAABYAIk5AAAATMMac9dIzAEAAAALIDEHAACAaUiFXePeAAAAABZAYg4AAADTsMbcNRJzAAAAwAJIzAEAAGAa8nLXSMwBAAAACyAxBwAAgGlYYu4aiTkAAABgASTmAAAAMI0Xq8xdIjEHAAAALIDEHAAAAKZhjblrJOYAAACABZCYAwAAwDQ21pi7RGIOAAAAWACJOQAAAEzDGnPXSMwBAAAACyAxBwAAgGnYx9w1EnMAAADAAkjMAQAAYBrWmLtGYg4AAABYAIk5AAAATENi7hqJOQAAAGABJOYAAAAwDd/86RqJOQAAAGABJOYAAAAwjReBuUsk5gAAAIAF0JgDAADANDaT/rtZL774omw2m4YPH+4YS0tL06BBg1SiRAkFBASoa9euOnv2rBvuhjMacwAAAEDSrl279Oabb6p27dpO4yNGjNC6deu0evVqffXVV/r111/1yCOPuP36lmrMr1y5ooSEBF29etXTpQAAACAf2GzmPPIqJSVF0dHRWrRokYoXL+4Yv3DhghYvXqxXX31VDz74oOrXr68lS5bo22+/1XfffefGO2ORxvzSpUvq27ev7rjjDtWoUUOnTp2SJA0ZMkQvvviih6sDAADA7SY9PV0XL150eqSnp7ucP2jQILVv315RUVFO43v27FFGRobTeNWqVVW2bFnFxcW5tWZLNObjx4/X/v37tW3bNvn6+jrGo6Ki9P7773uwMgAAALiTWWvMY2NjFRQU5PSIjY3Nsab33ntPe/fuzfF4YmKifHx8FBwc7DReqlQpJSYmuvXeWGK7xLVr1+r999/XP/7xD9n+8m8PNWrU0NGjRz1YGQAAAG5H48eP18iRI53G7HZ7tnk///yzhg0bpk2bNjkFxJ5gicb8t99+U2hoaLbx1NRUp0YdAAAAtzez9jG32+05NuJ/t2fPHp07d0733nuvYywzM1Pbt2/Xa6+9ps8//1xXrlxRcnKyU2p+9uxZhYWFubVmSyxladCggTZs2OB4fq0Zf/vttxUZGempsgAAAFDAtWzZUj/88IP27dvneDRo0EDR0dGOXxctWlSbN292vCYhIUGnTp1ye59qicR8xowZatu2rQ4ePKirV69q7ty5OnjwoL799lt99dVXni4PAAAAbnIre4znh2LFiqlmzZpOY/7+/ipRooRjvG/fvho5cqRCQkIUGBioIUOGKDIyUv/4xz/cWoslEvMmTZpo3759unr1qmrVqqUvvvhCoaGhiouLU/369T1dHgAAAAqx2bNnq0OHDuratauaNm2qsLAwffTRR26/js0wDMPtZ/WwNLZBx03as3uXlr6zWPEHf9Rvv/2m2fNe14Mto278QiAHreZ94+kScBvwK+qtfo3L6oFKISp+R1H9dC5V87Ye16GzKZKkPpFl9GCVOxVazK6rmYYSzqZo0TcnFZ+Y4uHKcbvYPrKxp0twsuOn86Zcp0nl4jeeZDGWSMz37t2rH374wfH8P//5j7p06aJ//etfunLligcrQ2Fz+fIlValSReOfn+TpUgAUEs+2qqQGZYM1/bOf1Hv5Pu06maxXu9XQnQE+kqSfz1/WnC3H1Hv59xr0/v+UeDFdr3StoSA/S6xGBeBGlmjMn3nmGR0+fFiSdOzYMT322GO64447tHr1ao0dO9bD1aEwafJAMw0eNkItox7ydCkACgGfIl5qWrmEFnx9QvtPX9Tp5DQtiftZp5PT1KX2n7s9fHno/7Tn1AWduZCuE79f1mtfHVeAvYgq3unv4eqBm2Mz6XE7skRjfvjwYdWtW1eStHr1ajVr1kyrVq3S0qVL9eGHH3q2OAAA8om3zaYiXjZduZrlNJ5+NUu17grMNr+Il02dapXSH2lXdfS3VLPKBGASS/w7mGEYysr68y+lL7/8Uh06dJAklSlTRv/3f//nydIAAMg3lzMy9eOvFxXzjzI6mXRZ5y9dUcuqJVWjdDGdTk5zzIssX1yT2leRb1Ev/Z56RaM+PKALfKAKtykvvqPGJUs05g0aNNC0adMUFRWlr776SgsWLJAkHT9+XKVKlbrua9PT05Wenu40ZnjnbkN5AAA8bdpnP2lc60r6+Jn7dDXL0E/nUrQ54TdVCQ1wzPn+5wvq++4+BfkVUcdaYZrSoYqeWfU/JV/O8GDlANzNEktZ5syZo71792rw4MF67rnnVKlSJUnSmjVr1KhRo+u+NjY2VkFBQU6Pl1+KNaNsAABu2a8X0jT0gx/Val6cHl20S8+s+p+KeHnp1wv/LzFPu5ql08lpOngmRS99cUSZWYba18z+jdnA7YA15q5ZIjGvXbu2064s17z88svy9va+7mvHjx+vkSNHOo0Z3qTlAIDbS9rVLKVdzVKA3Vv3RQRr4dcnXM612f784CiAgsUSjbkrvr6+N5xjt2dftsKyO9ysS6mpOnXqlOP56V9+0aH4eAUFBal0eLgHKwNQUN0XESybTfo56bLuCvbVP5uW06nzl/XpgXPyLeKlJxrerW+OJen3lAwF+RXRw3VL684Au7Ye5jNYuE3drnG2CTzWmBcvXly2XC7+T0pKyudqgD8dOPCj+vV50vF81sw/l0V16vywXpjxoqfKAlCABdi99XSTCJUMsOuPtKv66sjvWrTjpDKzDHnbpIiQO9SmRqiCfIvqYtpVHUr8Q0Pe/0Enfr/s6dIBuJnHGvM5c+Z46tKAS/fd31D7DyR4ugwAhcjWw79r6+Hfczx2JdPQ8+sOmVwRkL9sROYueawxj4mJ8dSlAQAAAMux3BrztLQ0XblyxWksMDD7lywAAADg9sM25q5Z4iPdqampGjx4sEJDQ+Xv76/ixYs7PQAAAICCzhKN+dixY7VlyxYtWLBAdrtdb7/9tqZMmaLw8HAtX77c0+UBAADATdjH3DVLLGVZt26dli9frubNm6tPnz564IEHVKlSJUVERGjlypWKjo72dIkAAABAvrJEYp6UlKQKFSpI+nM9+bXtEZs0aaLt27d7sjQAAAC4E5G5S5ZozCtUqKDjx49LkqpWraoPPvhA0p9JenBwsAcrAwAAAMzh0cb82LFjysrKUp8+fbR//35J0rhx4/T666/L19dXI0aM0JgxYzxZIgAAANzIZtJ/tyOPrjGvXLmyzpw5oxEjRkiSHnvsMc2bN0+HDh3Snj17VKlSJdWuXduTJQIAAACm8GhjbhiG0/NPP/1UsbGxqlChgiIiIjxUFQAAAPIL+5i7Zok15gAAAEBh59HE3Gazyfa3H5v+/hwAAAAFB52eax5fytK7d2/Z7XZJUlpamgYMGCB/f3+neR999JEnygMAAABM49HGPCYmxul5r169PFQJAAAATEFk7pJHG/MlS5Z48vIAAACAZXi0MQcAAEDhcrvuMW4GdmUBAAAALIDEHAAAAKZhAz7XSMwBAAAACyAxBwAAgGkIzF0jMQcAAAAsgMQcAAAA5iEyd4nEHAAAALAAEnMAAACYhn3MXSMxBwAAACyAxBwAAACmYR9z10jMAQAAAAsgMQcAAIBpCMxdIzEHAAAALIDEHAAAAOYhMneJxBwAAACwABJzAAAAmIZ9zF0jMQcAAAAsgMQcAAAApmEfc9dIzAEAAAALIDEHAACAaQjMXSMxBwAAACyAxBwAAADmITJ3icQcAAAAsAAScwAAAJiGfcxdIzEHAAAALIDEHAAAAKZhH3PXSMwBAAAACyAxBwAAgGkIzF0jMQcAAAAsgMQcAAAA5iEyd4nEHAAAALAAEnMAAACYhn3MXSMxBwAAACyAxBwAAACmYR9z10jMAQAAAAugMQcAAIBpbCY9cis2Nlb33XefihUrptDQUHXp0kUJCQlOc9LS0jRo0CCVKFFCAQEB6tq1q86ePXtT7/96aMwBAABQaH311VcaNGiQvvvuO23atEkZGRlq1aqVUlNTHXNGjBihdevWafXq1frqq6/066+/6pFHHnF7LTbDMAy3n9XD0q56ugIAkFrN+8bTJQCAto9s7OkSnBz97bIp16lY0u+mXvfbb78pNDRUX331lZo2baoLFy6oZMmSWrVqlbp16yZJOnTokKpVq6a4uDj94x//cFvNJOYAAAAocNLT03Xx4kWnR3p6+g1fd+HCBUlSSEiIJGnPnj3KyMhQVFSUY07VqlVVtmxZxcXFubVmGnMAAACYxmbSf7GxsQoKCnJ6xMbGXre2rKwsDR8+XI0bN1bNmjUlSYmJifLx8VFwcLDT3FKlSikxMdGt94btEgEAAFDgjB8/XiNHjnQas9vt133NoEGD9OOPP2rHjh35WZpLNOYAAAAwjVn7mNvt9hs24n81ePBgrV+/Xtu3b9fdd9/tGA8LC9OVK1eUnJzslJqfPXtWYWFh7iyZpSwAAAAovAzD0ODBg/Xxxx9ry5YtKl++vNPx+vXrq2jRotq8ebNjLCEhQadOnVJkZKRbayExBwAAgGms9sWfgwYN0qpVq/Sf//xHxYoVc6wbDwoKkp+fn4KCgtS3b1+NHDlSISEhCgwM1JAhQxQZGenWHVkkGnMAAAAUYgsWLJAkNW/e3Gl8yZIl6t27tyRp9uzZ8vLyUteuXZWenq7WrVvrjTfecHst7GMOAPmEfcwBWIHV9jE/8XuaKdcpV8LXlOu4E2vMAQAAAAtgKQsAAABMY7PcKnPrIDEHAAAALIDEHAAAAKYxax/z2xGJOQAAAGABJOYAAAAwDYG5ayTmAAAAgAWQmAMAAMA0rDF3jcQcAAAAsAAScwAAAJiIyNwVEnMAAADAAkjMAQAAYBrWmLtGYg4AAABYAIk5AAAATENg7hqJOQAAAGABNOYAAACABbCUBQAAAKbhw5+ukZgDAAAAFkBiDgAAANPY+PinSyTmAAAAgAWQmAMAAMA8BOYukZgDAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAATMM+5q6RmAMAAAAWQGIOAAAA07CPuWsk5gAAAIAFkJgDAADAPATmLpGYAwAAABZAYg4AAADTEJi7RmIOAAAAWACJOQAAAEzDPuaukZgDAAAAFkBiDgAAANOwj7lrJOYAAACABZCYAwAAwDSsMXeNxBwAAACwABpzAAAAwAJozAEAAAALYI05AAAATMMac9dIzAEAAAALIDEHAACAadjH3DUScwAAAMACSMwBAABgGtaYu0ZiDgAAAFgAiTkAAABMQ2DuGok5AAAAYAEk5gAAADAPkblLJOYAAACABZCYAwAAwDTsY+4aiTkAAABgASTmAAAAMA37mLtGYg4AAABYAIk5AAAATENg7hqJOQAAAGABJOYAAAAwD5G5SyTmAAAAgAWQmAMAAMA07GPuGok5AAAAYAEk5gAAADAN+5i7RmIOAAAAWIDNMAzD00UAVpOenq7Y2FiNHz9edrvd0+UAKIT4ewgofGjMgRxcvHhRQUFBunDhggIDAz1dDoBCiL+HgMKHpSwAAACABdCYAwAAABZAYw4AAABYAI05kAO73a5JkybxgSsAHsPfQ0Dhw4c/AQAAAAsgMQcAAAAsgMYcAAAAsAAacwAALGbp0qUKDg72dBkATEZjjgLNZrNd9zF58mRPlwigAOvdu3eOf/ccOXLE06UBsKAini4AyE9nzpxx/Pr999/XxIkTlZCQ4BgLCAhw/NowDGVmZqpIEf5nAcB92rRpoyVLljiNlSxZ0kPVALAyEnMUaGFhYY5HUFCQbDab4/mhQ4dUrFgxffbZZ6pfv77sdrt27Nih3r17q0uXLk7nGT58uJo3b+54npWVpdjYWJUvX15+fn6qU6eO1qxZY+6bA3BbsNvtTn8XhYWFae7cuapVq5b8/f1VpkwZDRw4UCkpKS7PsX//frVo0ULFihVTYGCg6tevr927dzuO79ixQw888ID8/PxUpkwZDR06VKmpqWa8PQBuRGOOQm/cuHF68cUXFR8fr9q1a+fqNbGxsVq+fLkWLlyoAwcOaMSIEerVq5e++uqrfK4WQEHg5eWlefPm6cCBA1q2bJm2bNmisWPHupwfHR2tu+++W7t27dKePXs0btw4FS1aVJJ09OhRtWnTRl27dtX//vc/vf/++9qxY4cGDx5s1tsB4Cb8mz0KvalTp+qhhx7K9fz09HTNmDFDX375pSIjIyVJFSpU0I4dO/Tmm2+qWbNm+VUqgNvQ+vXrnZbNtW3bVqtXr3Y8L1eunKZNm6YBAwbojTfeyPEcp06d0pgxY1S1alVJUuXKlR3HYmNjFR0dreHDhzuOzZs3T82aNdOCBQvk6+ubD+8KQH6gMUeh16BBgzzNP3LkiC5dupStmb9y5Yrq1avnztIAFAAtWrTQggULHM/9/f315ZdfKjY2VocOHdLFixd19epVpaWl6dKlS7rjjjuynWPkyJHq16+fVqxYoaioKD366KOqWLGipD+Xufzvf//TypUrHfMNw1BWVpaOHz+uatWq5f+bBOAWNOYo9Pz9/Z2ee3l56e9fiJuRkeH49bV1oBs2bNBdd93lNI+vzgbwd/7+/qpUqZLj+YkTJ9ShQwf985//1PTp0xUSEqIdO3aob9++unLlSo6N+eTJk/X4449rw4YN+uyzzzRp0iS99957evjhh5WSkqJnnnlGQ4cOzfa6smXL5ut7A+BeNObA35QsWVI//vij09i+ffsc6zmrV68uu92uU6dOsWwFQJ7t2bNHWVlZeuWVV+Tl9edHvT744IMbvu6ee+7RPffcoxEjRqhnz55asmSJHn74Yd177706ePCgU/MP4PbEhz+Bv3nwwQe1e/duLV++XD/99JMmTZrk1KgXK1ZMo0eP1ogRI7Rs2TIdPXpUe/fu1fz587Vs2TIPVg7gdlCpUiVlZGRo/vz5OnbsmFasWKGFCxe6nH/58mUNHjxY27Zt08mTJ/XNN99o165djiUqzz77rL799lsNHjxY+/bt008//aT//Oc/fPgTuA3RmAN/07p1a02YMEFjx47Vfffdpz/++ENPPvmk05wXXnhBEyZMUGxsrKpVq6Y2bdpow4YNKl++vIeqBnC7qFOnjl599VW99NJLqlmzplauXKnY2FiX8729vfX777/rySef1D333KPu3burbdu2mjJliiSpdu3a+uqrr3T48GE98MADqlevniZOnKjw8HCz3hIAN7EZf19MCwAAAMB0JOYAAACABdCYAwAAABZAYw4AAABYAI05AAAAYAE05gAAAIAF0JgDAAAAFkBjDgAAAFgAjTkAAABgATTmAAqt3r17q0uXLo7nzZs31/Dhw02vY9u2bbLZbEpOTnY5x2azae3atbk+5+TJk1W3bt1bquvEiROy2Wzat2/fLZ0HAJA7NOYALKV3796y2Wyy2Wzy8fFRpUqVNHXqVF29ejXfr/3RRx/phRdeyNXc3DTTAADkRRFPFwAAf9emTRstWbJE6enp+vTTTzVo0CAVLVpU48ePzzb3ypUr8vHxcct1Q0JC3HIeAABuBok5AMux2+0KCwtTRESE/vnPfyoqKkqffPKJpP+3/GT69OkKDw9XlSpVJEk///yzunfvruDgYIWEhKhz5846ceKE45yZmZkaOXKkgoODVaJECY0dO1aGYThd9+9LWdLT0/Xss8+qTJkystvtqlSpkhYvXqwTJ06oRYsWkqTixYvLZrOpd+/ekqSsrCzFxsaqfPny8vPzU506dbRmzRqn63z66ae655575OfnpxYtWjjVmVvPPvus7rnnHt1xxx2qUKGCJkyYoIyMjGzz3nzzTZUpU0Z33HGHunfvrgsXLjgdf/vtt1WtWjX5+vqqatWqeuONN/JcCwDAPWjMAVien5+frly54ni+efNmJSQkaNOmTVq/fr0yMjLUunVrFStWTF9//bW++eYbBQQEqE2bNo7XvfLKK1q6dKneeecd7dixQ0lJSfr444+ve90nn3xS//73vzVv3jzFx8frzTffVEBAgMqUKaMPP/xQkpSQkKAzZ85o7ty5kqTY2FgtX75cCxcu1IEDBzRixAj16tVLX331laQ/f4B45JFH1LFjR+3bt0/9+vXTuHHj8nxPihUrpqVLl+rgwYOaO3euFi1apNmzZzvNOXLkiD744AOtW7dOGzdu1Pfff6+BAwc6jq9cuVITJ07U9OnTFR8frxkzZmjChAlatmxZnusBALiBAQAWEhMTY3Tu3NkwDMPIysoyNm3aZNjtdmP06NGO46VKlTLS09Mdr1mxYoVRpUoVIysryzGWnp5u+Pn5GZ9//rlhGIZRunRpY+bMmY7jGRkZxt133+24lmEYRrNmzYxhw4YZhmEYCQkJhiRj06ZNOda5detWQ5Jx/vx5x1haWppxxx13GN9++63T3L59+xo9e/Y0DMMwxo8fb1SvXt3p+LPPPpvtXH8nyfj4449dHn/55ZeN+vXrO55PmjTJ8Pb2Nn755RfH2GeffWZ4eXkZZ86cMQzDMCpWrGisWrXK6TwvvPCCERkZaRiGYRw/ftyQZHz//fcurwsAcB/WmAOwnPXr1ysgIEAZGRnKysrS448/rsmTJzuO16pVy2ld+f79+3XkyBEVK1bM6TxpaWk6evSoLly4oDNnzqhhw4aOY0WKFFGDBg2yLWe5Zt++ffL29lazZs1yXfeRI0d06dIlPfTQQ07jV65cUb169SRJ8fHxTnVIUmRkZK6vcc3777+vefPm6ejRo0pJSdHVq1cVGBjoNKds2bK66667nK6TlZWlhIQEFStWTEePHlXfvn3Vv39/x5yrV68qKCgoz/UAAG4djTkAy2nRooUWLFggHx8fhYeHq0gR57+q/P39nZ6npKSofv36WrlyZbZzlSxZ8qZq8PPzy/NrUlJSJEkbNmxwaoilP9fNu0tcXJyio6M1ZcoUtW7dWkFBQXrvvff0yiuv5LnWRYsWZftBwdvb2221AgByj8YcgOX4+/urUqVKuZ5/77336v3331doaGi21Pia0qVLa+fOnWratKmkP5PhPXv26N57781xfq1atZSVlaWvvvpKUVFR2Y5fS+wzMzMdY9WrV5fdbtepU6dcJu3VqlVzfJD1mu++++7Gb/Ivvv32W0VEROi5555zjJ08eTLbvFOnTunXX39VeHi44zpeXl6qUqWKSpUqpfDwcB07dkzR0dF5uj4AIH/w4U8At73o6Gjdeeed6ty5s77++msdP35c27Zt09ChQ/XLL79IkoYNG6YXX3xRa9eu1aFDhzRw4MDr7kFerlw5xcTE6KmnntLatWsd5/zggw8kSREREbLZbFq/fr1+++03paSkqFixYho9erRGjBihZcuW6ejRo9q7d6/mz5/v+EDlgAED9NNPP2nMmDFKSEjQqlWrtHTp0jy938qVK+vUqVN67733dPToUc2bNy/HD7L6+voqJiZG+/fv19dff62hQ4eqe/fuCgsLkyRNmTJFsbGxmjdvng4fPqwffvhBS5Ys0auvvpqnegAA7kFjDuC2d8cdd2j79u0qW7asHnnkEVWrVk19+/ZVWlqaI0EfNWqUnnjiCcXExCgyMlLFihXTww8/fN3zLliwQN26ddPAgQNVtWpV9e/fX6mpqZKku+66S1OmTNG4ceNUqlQpDR48WJL0wgsvaMKECYqNjVW1atXUpk0bbdiwQeXLl5f057rvDz/8UGvXrlWdOnW0cOFCzZgxI0/vt1OnThoxYoQGDx6sunXr6ttvv9WECROyzatUqZIeeeQRtWvXTq1atVLt2rWdtkPs16+f3n77bS1ZskS1atVSs2bNtHTpUketAABz2QxXn3wCAAAAYBoScwAAAMACaMwBAAAAC6AxBwAAACyAxhwAAACwABpzAAAAwAJozAEAAAALoDEHAAAALIDGHAAAALAAGnMAAADAAmjMAQAAAAugMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBAAAAC6AxBwAAACyAxhwAAACwABpzAAAAwAJozIFCqFy5curdu7enyyhwdu3apUaNGsnf3182m0379u1z6/m3bdsmm82mbdu2ufW8tzP+LAMoSGjMgQLk6NGjeuaZZ1ShQgX5+voqMDBQjRs31ty5c3X58mVPl3ddu3bt0uDBg1WjRg35+/urbNmy6t69uw4fPpyn8+zbt0+9evVSmTJlZLfbFRISoqioKC1ZskSZmZn5VL2UkZGhRx99VElJSZo9e7ZWrFihiIiIfLue2Zo3by6bzabKlSvneHzTpk2y2Wyy2Wxas2ZNns9/8OBBTZ48WSdOnLjFSgHg9lXE0wUAcI8NGzbo0Ucfld1u15NPPqmaNWvqypUr2rFjh8aMGaMDBw7orbfe8nSZLr300kv65ptv9Oijj6p27dpKTEzUa6+9pnvvvVffffedatasecNzvP322xowYIBKlSqlJ554QpUrV9Yff/yhzZs3q2/fvjpz5oz+9a9/5Uv9R48e1cmTJ7Vo0SL169cvX67RtGlTXb58WT4+Pvly/hvx9fXVkSNH9N///lf333+/07GVK1fK19dXaWlpN3XugwcPasqUKWrevLnKlSuX69clJCTIy4uMCUDBQGMOFADHjx9Xjx49FBERoS1btqh06dKOY4MGDdKRI0e0YcMGD1Z4YyNHjtSqVaucms7HHntMtWrV0osvvqh33333uq//7rvvNGDAAEVGRurTTz9VsWLFHMeGDx+u3bt368cff8y3+s+dOydJCg4OzrdreHl5ydfXN9/OfyMVK1bU1atX9e9//9upMU9LS9PHH3+s9u3b68MPP8z3OgzDUFpamvz8/GS32/P9egBgFmIGoACYOXOmUlJStHjxYqem/JpKlSpp2LBhLl+flJSk0aNHq1atWgoICFBgYKDatm2r/fv3Z5s7f/581ahRQ3fccYeKFy+uBg0aaNWqVY7jf/zxh4YPH65y5crJbrcrNDRUDz30kPbu3Xvd99CoUaNsSXDlypVVo0YNxcfH3+gWaMqUKbLZbFq5cqVTU35NgwYNnNYip6amatSoUY4lL1WqVNGsWbNkGIbT62w2mwYPHqy1a9eqZs2astvtqlGjhjZu3OiY07t3bzVr1kyS9Oijj8pms6l58+aS/lwCcu3Xf9W7d+9syfB7772n+vXrq1ixYgoMDFStWrU0d+5cx3FXa8xXr16t+vXry8/PT3feead69eql06dPZ7teQECATp8+rS5duiggIEAlS5bU6NGj87TEp2fPnnr//feVlZXlGFu3bp0uXbqk7t27Z5t/8uRJDRw4UFWqVJGfn59KlCihRx991GnJytKlS/Xoo49Kklq0aOFYEnPtfZYrV04dOnTQ559/rgYNGsjPz09vvvmm49i131fDMNSiRQuVLFnS8YOSJF25ckW1atVSxYoVlZqamuv3CgBmozEHCoB169apQoUKatSo0U29/tixY1q7dq06dOigV199VWPGjNEPP/ygZs2a6ddff3XMW7RokYYOHarq1atrzpw5mjJliurWraudO3c65gwYMEALFixQ165d9cYbb2j06NHy8/PLVXP9d4Zh6OzZs7rzzjuvO+/SpUvavHmzmjZtqrJly+bqvJ06ddLs2bPVpk0bvfrqq6pSpYrGjBmjkSNHZpu/Y8cODRw4UD169NDMmTOVlpamrl276vfff5ckPfPMM44lMkOHDtWKFSv03HPP5em9btq0ST179lTx4sX10ksv6cUXX1Tz5s31zTffXPd1S5cuVffu3eXt7a3Y2Fj1799fH330kZo0aaLk5GSnuZmZmWrdurVKlCihWbNmqVmzZnrllVfytMTp8ccf15kzZ5x+OFi1apVatmyp0NDQbPN37dqlb7/9Vj169NC8efM0YMAAbd68Wc2bN9elS5ck/blEZ+jQoZKkf/3rX1qxYoVWrFihatWqOc6TkJCgnj176qGHHtLcuXNVt27dbNey2Wx65513lJaWpgEDBjjGJ02apAMHDmjJkiXy9/fP9XsFANMZAG5rFy5cMCQZnTt3zvVrIiIijJiYGMfztLQ0IzMz02nO8ePHDbvdbkydOtUx1rlzZ6NGjRrXPXdQUJAxaNCgXNdyPStWrDAkGYsXL77uvP379xuSjGHDhuXqvGvXrjUkGdOmTXMa79atm2Gz2YwjR444xiQZPj4+TmPXrjd//nzH2NatWw1JxurVq53O2axZM6NZs2bZaoiJiTEiIiIcz4cNG2YEBgYaV69edVn3tWts3brVMAzDuHLlihEaGmrUrFnTuHz5smPe+vXrDUnGxIkTna4nyen30zAMo169ekb9+vVdXvOv7+Pa732DBg2Mvn37GoZhGOfPnzd8fHyMZcuW5XgPLl26lO1ccXFxhiRj+fLljrHVq1c7vbe/ioiIMCQZGzduzPHYX/8sG4ZhvPnmm4Yk49133zW+++47w9vb2xg+fPgN3yMAeBqJOXCbu3jxoiTluHwjt+x2u+MDdJmZmfr9998VEBCgKlWqOC1BCQ4O1i+//KJdu3a5PFdwcLB27tzplLTfjEOHDmnQoEGKjIxUTEzMdefm9R58+umn8vb2dqS014waNUqGYeizzz5zGo+KilLFihUdz2vXrq3AwEAdO3YsV9fLjeDgYKWmpmrTpk25fs3u3bt17tw5DRw40Gntefv27VW1atUcP1fw1yRZkh544IE8v4/HH39cH330ka5cuaI1a9bI29tbDz/8cI5z/fz8HL/OyMjQ77//rkqVKik4OPiGy5v+qnz58mrdunWu5j799NNq3bq1hgwZoieeeEIVK1bUjBkzcn0tAPAUGnPgNhcYGCjpz7XdNysrK0uzZ89W5cqVZbfbdeedd6pkyZL63//+pwsXLjjmPfvsswoICND999+vypUra9CgQdmWWsycOVM//vijypQpo/vvv1+TJ0/Oc+OXmJio9u3bKygoyNH4XU9e78HJkycVHh6erZG/tnTi5MmTTuM5LY8pXry4zp8/n6vr5cbAgQN1zz33qG3btrr77rv11FNPOa1jz8m1OqtUqZLtWNWqVbO9D19fX5UsWdJp7GbeR48ePXThwgV99tlnWrlypTp06ODyh6LLly9r4sSJjrX81/5sJScnO/3ZupHy5cvnqcbFixfr0qVL+umnn7R06VKnHxAAwKpozIHbXGBgoMLDw29px5EZM2Zo5MiRatq0qd599119/vnn2rRpk2rUqOH0Ib9q1aopISFB7733npo0aaIPP/xQTZo00aRJkxxzunfvrmPHjmn+/PkKDw/Xyy+/rBo1amRLoV25cOGC2rZtq+TkZG3cuFHh4eE3fE2lSpVUpEgR/fDDD3l/87ng6gcD428fFM2JzWbLcfzvH7gMDQ3Vvn379Mknn6hTp07aunWr2rZte8N/LciLG/2Ak1ulS5dW8+bN9corr2j79u16/PHHXc4dMmSIpk+fru7du+uDDz7QF198oU2bNqlEiRJOf7ZuJK+N9bZt25Seni5J+fbnAgDcjcYcKAA6dOigo0ePKi4u7qZev2bNGrVo0UKLFy9Wjx491KpVK0VFRWX78KAk+fv767HHHtOSJUt06tQptW/fXtOnT3fav7p06dIaOHCg1q5dq+PHj6tEiRKaPn36DetIS0tTx44ddfjwYa1fv17Vq1fPVf133HGHHnzwQW3fvl0///zzDedHRETo119/zZawHzp0yHHcXYoXL57jffx7mi1JPj4+6tixo9544w3Hl0UtX75cR44cyfHc1+pMSEjIdiwhISFfv+Do8ccf19dff63AwEC1a9fO5bw1a9YoJiZGr7zyirp166aHHnooxw+muvoB5macOXNGQ4YMUatWrdShQweNHj06x/sNAFZDYw4UAGPHjpW/v7/69euns2fPZjt+9OhRp233/s7b2ztb+rt69epsW+5d24XkGh8fH1WvXl2GYSgjI0OZmZnZlieEhoYqPDzckV66kpmZqccee0xxcXFavXq1IiMjrzv/7yZNmiTDMPTEE08oJSUl2/E9e/Zo2bJlkqR27dopMzNTr732mtOc2bNny2azqW3btnm69vVUrFhRhw4d0m+//eYY279/f7YlQH+/t15eXqpdu7Ykubx3DRo0UGhoqBYuXOg057PPPlN8fLzat2/vrreRTbdu3TRp0iS98cYb1/3Co5z+bM2fPz/bvxhc2y0lpx9i8qp///7KysrS4sWL9dZbb6lIkSLq27dvrv6FAwA8iS8YAgqAihUratWqVXrsscdUrVo1p2/+/Pbbb7V69WqnPbz/rkOHDpo6dar69OmjRo0a6YcfftDKlStVoUIFp3mtWrVSWFiYGjdurFKlSik+Pl6vvfaa2rdvr2LFiik5OVl33323unXrpjp16iggIEBffvmldu3apVdeeeW672HUqFH65JNP1LFjRyUlJWX7QqFevXpd9/WNGjXS66+/roEDB6pq1apO3/y5bds2ffLJJ5o2bZokqWPHjmrRooWee+45nThxQnXq1NEXX3yh//znPxo+fLjTBz1v1VNPPaVXX31VrVu3Vt++fXXu3DktXLhQNWrUcHxoVZL69eunpKQkPfjgg7r77rt18uRJzZ8/X3Xr1nXaNvCvihYtqpdeekl9+vRRs2bN1LNnT509e1Zz585VuXLlNGLECLe9j78LCgrS5MmTbzivQ4cOWrFihYKCglS9enXFxcXpyy+/VIkSJZzm1a1bV97e3nrppZd04cIF2e12PfjggzluwXg9S5Ys0YYNG7R06VLdfffdkv78QaBXr15asGCBBg4cmKfzAYCpPLgjDAA3O3z4sNG/f3+jXLlyho+Pj1GsWDGjcePGxvz58420tDTHvJy2Sxw1apRRunRpw8/Pz2jcuLERFxeXbau/N99802jatKlRokQJw263GxUrVjTGjBljXLhwwTAMw0hPTzfGjBlj1KlTxyhWrJjh7+9v1KlTx3jjjTduWHuzZs0MSS4fubVnzx7j8ccfN8LDw42iRYsaxYsXN1q2bGksW7bMaUvIP/74wxgxYoRjXuXKlY2XX37ZyMrKcjqfpBy3f/z7PXS1XaJhGMa7775rVKhQwfDx8THq1q1rfP7559m2S1yzZo3RqlUrIzQ01PDx8THKli1rPPPMM8aZM2eyXePvWwq+//77Rr169Qy73W6EhIQY0dHRxi+//OI0JyYmxvD3989W26RJk3J1f/+6XaIrOd2D8+fPG3369DHuvPNOIyAgwGjdurVx6NChHLc5XLRokVGhQgXD29vb6X1GREQY7du3z/Gafz3Pzz//bAQFBRkdO3bMNu/hhx82/P39jWPHjt3wvQKAp9gMg3/bAwAAADyNNeYAAACABdCYAwAAABZAYw4AAABYAI05AAAAYAE05gAAAIAF0JgDAAAAFkBjDgAAgEJt+/bt6tixo8LDw2Wz2bR27dpsc+Lj49WpUycFBQXJ399f9913n06dOuU4npaWpkGDBqlEiRIKCAhQ165dc/w27uspkN/86VdvsKdLAACd3/Wap0sAAPlarNszq0+7/H3u/w5OTU1VnTp19NRTT+mRRx7Jdvzo0aNq0qSJ+vbtqylTpigwMFAHDhyQr6+vY86IESO0YcMGrV69WkFBQRo8eLAeeeQRffPNN7muo0B+wRCNOQAroDEHYAU05nljs9n08ccfq0uXLo6xHj16qGjRolqxYkWOr7lw4YJKliypVatWqVu3bpKkQ4cOqVq1aoqLi9M//vGPXF2bpSwAAAAwj83LnIebZGVlacOGDbrnnnvUunVrhYaGqmHDhk7LXfbs2aOMjAxFRUU5xqpWraqyZcsqLi4u19eiMQcAAECBk56erosXLzo90tPT83yec+fOKSUlRS+++KLatGmjL774Qg8//LAeeeQRffXVV5KkxMRE+fj4KDg42Om1pUqVUmJiYq6vRWMOAAAA89hspjxiY2MVFBTk9IiNjc1zuVlZWZKkzp07a8SIEapbt67GjRunDh06aOHChW69NRZbdQQAAADcuvHjx2vkyJFOY3a7Pc/nufPOO1WkSBFVr17dabxatWrasWOHJCksLExXrlxRcnKyU2p+9uxZhYWF5fpaJOYAAAAwj0lrzO12uwIDA50eN9OY+/j46L777lNCQoLT+OHDhxURESFJql+/vooWLarNmzc7jickJOjUqVOKjIzM9bVIzAEAAFCopaSk6MiRI47nx48f1759+xQSEqKyZctqzJgxeuyxx9S0aVO1aNFCGzdu1Lp167Rt2zZJUlBQkPr27auRI0cqJCREgYGBGjJkiCIjI3O9I4tEYw4AAAAz2WyeriCb3bt3q0WLFo7n15bAxMTEaOnSpXr44Ye1cOFCxcbGaujQoapSpYo+/PBDNWnSxPGa2bNny8vLS127dlV6erpat26tN954I091sI85AOQT9jEHYAWW28f8vpE3nuQGl3e9asp13Mliv1UAAAAo0Ny4x3hBw50BAAAALIDEHAAAAOax4BpzqyAxBwAAACyAxBwAAADmYY25S9wZAAAAwAJIzAEAAGAe1pi7RGIOAAAAWACJOQAAAMzDGnOXuDMAAACABZCYAwAAwDysMXeJxBwAAACwABJzAAAAmIc15i5xZwAAAAALIDEHAACAeVhj7hKJOQAAAGABJOYAAAAwD2vMXeLOAAAAABZAYg4AAADzkJi7xJ0BAAAALIDEHAAAAObxYlcWV0jMAQAAAAsgMQcAAIB5WGPuEncGAAAAsAAScwAAAJiHb/50icQcAAAAsAAacwAAAMACWMoCAAAA8/DhT5e4MwAAAIAFkJgDAADAPHz40yUScwAAAMACSMwBAABgHtaYu8SdAQAAACyAxBwAAADmYY25SyTmAAAAgAWQmAMAAMA8rDF3iTsDAAAAWACJOQAAAMzDGnOXSMwBAAAACyAxBwAAgHlYY+4SdwYAAACwABJzAAAAmIc15i6RmAMAAAAWQGIOAAAA87DG3CXuDAAAAGABJOYAAAAwD4m5S9wZAAAAwAJIzAEAAGAedmVxicQcAAAAsAAScwAAAJiHNeYucWcAAAAACyAxBwAAgHlYY+4SiTkAAABgATTmAAAAMI/Ny5xHHmzfvl0dO3ZUeHi4bDab1q5d63LugAEDZLPZNGfOHKfxpKQkRUdHKzAwUMHBwerbt69SUlLyVAeNOQAAAAq11NRU1alTR6+//vp153388cf67rvvFB4enu1YdHS0Dhw4oE2bNmn9+vXavn27nn766TzVwRpzAAAAmMeCa8zbtm2rtm3bXnfO6dOnNWTIEH3++edq376907H4+Hht3LhRu3btUoMGDSRJ8+fPV7t27TRr1qwcG/mckJgDAAAA15GVlaUnnnhCY8aMUY0aNbIdj4uLU3BwsKMpl6SoqCh5eXlp586dub4OiTkAAABMYzMpMU9PT1d6errTmN1ul91uz/O5XnrpJRUpUkRDhw7N8XhiYqJCQ0OdxooUKaKQkBAlJibm+jok5gAAAChwYmNjFRQU5PSIjY3N83n27NmjuXPnaunSpfn+QwWJOQAAAExjVmI+fvx4jRw50mnsZtLyr7/+WufOnVPZsmUdY5mZmRo1apTmzJmjEydOKCwsTOfOnXN63dWrV5WUlKSwsLBcX4vGHAAAAAXOzS5b+bsnnnhCUVFRTmOtW7fWE088oT59+kiSIiMjlZycrD179qh+/fqSpC1btigrK0sNGzbM9bVozAEAAGAe623KopSUFB05csTx/Pjx49q3b59CQkJUtmxZlShRwml+0aJFFRYWpipVqkiSqlWrpjZt2qh///5auHChMjIyNHjwYPXo0SPXO7JIrDEHAABAIbd7927Vq1dP9erVkySNHDlS9erV08SJE3N9jpUrV6pq1apq2bKl2rVrpyZNmuitt97KUx0k5gAAADCNWWvM86J58+YyDCPX80+cOJFtLCQkRKtWrbqlOkjMAQAAAAsgMQcAAIBprJiYWwWJOQAAAGABJOYAAAAwDYm5ayTmAAAAgAWQmAMAAMA0JOaukZgDAAAAFkBiDgAAAPMQmLtEYg4AAABYAIk5AAAATMMac9dIzAEAAAALIDEHAACAaUjMXSMxBwAAACyAxBwAAACmITF3jcQcAAAAsAAScwAAAJiGxNw1EnMAAADAAkjMAQAAYB4Cc5dIzAEAAAALIDEHAACAaVhj7hqJOQAAAGABJOYAAAAwDYm5ayTmAAAAgAWQmAMAAMA0JOaukZgDAAAAFkBiDgAAAPMQmLtEYg4AAABYAIk5AAAATMMac9dIzAEAAAALIDEHAACAaUjMXSMxBwAAACyAxBwAAACmITF3jcQcAAAAsAAScwAAAJiGxNw1EnMAAADAAkjMAQAAYB4Cc5dIzAEAAAALIDEHAACAaVhj7hqJOQAAAGABJOYAAAAwDYm5ayTmAAAAgAWQmAMAAMA0JOauWSIx//rrr9WrVy9FRkbq9OnTkqQVK1Zox44dHq4MAAAAMIfHG/MPP/xQrVu3lp+fn77//nulp6dLki5cuKAZM2Z4uDoAAAC4lc2kx23I4435tGnTtHDhQi1atEhFixZ1jDdu3Fh79+71YGUAAACAeTy+xjwhIUFNmzbNNh4UFKTk5GTzCwIAAEC+YY25ax5PzMPCwnTkyJFs4zt27FCFChU8UBEAAABgPo835v3799ewYcO0c+dO2Ww2/frrr1q5cqVGjx6tf/7zn54uDwAAAG5ks9lMedyOPL6UZdy4ccrKylLLli116dIlNW3aVHa7XaNHj9aQIUM8XR4AAABgCo835jabTc8995zGjBmjI0eOKCUlRdWrV1dAQICnS0MB0/jeihrxZJTurV5WpUsGqfuIt7Ru2/+c5lQpX0rThnXRA/dWUpEiXjp0LFE9R7+tnxPPS5KeeqSxHmvbQHWr3q3AAD+FPTBGF1Iue+LtACig9uzepaXvLFb8wR/122+/afa81/VgyyhPlwW4ze2aZpvB40tZrvHx8VH16tV1//3305QjX/j72fXD4dMaHvt+jsfL332nNr8zUoePJ6p1/7m6r3usYhdtVFp6hmPOHb5Ftenbg3r5nS/MKhtAIXP58iVVqVJF45+f5OlSAJjM44l5ixYtrvuT05YtW0ysBgXZF98c1BffHHR5fMrgjvp8xwE9N/c/jrHjv/yf05zXVm2TJD1Qv3K+1AgATR5opiYPNPN0GUC+ITF3zeONed26dZ2eZ2RkaN++ffrxxx8VExPjmaJQ6NhsNrVpUkOvLvtSn7w+SHWq3q2Tp3/Xy+98kW25CwAAQH7weGM+e/bsHMcnT56slJQUk6tBYRUaEqBi/r4a3echTXl9vZ6fu1atGlfXe6/0U+un52nHnuxbegIAgJtAYO6SZdaY/12vXr30zjvv3HBeenq6Ll686PQwsjJNqBAFiZfXn/9TWL/tB81fuVX/O3xas5Zs0qdfH1D/bk08XB0AAMhP27dvV8eOHRUeHi6bzaa1a9c6jmVkZOjZZ59VrVq15O/vr/DwcD355JP69ddfnc6RlJSk6OhoBQYGKjg4WH379s1zyGzZxjwuLk6+vr43nBcbG6ugoCCnx9Wze0yoEAXJ/51PUUZGpuKPnXEaTziWqDJhxT1UFQAABY8V9zFPTU1VnTp19Prrr2c7dunSJe3du1cTJkzQ3r179dFHHykhIUGdOnVymhcdHa0DBw5o06ZNWr9+vbZv366nn346T3V4fCnLI4884vTcMAydOXNGu3fv1oQJE274+vHjx2vkyJFOY6EPPOvWGlHwZVzN1J6DJ3VPRCmn8coRoTp15ryHqgIAAGZo27at2rZtm+OxoKAgbdq0yWnstdde0/33369Tp06pbNmyio+P18aNG7Vr1y41aNBAkjR//ny1a9dOs2bNUnh4eK7q8HhjHhQU5PTcy8tLVapU0dSpU9WqVasbvt5ut8tutzuN2by83VojCgZ/Px9VLFPS8bzcXSVU+567dP7iJf2ceF6zl32pFS89pR17j+ir3YfVqlF1tWtaU637z3W8plSJYipVIlAVy94pSapZOVx/pKbp58TzOn/xkunvCUDBcyk1VadOnXI8P/3LLzoUH6+goCCVzuX/uQNWZtauLOnp6UpPT3cay6lvvBkXLlyQzWZTcHCwpD9XegQHBzuackmKioqSl5eXdu7cqYcffjhX5/VoY56Zmak+ffqoVq1aKl6c5QLIX/dWj9AXbw9zPJ85uqskacUn3+npSe/qk63/05Dp72nMU630ythuOnzynHqOeVvf7jvmeE2/bg/o+QHtHM+/fGeEJKn/xBV6d91Ok94JgILswIEf1a/Pk47ns2bGSpI6dX5YL8x40VNlAbed2NhYTZkyxWls0qRJmjx58i2dNy0tTc8++6x69uypwMBASVJiYqJCQ0Od5hUpUkQhISFKTEzM9bk92ph7e3urVatWio+PpzFHvvt6z0/yqzf4unOW/+c7Lf/Pdy6PT3/zU01/81N3lwYADvfd31D7DyR4ugwg35i1jXlOy51vNS3PyMhQ9+7dZRiGFixYcEvnyonHl7LUrFlTx44dU/ny5T1dCgAAAAoIdy1bueZaU37y5Elt2bLFkZZLUlhYmM6dO+c0/+rVq0pKSlJYWFiur+HxXVmmTZum0aNHa/369Tpz5ky2rQ8BAABQcFhxV5YbudaU//TTT/ryyy9VokQJp+ORkZFKTk7Wnj3/b2fALVu2KCsrSw0bNsz1dTyWmE+dOlWjRo1Su3Z/rtft1KmT0000DEM2m02ZmexJDgAAgPyTkpKiI0f+35cJHj9+XPv27VNISIhKly6tbt26ae/evVq/fr0yMzMd68ZDQkLk4+OjatWqqU2bNurfv78WLlyojIwMDR48WD169Mj1jiySZDMMw3D7u8sFb29vnTlzRvHx8ded16xZszyf+0briAHADOd3vebpEgBAvh5fuOzsnrEbTbnO4Zltcj1327ZtatGiRbbxmJgYTZ482eWS661bt6p58+aS/vyCocGDB2vdunXy8vJS165dNW/ePAUEBOS6Do/9Vl37eeBmGm8AAADAXZo3b67rZdW5ybFDQkK0atWqW6rDoz9DmbWPJQAAAKyB/s81jzbm99xzzw1/c5KSkkyqBgAAAPAcjzbmU6ZMyfbNnwAAACi4CMxd82hj3qNHj2zfkgQAAAAURh5rzFlfBAAAUPh4edEDuuKxLxjy0C6NAAAAgCV5LDHPysry1KUBAADgISyacM1jiTkAAACA/8di3wUFAACAgozPGbpGYg4AAABYAIk5AAAATENg7hqJOQAAAGABJOYAAAAwDWvMXSMxBwAAACyAxBwAAACmITF3jcQcAAAAsAAScwAAAJiGwNw1EnMAAADAAkjMAQAAYBrWmLtGYg4AAABYAIk5AAAATENg7hqJOQAAAGABJOYAAAAwDWvMXSMxBwAAACyAxBwAAACmITB3jcQcAAAAsAAScwAAAJiGNeaukZgDAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAATMMac9dIzAEAAAALIDEHAACAaQjMXSMxBwAAACyAxBwAAACmYY25ayTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAANOwxtw1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAaVhj7hqJOQAAAGABJOYAAAAwDYm5ayTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAANOwxtw1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAaVhj7hqJOQAAAAq17du3q2PHjgoPD5fNZtPatWudjhuGoYkTJ6p06dLy8/NTVFSUfvrpJ6c5SUlJio6OVmBgoIKDg9W3b1+lpKTkqQ4acwAAAJjGZjPnkRepqamqU6eOXn/99RyPz5w5U/PmzdPChQu1c+dO+fv7q3Xr1kpLS3PMiY6O1oEDB7Rp0yatX79e27dv19NPP52nOljKAgAAgEKtbdu2atu2bY7HDMPQnDlz9Pzzz6tz586SpOXLl6tUqVJau3atevToofj4eG3cuFG7du1SgwYNJEnz589Xu3btNGvWLIWHh+eqDhJzAAAAmMbLZjPlkZ6erosXLzo90tPT81zv8ePHlZiYqKioKMdYUFCQGjZsqLi4OElSXFycgoODHU25JEVFRcnLy0s7d+7M/b3Jc3UAAACAxcXGxiooKMjpERsbm+fzJCYmSpJKlSrlNF6qVCnHscTERIWGhjodL1KkiEJCQhxzcoOlLAAAADCNWZuyjB8/XiNHjnQas9vt5lz8JtGYAwAAoMCx2+1uacTDwsIkSWfPnlXp0qUd42fPnlXdunUdc86dO+f0uqtXryopKcnx+txgKQsAAABMY7PZTHm4S/ny5RUWFqbNmzc7xi5evKidO3cqMjJSkhQZGank5GTt2bPHMWfLli3KyspSw4YNc30tEnMAAAAUaikpKTpy5Ijj+fHjx7Vv3z6FhISobNmyGj58uKZNm6bKlSurfPnymjBhgsLDw9WlSxdJUrVq1dSmTRv1799fCxcuVEZGhgYPHqwePXrkekcWicYcAAAAJvKy4Bd/7t69Wy1atHA8v7Y2PSYmRkuXLtXYsWOVmpqqp59+WsnJyWrSpIk2btwoX19fx2tWrlypwYMHq2XLlvLy8lLXrl01b968PNVhMwzDcM9bsg6/eoM9XQIA6Pyu1zxdAgDI12IxbNsFud8+8FZ89s/cLyGxCov9VgEAAKAgc+f674KGD38CAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAATGMTkbkrJOYAAACABZCYAwAAwDRW3MfcKkjMAQAAAAsgMQcAAIBp2MfcNRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgGm8iMxdIjEHAAAALIDEHAAAAKYhMHeNxBwAAACwABJzAAAAmIZ9zF0jMQcAAAAsgMQcAAAApiEwd43EHAAAALAAEnMAAACYhn3MXSMxBwAAACwgV4n5J598kusTdurU6aaLAQAAQMFGXu5arhrzLl265OpkNptNmZmZt1IPAAAAUCjlqjHPysrK7zoAAABQCLCPuWu3tMY8LS3NXXUAAAAAhVqeG/PMzEy98MILuuuuuxQQEKBjx45JkiZMmKDFixe7vUAAAAAUHF42cx63ozw35tOnT9fSpUs1c+ZM+fj4OMZr1qypt99+263FAQAAAIVFnhvz5cuX66233lJ0dLS8vb0d43Xq1NGhQ4fcWhwAAAAKFpvNZsrjdpTnxvz06dOqVKlStvGsrCxlZGS4pSgAAACgsMlzY169enV9/fXX2cbXrFmjevXquaUoAAAAFEw2mzmP21Gutkv8q4kTJyomJkanT59WVlaWPvroIyUkJGj58uVav359ftQIAAAAFHh5Tsw7d+6sdevW6csvv5S/v78mTpyo+Ph4rVu3Tg899FB+1AgAAIACgjXmruU5MZekBx54QJs2bXJ3LQAAAEChdVONuSTt3r1b8fHxkv5cd16/fn23FQUAAICC6XbdY9wMeW7Mf/nlF/Xs2VPffPONgoODJUnJyclq1KiR3nvvPd19993urhEAAAAo8PK8xrxfv37KyMhQfHy8kpKSlJSUpPj4eGVlZalfv375USMAAAAKCNaYu5bnxPyrr77St99+qypVqjjGqlSpovnz5+uBBx5wa3EAAABAYZHnxrxMmTI5fpFQZmamwsPD3VIUAAAACqbbM8s2R56Xsrz88ssaMmSIdu/e7RjbvXu3hg0bplmzZrm1OAAAAKCwyFViXrx4cae1OqmpqWrYsKGKFPnz5VevXlWRIkX01FNPqUuXLvlSKAAAAG5/Xrfp+m8z5KoxnzNnTj6XAQAAABRuuWrMY2Ji8rsOAAAAFAIE5q7d9BcMSVJaWpquXLniNBYYGHhLBQEAAACFUZ4b89TUVD377LP64IMP9Pvvv2c7npmZ6ZbCAAAAUPDcrnuMmyHPu7KMHTtWW7Zs0YIFC2S32/X2229rypQpCg8P1/Lly/OjRgAAAKDAy3Nivm7dOi1fvlzNmzdXnz599MADD6hSpUqKiIjQypUrFR0dnR91AgAAoAAgMHctz4l5UlKSKlSoIOnP9eRJSUmSpCZNmmj79u3urQ4AAAAoJPLcmFeoUEHHjx+XJFWtWlUffPCBpD+T9ODgYLcWBwAAgILFy2Yz5XE7ynNj3qdPH+3fv1+SNG7cOL3++uvy9fXViBEjNGbMGLcXCAAAABQGeV5jPmLECMevo6KidOjQIe3Zs0eVKlVS7dq13VocAAAAUFjc0j7mkhQREaGIiAh31AIAAIAC7jZdZWKKXDXm8+bNy/UJhw4detPFAAAAAIVVrhrz2bNn5+pkNpuNxhwAAAAuWe0LhjIzMzV58mS9++67SkxMVHh4uHr37q3nn3/eUathGJo0aZIWLVqk5ORkNW7cWAsWLFDlypXdWkuuGvNru7AAAAAABclLL72kBQsWaNmyZapRo4Z2796tPn36KCgoyBE4z5w5U/PmzdOyZctUvnx5TZgwQa1bt9bBgwfl6+vrtlpshmEYbjubRVy6UuDeEoDb0Itbj3i6BADQ1NbuTXVv1ZCP4025zvyHq+VqXocOHVSqVCktXrzYMda1a1f5+fnp3XfflWEYCg8P16hRozR69GhJ0oULF1SqVCktXbpUPXr0cFvNed4uEQAAACgoGjVqpM2bN+vw4cOSpP3792vHjh1q27atpD9XjiQmJioqKsrxmqCgIDVs2FBxcXFureWWd2UBAAAAcsusNebp6elKT093GrPb7bLb7U5j48aN08WLF1W1alV5e3srMzNT06dPV3R0tCQpMTFRklSqVCmn15UqVcpxzF1IzAEAAFDgxMbGKigoyOkRGxubbd4HH3yglStXatWqVdq7d6+WLVumWbNmadmyZabXTGIOAAAA03iZtCnL+PHjNXLkSKexv6flkjRmzBiNGzfOsVa8Vq1aOnnypGJjYxUTE6OwsDBJ0tmzZ1W6dGnH686ePau6deu6teabSsy//vpr9erVS5GRkTp9+rQkacWKFdqxY4dbiwMAAABuht1uV2BgoNMjp8b80qVL8vJybom9vb2VlZUlSSpfvrzCwsK0efNmx/GLFy9q586dioyMdGvNeW7MP/zwQ7Vu3Vp+fn76/vvvHWt3Lly4oBkzZri1OAAAABQsXjZzHrnVsWNHTZ8+XRs2bNCJEyf08ccf69VXX9XDDz8s6c818cOHD9e0adP0ySef6IcfftCTTz6p8PBwdenSxa33Js9LWaZNm6aFCxfqySef1HvvvecYb9y4saZNm+bW4gAAAID8NH/+fE2YMEEDBw7UuXPnFB4ermeeeUYTJ050zBk7dqxSU1P19NNPKzk5WU2aNNHGjRvduoe5dBP7mN9xxx06ePCgypUrp2LFiv1/7d19fM71////+zHs2MxOTLZZORlzMueitMhJLSeVCElR4y0l5mRy+untNFlnclYoejt7k7OiECVCsrydRCeY81SMaqFNO7G9fn/4On4d5tCmY6/jZbtdu7wuF8fzdfY4jnp7P3bf8/U8tG/fPlWuXFnHjh1TzZo1lZ6e7tYCbwTrmAOwAtYxB2AFVlvH/PnVSabcZ1K76qbcx53yPZUlLCxMR47k/j+bbdu2qXLlym4pCgAAAChq8t2Y9+7dWwMHDtSOHTtks9l06tQpLVq0SEOGDNFzzz1XEDUCAACgkLDaHHMryfcc8xEjRignJ0f33XefLl68qGbNmslut2vIkCHq379/QdQIAAAAFHr5bsxtNpteeOEFDR06VEeOHFFqaqpq1qypUqVKFUR9AAAAKERM+uLPm9INf8GQt7e3atas6c5aAAAAgCIr3415y5YtZbvOjzqbNm36RwUBAACg8PIiMncp34351V89mpWVpb179+q7775TbGysu+oCAAAAipR8N+aTJ0++5vjYsWOVmpr6jwsCAABA4ZXvJQGLELd9Nt27d9d//vMfd10OAAAAKFJu+OHPqyUmJrr9a0kBAABQuDDF3LV8N+YdO3Z0em0Yhk6fPq1du3Zp1KhRbisMAAAAKEry3ZgHBgY6vfby8lL16tU1fvx4tWrVym2FAQAAoPBhVRbX8tWYZ2dnq2fPnqpTp45Kly5dUDUBAAAARU6+Hv4sVqyYWrVqpXPnzhVQOQAAACjMbDZztptRvldlqV27to4dO1YQtQAAAABFVr4b8wkTJmjIkCFas2aNTp8+rQsXLjhtAAAAgCteNnO2m1Ge55iPHz9ezz//vB544AFJ0sMPPyzbX35PYBiGbDabsrOz3V8lAAAAUMjluTEfN26c+vTpo88//7wg6wEAAEAhxqosruW5MTcMQ5LUvHnzAisGAAAAKKrytVyijZ9wAAAA8A/QTrqWr8a8WrVqf9ucp6Sk/KOCAAAAgKIoX435uHHjcn3zJwAAAJBXN+uKKWbIV2PetWtXhYSEFFQtAAAAQJGV58ac+eUAAAD4p2yip3Qlz18wdGVVFgAAAADul+fEPCcnpyDrAAAAQBHAHHPX8pyYAwAAACg4+Xr4EwAAAPgnSMxdIzEHAAAALIDEHAAAAKZhpT/XSMwBAAAACyAxBwAAgGmYY+4aiTkAAABgASTmAAAAMA1TzF0jMQcAAAAsgMQcAAAApvEiMneJxBwAAACwABJzAAAAmIZVWVwjMQcAAAAsgMQcAAAApmGKuWsk5gAAAIAFkJgDAADANF4iMneFxBwAAACwABJzAAAAmIY55q6RmAMAAAAWQGIOAAAA07COuWsk5gAAAIAFkJgDAADANF5MMneJxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgGuaYu0ZiDgAAAFgAiTkAAABMQ2DuGok5AAAAirSff/5Z3bt3V5kyZeTr66s6depo165djv2GYWj06NEqV66cfH19FRMTo8OHD7u9DhpzAAAAmMbLpC2vfv/9dzVp0kQlSpTQunXrtH//fk2aNEmlS5d2HPPqq69q2rRpmjVrlnbs2CE/Pz+1bt1a6enpN/w5XAtTWQAAAFBkvfLKKypfvrzmzp3rGIuIiHD82TAMTZkyRf/+97/Vvn17SdKCBQsUGhqqVatWqWvXrm6rhcQcAAAAprHZbKZsGRkZunDhgtOWkZGRq56PPvpIjRo10qOPPqqQkBA1aNBAs2fPduw/fvy4kpOTFRMT4xgLDAxU48aNlZiY6NbPhsYcAAAAhU5CQoICAwOdtoSEhFzHHTt2TDNnzlTVqlX1ySef6LnnntOAAQM0f/58SVJycrIkKTQ01Om80NBQxz53YSoLAAAATGPWoiwjR47U4MGDncbsdnuu43JyctSoUSNNnDhRktSgQQN99913mjVrlmJjY02p9QoScwAAABQ6drtdAQEBTtu1GvNy5cqpZs2aTmNRUVE6efKkJCksLEySdObMGadjzpw549jnLjTmAAAAMI2XzWbKlldNmjRRUlKS09ihQ4dUsWJFSZcfBA0LC9PGjRsd+y9cuKAdO3YoOjraPR/K/8NUFgAAABRZ8fHxuvvuuzVx4kR16dJF//vf//TOO+/onXfekXT5YdVBgwZpwoQJqlq1qiIiIjRq1CiFh4erQ4cObq2FxhwAAACmsdoXf95xxx1auXKlRo4cqfHjxysiIkJTpkxRt27dHMcMGzZMaWlpeuaZZ3Tu3Dk1bdpU69evl4+Pj1trsRmGYbj1ihZwMbPQvSUAN6GXPz/i6RIAQONbV/V0CU4W7f7JlPt0a3ibKfdxJxJzAAAAmCYf07+LHB7+BAAAACyAxBwAAACmsRGZu0RiDgAAAFgAiTkAAABMQyrsGp8NAAAAYAEk5gAAADANc8xdIzEHAAAALIDEHAAAAKYhL3eNxBwAAACwABJzAAAAmIY55q6RmAMAAAAWQGIOAAAA05AKu8ZnAwAAAFgAiTkAAABMwxxz10jMAQAAAAsgMQcAAIBpyMtdIzEHAAAALIDEHAAAAKZhirlrJOYAAACABZCYAwAAwDRezDJ3icQcAAAAsAAScwAAAJiGOeaukZgDAAAAFkBiDgAAANPYmGPuEok5AAAAYAEk5gAAADANc8xdIzEHAAAALIDEHAAAAKZhHXPXSMwBAAAACyAxBwAAgGmYY+4aiTkAAABgASTmAAAAMA2JuWsk5gAAAIAFkJgDAADANHzzp2sk5gAAAIAFkJgDAADANF4E5i6RmAMAAAAWQGIOAAAA0zDH3DUScwAAAMACLNWYZ2ZmKikpSZcuXfJ0KQAAACgANps5283IEo35xYsX1atXL5UsWVK1atXSyZMnJUn9+/fXyy+/7OHqAAAAgIJnicZ85MiR2rdvnzZv3iwfHx/HeExMjJYuXerBygAAAOBONpP+uRlZ4uHPVatWaenSpbrrrrtk+8vvHmrVqqWjR496sDIAAADAHJZozH/55ReFhITkGk9LS3Nq1AEAAHBzYx1z1ywxlaVRo0Zau3at4/WVZnzOnDmKjo72VFkAAACAaSyRmE+cOFFt27bV/v37denSJU2dOlX79+/X9u3btWXLFk+XBwAAADe5Wed/m8ESiXnTpk21d+9eXbp0SXXq1NGnn36qkJAQJSYmqmHDhp4uDwAAAChwlkjMJalKlSqaPXu2p8tAEbZs6XtasfQ9nTr1sySpcpVIPdOnn5re08zDlQEozFaP/ZcuppzNNR7Z9EE17PKc47VhGNo6a6ySD+xWk6df0G11meqJmxOPD7pmicZ8z549KlGihOrUqSNJ+vDDDzV37lzVrFlTY8eOlbe3t4crRFEQGhqq/oOeV4WKFSXD0OqPVil+QD8tWf6BqkRW9XR5AAqp+5+fLMPIcbw+f/oHbXnr3yrfoInTcYc2f0hDAxRylpjK8uyzz+rQoUOSpGPHjumxxx5TyZIltXz5cg0bNszD1aGoaN7iXt3TrLkqVqykipUiFDcgXiVLltQ33+zzdGkACjEf/0D5BpR2bKe++59K3VJOZSPrOI75/adjStq0Unc8MchzhQJuYjNpuxlZojE/dOiQ6tevL0lavny5mjdvrsWLF2vevHl6//33PVsciqTs7GytX7dWf/55UXXr1fd0OQCKiOxLWfph12ZF3HW/Y4WyS5np+mr+a2r46HPyDSjt4QoBFCRLTGUxDEM5OZd/jffZZ5/poYcekiSVL19ev/76qydLQxFz+FCSYrs/rszMDPmWLKlJU95UlSqRni4LQBHx8zdfKevPVEU0vs8x9vUHc1QmIkq31r3Lg5UB7uPFnCyXLJGYN2rUSBMmTNDChQu1ZcsWPfjgg5Kk48ePKzQ09LrnZmRk6MKFC05bRkaGGWWjEKoUEaElK1ZqwaKlerRLV43+9wgdPXrE02UBKCKOf/WpykU1lG9gGUnSz9/u0NnD+9SgU28PVwYUHS+//LJsNpsGDRrkGEtPT1e/fv1UpkwZlSpVSp06ddKZM2fcfm9LNOZTpkzRnj17FBcXpxdeeEGRkZcTyhUrVujuu+++7rkJCQkKDAx02l5/NcGMslEIlSjhrQoVKqpmrdoaMOh5VatWQ+/9d4GnywJQBKSlnNWZpH2qHN3aMXbm0D6l/pqslcMf07JBD2vZoIclSdvfTdCmaSM8VSrwj1h5jvnOnTv19ttvq27duk7j8fHxWr16tZYvX64tW7bo1KlT6tix4w3exTVLTGWpW7euvv3221zjr732mooVK3bdc0eOHKnBgwc7jWXbWMUF7mEYOcrMzPR0GQCKgONfbZDdP1Dlat3hGIu6/1FVjm7ldNwnL8epfsenFV77TrNLBAq11NRUdevWTbNnz9aECRMc4+fPn9e7776rxYsX695775UkzZ07V1FRUfrqq690113um2ZmicbcFR8fn789xm63y263O41dzDQKqiQUYtOmTFKTps1Urlw5paWlad3Ha7Rr5/80Y9YcT5cGoJAzcnJ0fMdnqnTnffL6SyB1ZaWWq5UsXValyoSZWSLgPiZNMc/IyMg1vflafeMV/fr104MPPqiYmBinxnz37t3KyspSTEyMY6xGjRqqUKGCEhMTC0djXrp0accT538nJSWlgKsBLv93NuqF4fr1l19Uyt9fVatW14xZc3TX3U3+/mQA+AfOJO3Vxd9/UeW77vd0KUChkZCQoHHjxjmNjRkzRmPHjs117JIlS7Rnzx7t3Lkz177k5GR5e3srKCjIaTw0NFTJycnuLNlzjfmUKVM8dWvgmsaOf8nTJQAoosKibtdj09bk6di8HgdYlc2kyPxa052vlZb/+OOPGjhwoDZs2JCn2RoFyWONeWxsrKduDQAAgELuetNW/mr37t06e/asbr/9dsdYdna2tm7dqjfffFOffPKJMjMzde7cOafU/MyZMwoLc++UMsvNMU9PT8/1sF1AQICHqgEAAIA7WW0Z8/vuuy/XIiQ9e/ZUjRo1NHz4cJUvX14lSpTQxo0b1alTJ0lSUlKSTp48qejoaLfWYonGPC0tTcOHD9eyZcv022+/5dqfnZ3tgaoAAABQ2Pn7+6t27dpOY35+fipTpoxjvFevXho8eLCCg4MVEBCg/v37Kzo62q0PfkoWWcd82LBh2rRpk2bOnCm73a45c+Zo3LhxCg8P14IFrCENAABQWFh5HXNXJk+erIceekidOnVSs2bNFBYWpg8++MDNd5FshmF4fG3BChUqaMGCBWrRooUCAgK0Z88eRUZGauHChXrvvff08ccf5+t6LJcIwApe/pxvjQXgeeNbV/V0CU52Hjtvyn3uqBxoyn3cyRKJeUpKiipXrizp8nzyK8sjNm3aVFu3bvVkaQAAAHCnmzEyN4klGvPKlSvr+PHjki4v2L5s2TJJ0urVq3OtGQkAAAAURh5tzI8dO6acnBz17NlT+/btkySNGDFCb731lnx8fBQfH6+hQ4d6skQAAAC4kc2kf25GHl2VpWrVqjp9+rTi4+MlSY899pimTZumgwcPavfu3YqMjFTdunU9WSIAAABgCo825lc/d/rxxx8rISFBlStXVsWKFT1UFQAAAAqK1dYxtxJLzDEHAAAAijqPJuY2m022q35suvo1AAAACg86Pdc8PpWlR48estvtkqT09HT16dNHfn5+TscVxALuAAAAgJV4tDGPjY11et29e3cPVQIAAABTEJm75NHGfO7cuZ68PQAAAGAZHm3MAQAAULTcrGuMm4FVWQAAAAALIDEHAACAaViAzzUScwAAAMACSMwBAABgGgJz10jMAQAAAAsgMQcAAIB5iMxdIjEHAAAALIDEHAAAAKZhHXPXSMwBAAAACyAxBwAAgGlYx9w1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAeYjMXSIxBwAAACyAxBwAAACmYR1z10jMAQAAAAsgMQcAAIBpWMfcNRJzAAAAwAJIzAEAAGAaAnPXSMwBAAAACyAxBwAAgHmIzF0iMQcAAAAsgMQcAAAApmEdc9dIzAEAAAALIDEHAACAaVjH3DUScwAAAMACSMwBAABgGgJz10jMAQAAAAsgMQcAAIB5iMxdIjEHAAAALIDEHAAAAKZhHXPXSMwBAAAACyAxBwAAgGlYx9w1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAeYjMXSIxBwAAACyAxBwAAACmYR1z10jMAQAAAAsgMQcAAIBpWMfcNRJzAAAAFFkJCQm644475O/vr5CQEHXo0EFJSUlOx6Snp6tfv34qU6aMSpUqpU6dOunMmTNur4XGHAAAAKaxmbTl1ZYtW9SvXz999dVX2rBhg7KystSqVSulpaU5jomPj9fq1au1fPlybdmyRadOnVLHjh1v+DNwxWYYhuH2q3rYxcxC95YA3IRe/vyIp0sAAI1vXdXTJTg58Wu6KfepdIvPDZ33yy+/KCQkRFu2bFGzZs10/vx5lS1bVosXL1bnzp0lSQcPHlRUVJQSExN11113ua1mEnMAAACYx2qR+VXOnz8vSQoODpYk7d69W1lZWYqJiXEcU6NGDVWoUEGJiYk3fqNr4OFPAAAAFDoZGRnKyMhwGrPb7bLb7S7PycnJ0aBBg9SkSRPVrl1bkpScnCxvb28FBQU5HRsaGqrk5GS31kxiDgAAANPYTPonISFBgYGBTltCQsJ1a+vXr5++++47LVmyxKRPwxmJOQAAAAqdkSNHavDgwU5j10vL4+LitGbNGm3dulW33XabYzwsLEyZmZk6d+6cU2p+5swZhYWFubVmEnMAAACYxmYzZ7Pb7QoICHDartWYG4ahuLg4rVy5Ups2bVJERITT/oYNG6pEiRLauHGjYywpKUknT55UdHS0Wz8bEnMAAAAUWf369dPixYv14Ycfyt/f3zFvPDAwUL6+vgoMDFSvXr00ePBgBQcHKyAgQP3791d0dLRbV2SRaMwBAABgIqt98efMmTMlSS1atHAanzt3rnr06CFJmjx5sry8vNSpUydlZGSodevWmjFjhttrYR1zACggrGMOwAqsto75jykZf3+QG5QPdj2f3KpIzAEAAGAam9Uicwvh4U8AAADAAkjMAQAAYCIic1dIzAEAAAALIDEHAACAaZhj7hqJOQAAAGABJOYAAAAwDYG5ayTmAAAAgAXQmAMAAAAWwFQWAAAAmIaHP10jMQcAAAAsgMQcAAAAprHx+KdLJOYAAACABZCYAwAAwDwE5i6RmAMAAAAWQGIOAAAA0xCYu0ZiDgAAAFgAiTkAAABMwzrmrpGYAwAAABZAYg4AAADTsI65ayTmAAAAgAWQmAMAAMA8BOYukZgDAAAAFkBiDgAAANMQmLtGYg4AAABYAIk5AAAATMM65q6RmAMAAAAWQGIOAAAA07COuWsk5gAAAIAFkJgDAADANMwxd43EHAAAALAAGnMAAADAAmjMAQAAAAtgjjkAAABMwxxz10jMAQAAAAsgMQcAAIBpWMfcNRJzAAAAwAJIzAEAAGAa5pi7RmIOAAAAWACJOQAAAExDYO4aiTkAAABgASTmAAAAMA+RuUsk5gAAAIAFkJgDAADANKxj7hqJOQAAAGABJOYAAAAwDeuYu0ZiDgAAAFgAiTkAAABMQ2DuGok5AAAAYAEk5gAAADAPkblLJOYAAACABZCYAwAAwDSsY+4aiTkAAABgASTmAAAAMA3rmLtGYg4AAABYgM0wDMPTRQBWk5GRoYSEBI0cOVJ2u93T5QAogvh7CCh6aMyBa7hw4YICAwN1/vx5BQQEeLocAEUQfw8BRQ9TWQAAAAALoDEHAAAALIDGHAAAALAAGnPgGux2u8aMGcMDVwA8hr+HgKKHhz8BAAAACyAxBwAAACyAxhwAAACwABpzAAAsZt68eQoKCvJ0GQBMRmOOQs1ms113Gzt2rKdLBFCI9ejR45p/9xw5csTTpQGwoOKeLgAoSKdPn3b8eenSpRo9erSSkpIcY6VKlXL82TAMZWdnq3hx/mcBwH3atGmjuXPnOo2VLVvWQ9UAsDIScxRqYWFhji0wMFA2m83x+uDBg/L399e6devUsGFD2e12bdu2TT169FCHDh2crjNo0CC1aNHC8TonJ0cJCQmKiIiQr6+v6tWrpxUrVpj75gDcFOx2u9PfRWFhYZo6darq1KkjPz8/lS9fXn379lVqaqrLa+zbt08tW7aUv7+/AgIC1LBhQ+3atcuxf9u2bbrnnnvk6+ur8uXLa8CAAUpLSzPj7QFwIxpzFHkjRozQyy+/rAMHDqhu3bp5OichIUELFizQrFmz9P333ys+Pl7du3fXli1bCrhaAIWBl5eXpk2bpu+//17z58/Xpk2bNGzYMJfHd+vWTbfddpt27typ3bt3a8SIESpRooQk6ejRo2rTpo06deqkb775RkuXLtW2bdsUFxdn1tsB4Cb8zh5F3vjx43X//ffn+fiMjAxNnDhRn332maKjoyVJlStX1rZt2/T222+refPmBVUqgJvQmjVrnKbNtW3bVsuXL3e8rlSpkiZMmKA+ffpoxowZ17zGyZMnNXToUNWoUUOSVLVqVce+hIQEdevWTYMGDXLsmzZtmpo3b66ZM2fKx8enAN4VgIJAY44ir1GjRvk6/siRI7p48WKuZj4zM1MNGjRwZ2kACoGWLVtq5syZjtd+fn767LPPlJCQoIMHD+rChQu6dOmS0tPTdfHiRZUsWTLXNQYPHqynn35aCxcuVExMjB599FFVqVJF0uVpLt98840WLVrkON4wDOXk5Oj48eOKiooq+DcJwC1ozFHk+fn5Ob328vLS1V+Im5WV5fjzlXmga9eu1a233up0HF+dDeBqfn5+ioyMdLw+ceKEHnroIT333HN66aWXFBwcrG3btqlXr17KzMy8ZmM+duxYPfHEE1q7dq3WrVunMWPGaMmSJXrkkUeUmpqqZ599VgMGDMh1XoUKFQr0vQFwLxpz4Cply5bVd9995zS2d+9ex3zOmjVrym636+TJk0xbAZBvu3fvVk5OjiZNmiQvr8uPei1btuxvz6tWrZqqVaum+Ph4Pf7445o7d64eeeQR3X777dq/f79T8w/g5sTDn8BV7r33Xu3atUsLFizQ4cOHNWbMGKdG3d/fX0OGDFF8fLzmz5+vo0ePas+ePZo+fbrmz5/vwcoB3AwiIyOVlZWl6dOn69ixY1q4cKFmzZrl8vg///xTcXFx2rx5s3744Qd9+eWX2rlzp2OKyvDhw7V9+3bFxcVp7969Onz4sD788EMe/gRuQjTmwFVat26tUaNGadiwYbrjjjv0xx9/6KmnnnI65sUXX9SoUaOUkJCgqKgotWnTRmvXrlVERISHqgZws6hXr57eeOMNvfLKK6pdu7YWLVqkhIQEl8cXK1ZMv/32m5566ilVq1ZNXbp0Udu2bTVu3DhJUt26dbVlyxYdOnRI99xzjxo0aKDRo0crPDzcrLcEwE1sxtWTaQEAAACYjsQcAAAAsAAacwAAAMACaMwBAAAAC6AxBwAAACyAxhwAAACwABpzAAAAwAJozAEAAAALoDEHAAAALIDGHECR1aNHD3Xo0MHxukWLFho0aJDpdWzevFk2m03nzp1zeYzNZtOqVavyfM2xY8eqfv36/6iuEydOyGazae/evf/oOgCAvKExB2ApPXr0kM1mk81mk7e3tyIjIzV+/HhdunSpwO/9wQcf6MUXX8zTsXlppgEAyI/ini4AAK7Wpk0bzZ07VxkZGfr444/Vr18/lShRQiNHjsx1bGZmpry9vd1y3+DgYLdcBwCAG0FiDsBy7Ha7wsLCVLFiRT333HOKiYnRRx99JOn/n37y0ksvKTw8XNWrV5ck/fjjj+rSpYuCgoIUHBys9u3b68SJE45rZmdna/DgwQoKClKZMmU0bNgwGYbhdN+rp7JkZGRo+PDhKl++vOx2uyIjI/Xuu+/qxIkTatmypSSpdOnSstls6tGjhyQpJydHCQkJioiIkK+vr+rVq6cVK1Y43efjjz9WtWrV5Ovrq5YtWzrVmVfDhw9XtWrVVLJkSVWuXFmjRo1SVlZWruPefvttlS9fXiVLllSXLl10/vx5p/1z5sxRVFSUfHx8VKNGDc2YMSPftQAA3IPGHIDl+fr6KjMz0/F648aNSkpK0oYNG7RmzRplZWWpdevW8vf31xdffKEvv/xSpUqVUps2bRznTZo0SfPmzdN//vMfbdu2TSkpKVq5cuV17/vUU0/pvffe07Rp03TgwAG9/fbbKlWqlMqXL6/3339fkpSUlKTTp09r6tSpkqSEhAQtWLBAs2bN0vfff6/4+Hh1795dW7ZskXT5B4iOHTuqXbt22rt3r55++mmNGDEi35+Jv7+/5s2bp/3792vq1KmaPXu2Jk+e7HTMkSNHtGzZMq1evVrr16/X119/rb59+zr2L1q0SKNHj9ZLL72kAwcOaOLEiRo1apTmz5+f73oAAG5gAICFxMbGGu3btzcMwzBycnKMDRs2GHa73RgyZIhjf2hoqJGRkeE4Z+HChUb16tWNnJwcx1hGRobh6+trfPLJJ4ZhGEa5cuWMV1991bE/KyvLuO222xz3MgzDaN68uTFw4EDDMAwjKSnJkGRs2LDhmnV+/vnnhiTj999/d4ylp6cbJUuWNLZv3+50bK9evYzHH3/cMAzDGDlypFGzZk2n/cOHD891ratJMlauXOly/2uvvWY0bNjQ8XrMmDFGsWLFjJ9++skxtm7dOsPLy8s4ffq0YRiGUaVKFWPx4sVO13nxxReN6OhowzAM4/jx44Yk4+uvv3Z5XwCA+zDHHIDlrFmzRqVKlVJWVpZycnL0xBNPaOzYsY79derUcZpXvm/fPh05ckT+/v5O10lPT9fRo0d1/vx5nT59Wo0bN3bsK168uBo1apRrOssVe/fuVbFixdS8efM8133kyBFdvHhR999/v9N4ZmamGjRoIEk6cOCAUx2SFB0dned7XLF06VJNmzZNR48eVWpqqi5duqSAgACnYypUqKBbb73V6T45OTlKSkqSv7+/jh49ql69eql3796OYy5duqTAwMB81wMA+OdozAFYTsuWLTVz5kx5e3srPDxcxYs7/1Xl5+fn9Do1NVUNGzbUokWLcl2rbNmyN1SDr69vvs9JTU2VJK1du9apIZYuz5t3l8TERHXr1k3jxo1T69atFRgYqCVLlmjSpEn5rnX27Nm5flAoVqyY22oFAOQdjTkAy/Hz81NkZGSej7/99tu1dOlShYSE5EqNryhXrpx27NihZs2aSbqcDO/evVu33377NY+vU6eOcnJytGXLFsXExOTafyWxz87OdozVrFlTdrtdJ0+edJm0R0VFOR5kveKrr776+zf5F9u3b1fFihX1wgsvOMZ++OGHXMedPHlSp06dUnh4uOM+Xl5eql69ukJDQxUeHq5jx46pW7du+bo/AKBg8PAngJtet27ddMstt6h9+/b64osvdPz4cW3evFkDBgzQTz/9JEkaOHCgXn75Za1atUoHDx5U3759r7sGeaVKlRQbG6t//etfWrVqleOay5YtkyRVrFhRNptNa9as0S+//KLU1FT5+/tryJAhio+P1/z583X06FHt2bNH06dPdzxQ2adPHx0+fFhDhw5VUlKSFi9erHnz5uXr/VatWlUnT57UkiVLdPToUU2bNu2aD7L6+PgoNjZW+/bt0xdffKEBAwaoS5cuCgsLkySNGzdOCQkJmjZtmg4dOqRvv/1Wc+fO1RtvvJGvegAA7kFjDuCmV7JkSW3dulUVKlRQx44dFRUVpV69eik9Pd2RoD///PN68sknFRsbq+joaPn7++uRRx657nVnzpypzp07q2/fvqpRo4Z69+6ttLQ0SdKtt96qcePGacSIEQoNDVVcXJwk6cUXX9SoUaOUkJCgqKgotWnTRmvXrlVERISky/O+33//fa1atUr16tXTrFmzNHHixHy934cffljx8fGKi4tT/fr1tX37do0aNSrXcZGRkerYsaMeeOABtWrVSnXr1nVaDvHpp5/WnDlzNHfuXNWpU0fNmzfXvHnzHLUCAMxlM1w9+QQAAADANCTmAAAAgAXQmAMAAAAWQGMOAAAAWACNOQAAAGABNOYAAACABdCYAwAAABZAYw4AAABYAI05AAAAYAE05gAAAIAF0JgDAAAAFkBjDgAAAFgAjTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWACNOQAAAGABNOYAAACABdCYA0VQpUqV1KNHD0+XUejs3LlTd999t/z8/GSz2bR37163Xn/z5s2y2WzavHmzW697M+O/ZQCFCY05UIgcPXpUzz77rCpXriwfHx8FBASoSZMmmjp1qv78809Pl3dd33//vR599FFVrlxZJUuW1C233KJmzZpp9erV+brO3r171b17d5UvX152u13BwcGKiYnR3LlzlZ2dXUDVS1lZWXr00UeVkpKiyZMna+HChapYsWKB3c9sLVq0kM1mU9WqVa+5f8OGDbLZbLLZbFqxYkW+r79//36NHTtWJ06c+IeVAsDNq7inCwDgHmvXrtWjjz4qu92up556SrVr11ZmZqa2bdumoUOH6vvvv9c777zj6TJd+uGHH/THH38oNjZW4eHhunjxot5//309/PDDevvtt/XMM8/87TXmzJmjPn36KDQ0VE8++aSqVq2qP/74Qxs3blSvXr10+vRp/d///V+B1H/06FH98MMPmj17tp5++ukCuUezZs30559/ytvbu0Cu/3d8fHx05MgR/e9//9Odd97ptG/RokXy8fFRenr6DV17//79GjdunFq0aKFKlSrl+bykpCR5eZExASgcaMyBQuD48ePq2rWrKlasqE2bNqlcuXKOff369dORI0e0du1aD1b49x544AE98MADTmNxcXFq2LCh3njjjb9tzL/66iv16dNH0dHR+vjjj+Xv7+/YN2jQIO3atUvfffddgdQuSWfPnpUkBQUFFdg9vLy85OPjU2DX/ztVqlTRpUuX9N577zk15unp6Vq5cqUefPBBvf/++wVeh2EYSk9Pl6+vr+x2e4HfDwDMQswAFAKvvvqqUlNT9e677zo15VdERkZq4MCBLs9PSUnRkCFDVKdOHZUqVUoBAQFq27at9u3bl+vY6dOnq1atWipZsqRKly6tRo0aafHixY79f/zxhwYNGqRKlSrJbrcrJCRE999/v/bs2ZPv91WsWDGVL19e586d+9tjx40bJ5vNpkWLFjk15Vc0atTIaS5yWlqann/+eceUl+rVq+v111+XYRhO59lsNsXFxWnVqlWqXbu27Ha7atWqpfXr1zuO6dGjh5o3by5JevTRR2Wz2dSiRQtJl6eAXPnzX/Xo0SNXMrxkyRI1bNhQ/v7+CggIUJ06dTR16lTHfldzzJcvX66GDRvK19dXt9xyi7p3766ff/451/1KlSqln3/+WR06dFCpUqVUtmxZDRkyJF9TfB5//HEtXbpUOTk5jrHVq1fr4sWL6tKlS67jf/jhB/Xt21fVq1eXr6+vypQpo0cffdRpysq8efP06KOPSpJatmzpmBJz5X1WqlRJDz30kD755BM1atRIvr6+evvttx37rvx7NQxDLVu2VNmyZR0/KElSZmam6tSpoypVqigtLS3P7xUAzEZjDhQCq1evVuXKlXX33Xff0PnHjh3TqlWr9NBDD+mNN97Q0KFD9e2336p58+Y6deqU47jZs2drwIABqlmzpqZMmaJx48apfv362rFjh+OYPn36aObMmerUqZNmzJihIUOGyNfXVwcOHMhTLWlpafr111919OhRTZ48WevWrdN999133XMuXryojRs3qlmzZqpQocLf3sMwDD388MOaPHmy2rRpozfeeEPVq1fX0KFDNXjw4FzHb9u2TX379lXXrl316quvKj09XZ06ddJvv/0mSXr22WcdU2QGDBighQsX6oUXXsjT+71iw4YNevzxx1W6dGm98sorevnll9WiRQt9+eWX1z1v3rx56tKli4oVK6aEhAT17t1bH3zwgZo2bZrrB5rs7Gy1bt1aZcqU0euvv67mzZtr0qRJ+Zri9MQTT+j06dNOPxwsXrxY9913n0JCQnIdv3PnTm3fvl1du3bVtGnT1KdPH23cuFEtWrTQxYsXJV2eojNgwABJ0v/93/9p4cKFWrhwoaKiohzXSUpK0uOPP677779fU6dOVf369XPdy2az6T//+Y/S09PVp08fx/iYMWP0/fffa+7cufLz88vzewUA0xkAbmrnz583JBnt27fP8zkVK1Y0YmNjHa/T09ON7Oxsp2OOHz9u2O12Y/z48Y6x9u3bG7Vq1brutQMDA41+/frluZarPfvss4YkQ5Lh5eVldO7c2UhJSbnuOfv27TMkGQMHDszTPVatWmVIMiZMmOA03rlzZ8NmsxlHjhxxjEkyvL29ncau3G/69OmOsc8//9yQZCxfvtzpms2bNzeaN2+eq4bY2FijYsWKjtcDBw40AgICjEuXLrms+8o9Pv/8c8MwDCMzM9MICQkxateubfz555+O49asWWNIMkaPHu10P0lO/z4NwzAaNGhgNGzY0OU9//o+rvy7b9SokdGrVy/DMAzj999/N7y9vY358+df8zO4ePFirmslJiYakowFCxY4xpYvX+703v6qYsWKhiRj/fr119z31/+WDcMw3n77bUOS8d///tf46quvjGLFihmDBg362/cIAJ5GYg7c5C5cuCBJ15y+kVd2u93xAF12drZ+++03lSpVStWrV3eaghIUFKSffvpJO3fudHmtoKAg7dixwylpz49BgwZpw4YNmj9/vtq2bavs7GxlZmZe95z8fgYff/yxihUr5khpr3j++edlGIbWrVvnNB4TE6MqVao4XtetW1cBAQE6duxYnu6XF0FBQUpLS9OGDRvyfM6uXbt09uxZ9e3b12nu+YMPPqgaNWpc87mCvybJknTPPffk+3088cQT+uCDD5SZmakVK1aoWLFieuSRR655rK+vr+PPWVlZ+u233xQZGamgoKB8TW+KiIhQ69at83TsM888o9atW6t///568sknVaVKFU2cODHP9wIAT6ExB25yAQEBki7P7b5ROTk5mjx5sqpWrSq73a5bbrlFZcuW1TfffKPz5887jhs+fLhKlSqlO++8U1WrVlW/fv1yTbV49dVX9d1336l8+fK68847NXbs2Hw1fjVq1FBMTIyeeuoprVmzRqmpqWrXrl2uud9/ld/P4IcfflB4eHiuRv7K1IkffvjBafxa02NKly6t33//PU/3y4u+ffuqWrVqatu2rW677Tb961//cprHfi1X6qxevXqufTVq1Mj1Pnx8fFS2bFmnsRt5H127dtX58+e1bt06LVq0SA899JDLH4r+/PNPjR492jGX/8p/W+fOnXP6b+vvRERE5KvGd999VxcvXtThw4c1b948px8QAMCqaMyBm1xAQIDCw8P/0YojEydO1ODBg9WsWTP997//1SeffKINGzaoVq1aTg/5RUVFKSkpSUuWLFHTpk31/vvvq2nTphozZozjmC5duujYsWOaPn26wsPD9dprr6lWrVq5Uui86ty5s3bu3KlDhw65PCYyMlLFixfXt99+e0P3+DvFihW75vj1fli4wmazXXP86gcuQ0JCtHfvXn300Ud6+OGH9fnnn6tt27aKjY3Nf8EuuHof+VWuXDm1aNFCkyZN0tatW/XEE0+4PLZ///566aWX1KVLFy1btkyffvqpNmzYoDJlyjj9t/V38ttYb968WRkZGZJUYP9dAIC70ZgDhcBDDz2ko0ePKjEx8YbOX7FihVq2bKl3331XXbt2VatWrRQTE3PN1VD8/Pz02GOPae7cuTp58qQefPBBvfTSS07rV5crV059+/bVqlWrdPz4cZUpU0YvvfTSDdV25YuRrpeulixZUvfee6+2bt2qH3/88W+vWbFiRZ06dSpXwn7w4EHHfncpXbr0NT/Hq9NsSfL29la7du00Y8YMx5dFLViwQEeOHLnmta/UmZSUlGtfUlJSgX7B0RNPPKEvvvhCAQEBuZa5/KsVK1YoNjZWkyZNUufOnXX//fdf88FUVz/A3IjTp0+rf//+atWqlR566CENGTLkmp83AFgNjTlQCAwbNkx+fn56+umndebMmVz7jx496rTs3tWKFSuWK/1dvnx5riX3rqxCcoW3t7dq1qwpwzCUlZWl7OzsXA10SEiIwsPDHemlK39d3u6KrKwsLViwQL6+vqpZs+Z1zx8zZowMw9CTTz6p1NTUXPt3796t+fPnS7q8Znp2drbefPNNp2MmT54sm82mtm3bXvde+VGlShUdPHhQv/zyi2Ns3759uaYAXf3Zenl5qW7dupLk8rNr1KiRQkJCNGvWLKdj1q1bpwMHDujBBx9019vIpXPnzhozZoxmzJhx3S88utZ/W9OnT8/1G4Mrq6XkZWnMv9O7d2/l5OTo3Xff1TvvvKPixYurV69eefoNBwB4El8wBBQCVapU0eLFi/XYY48pKirK6Zs/t2/fruXLlzut4X21hx56SOPHj1fPnj11991369tvv9WiRYtUuXJlp+NatWqlsLAwNWnSRKGhoTpw4IDefPNNPfjgg/L399e5c+d02223qXPnzqpXr55KlSqlzz77TDt37tSkSZOu+x6effZZXbhwQc2aNdOtt96q5ORkLVq0SAcPHtSkSZNUqlSp655/991366233lLfvn1Vo0YNp2/+3Lx5sz766CNNmDBBktSuXTu1bNlSL7zwgk6cOKF69erp008/1YcffqhBgwY5Pej5T/3rX//SG2+8odatW6tXr146e/asZs2apVq1ajkeWpWkp59+WikpKbr33nt122236YcfftD06dNVv359p2UD/6pEiRJ65ZVX1LNnTzVv3lyPP/64zpw5o6lTp6pSpUqKj4932/u4WmBgoMaOHfu3xz300ENauHChAgMDVbNmTSUmJuqzzz5TmTJlnI6rX7++ihUrpldeeUXnz5+X3W7Xvffee80lGK9n7ty5Wrt2rebNm6fbbrtN0uUfBLp3766ZM2eqb9+++boeAJjKcwvCAHC3Q4cOGb179zYqVapkeHt7G/7+/kaTJk2M6dOnG+np6Y7jrrVc4vPPP2+UK1fO8PX1NZo0aWIkJibmWurv7bffNpo1a2aUKVPGsNvtRpUqVYyhQ4ca58+fNwzDMDIyMoyhQ4ca9erVM/z9/Q0/Pz+jXr16xowZM/629vfee8+IiYkxQkNDjeLFixulS5c2YmJijA8//DBfn8Hu3buNJ554wggPDzdKlChhlC5d2rjvvvuM+fPnOy0J+ccffxjx8fGO46pWrWq89tprRk5OjtP1JF1z+cerP0NXyyUahmH897//NSpXrmx4e3sb9evXNz755JNcyyWuWLHCaNWqlRESEmJ4e3sbFSpUMJ599lnj9OnTue5x9ZKCS5cuNRo0aGDY7XYjODjY6Natm/HTTz85HRMbG2v4+fnlqm3MmDFGXv6v4K/LJbpyrc/g999/N3r27GnccsstRqlSpYzWrVsbBw8evOYyh7NnzzYqV65sFCtWzOl9VqxY0XjwwQevec+/XufHH380AgMDjXbt2uU67pFHHjH8/PyMY8eO/e17BQBPsRkGv9sDAAAAPI055gAAAIAF0JgDAAAAFkBjDgAAAFgAjTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWECh/OZP3wZxni4BAPT7zjc9XQIAyMdi3Z5ZfdqfX998fweTmAMAAAAWYLGfoQAAAFCo2ciFXeGTAQAAACyAxBwAAADmsdk8XYFlkZgDAAAAFkBiDgAAAPMwx9wlPhkAAADAAkjMAQAAYB7mmLtEYg4AAABYAIk5AAAAzMMcc5f4ZAAAAAALIDEHAACAeZhj7hKJOQAAAGABJOYAAAAwD3PMXeKTAQAAACyAxBwAAADmYY65SyTmAAAAgAWQmAMAAMA8zDF3iU8GAAAAsAAScwAAAJiHOeYukZgDAAAAFkBiDgAAAPMwx9wlPhkAAADAAkjMAQAAYB7mmLtEYg4AAABYAIk5AAAAzMMcc5f4ZAAAAAALIDEHAACAeUjMXeKTAQAAQJG2detWtWvXTuHh4bLZbFq1alWuYw4cOKCHH35YgYGB8vPz0x133KGTJ0869qenp6tfv34qU6aMSpUqpU6dOunMmTP5qoPGHAAAAObxspmz5UNaWprq1aunt95665r7jx49qqZNm6pGjRravHmzvvnmG40aNUo+Pj6OY+Lj47V69WotX75cW7Zs0alTp9SxY8d81WEzDMPI1xk3Ad8GcZ4uAQD0+843PV0CAMjHYhOXfVu+aMp9/vx81A2dZ7PZtHLlSnXo0MEx1rVrV5UoUUILFy685jnnz59X2bJltXjxYnXu3FmSdPDgQUVFRSkxMVF33XVXnu5NYg4AAADz2LxM2TIyMnThwgWnLSMjI9/l5uTkaO3atapWrZpat26tkJAQNW7c2Gm6y+7du5WVlaWYmBjHWI0aNVShQgUlJibm+V405gAAACh0EhISFBgY6LQlJCTk+zpnz55VamqqXn75ZbVp00affvqpHnnkEXXs2FFbtmyRJCUnJ8vb21tBQUFO54aGhio5OTnP97LYLzcAAABQqJn0zZ8jR47U4MGDncbsdnu+r5OTkyNJat++veLj4yVJ9evX1/bt2zVr1iw1b978nxf7/9CYAwAAoNCx2+031Ihf7ZZbblHx4sVVs2ZNp/GoqCht27ZNkhQWFqbMzEydO3fOKTU/c+aMwsLC8nwvprIAAAAALnh7e+uOO+5QUlKS0/ihQ4dUsWJFSVLDhg1VokQJbdy40bE/KSlJJ0+eVHR0dJ7vRWIOAAAA81jwC4ZSU1N15MgRx+vjx49r7969Cg4OVoUKFTR06FA99thjatasmVq2bKn169dr9erV2rx5syQpMDBQvXr10uDBgxUcHKyAgAD1799f0dHReV6RRaIxBwAAQBG3a9cutWzZ0vH6ytz02NhYzZs3T4888ohmzZqlhIQEDRgwQNWrV9f777+vpk2bOs6ZPHmyvLy81KlTJ2VkZKh169aaMWNGvupgHXMAKCCsYw7ACiy3jvn9r5hynz83DDflPu5kvd8lAAAAAEWQxX6GAgAAQKFmwTnmVsEnAwAAAFgAiTkAAADMY9IXDN2MSMwBAAAACyAxBwAAgHmYY+4SnwwAAABgASTmAAAAMA9zzF0iMQcAAAAsgMQcAAAA5mGOuUt8MgAAAIAFkJgDAADAPMwxd4nEHAAAALAAEnMAAACYhznmLvHJAAAAABZAYg4AAADzkJi7xCcDAAAAWACJOQAAAMzDqiwukZgDAAAAFkBiDgAAAPMwx9wlPhkAAADAAkjMAQAAYB7mmLtEYg4AAABYAIk5AAAAzMMcc5f4ZAAAAAALIDEHAACAeZhj7hKJOQAAAGABJOYAAAAwjY3E3CUScwAAAMACSMwBAABgGhJz10jMAQAAAAsgMQcAAIB5CMxdIjEHAAAALIDEHAAAAKZhjrlrJOYAAACABZCYAwAAwDQk5q6RmAMAAAAWQGIOAAAA05CYu0ZiDgAAAFgAiTkAAABMQ2LuGok5AAAAYAEk5gAAADAPgblLJOYAAACABZCYAwAAwDTMMXeNxBwAAACwABJzAAAAmIbE3DUScwAAAMACSMwBAABgGhJz10jMAQAAAAugMQcAAIBpbDabKVt+bN26Ve3atVN4eLhsNptWrVrl8tg+ffrIZrNpypQpTuMpKSnq1q2bAgICFBQUpF69eik1NTVfddCYAwAAoEhLS0tTvXr19NZbb133uJUrV+qrr75SeHh4rn3dunXT999/rw0bNmjNmjXaunWrnnnmmXzVwRxzAAAAmMeCU8zbtm2rtm3bXveYn3/+Wf3799cnn3yiBx980GnfgQMHtH79eu3cuVONGjWSJE2fPl0PPPCAXn/99Ws28tdCYg4AAABcR05Ojp588kkNHTpUtWrVyrU/MTFRQUFBjqZckmJiYuTl5aUdO3bk+T4k5gAAADCNWauyZGRkKCMjw2nMbrfLbrfn+1qvvPKKihcvrgEDBlxzf3JyskJCQpzGihcvruDgYCUnJ+f5PiTmAAAAKHQSEhIUGBjotCUkJOT7Ort379bUqVM1b968Av+hgsQcAAAApjErMR85cqQGDx7sNHYjafkXX3yhs2fPqkKFCo6x7OxsPf/885oyZYpOnDihsLAwnT171um8S5cuKSUlRWFhYXm+F405AAAACp0bnbZytSeffFIxMTFOY61bt9aTTz6pnj17SpKio6N17tw57d69Ww0bNpQkbdq0STk5OWrcuHGe70VjDgAAANNY8Zs/U1NTdeTIEcfr48ePa+/evQoODlaFChVUpkwZp+NLlCihsLAwVa9eXZIUFRWlNm3aqHfv3po1a5aysrIUFxenrl275nlFFok55gAAACjidu3apQYNGqhBgwaSpMGDB6tBgwYaPXp0nq+xaNEi1ahRQ/fdd58eeOABNW3aVO+8806+6iAxBwAAgHmsF5irRYsWMgwjz8efOHEi11hwcLAWL178j+ogMQcAAAAsgMQcAAAAprHiHHOrIDEHAAAALIDEHAAAAKYhMXeNxBwAAACwABJzAAAAmIbE3DUScwAAAMACSMwBAABgGhJz10jMAQAAAAsgMQcAAIB5CMxdIjEHAAAALIDEHAAAAKZhjrlrJOYAAACABZCYAwAAwDQk5q6RmAMAAAAWQGIOAAAA05CYu2aJxPyLL75Q9+7dFR0drZ9//lmStHDhQm3bts3DlQEAAADm8Hhj/v7776t169by9fXV119/rYyMDEnS+fPnNXHiRA9XBwAAALeymbTdhDzemE+YMEGzZs3S7NmzVaJECcd4kyZNtGfPHg9WBgAAAJjH43PMk5KS1KxZs1zjgYGBOnfunPkFAQAAoMAwx9w1jyfmYWFhOnLkSK7xbdu2qXLlyh6oCAAAADCfxxvz3r17a+DAgdqxY4dsNptOnTqlRYsWaciQIXruuec8XR4AAADcyGazmbLdjDw+lWXEiBHKycnRfffdp4sXL6pZs2ay2+0aMmSI+vfv7+nyAAAAAFN4vDG32Wx64YUXNHToUB05ckSpqamqWbOmSpUq5enSUMg0ub2K4p+K0e01K6hc2UB1iX9Hqzd/49j/59dvXvO8/5u8UpMXbJQkDevVWm3vqaW61W5T5qVLKtdsmCm1Ayh6lixepPlz39Wvv/6iatVraMT/jVKdunU9XRbwj92sabYZPN6YX+Ht7a2aNWt6ugwUYn6+dn176Gct+DBRS994Jtf+SjEjnV63alJLs8Y8oZUb9zrGvEsU0wcbvtaOb44rtkN0QZcMoIhav+5jvf5qgv49Zpzq1KmnRQvn67lne+nDNetVpkwZT5cHoIB4vDFv2bLldX9y2rRpk4nVoDD79Mv9+vTL/S73n/ntD6fX7VrU0Zadh3Xi598cYxNmfSxJ6t6uccEUCQCSFs6fq46du6jDI50kSf8eM05bt27Wqg/eV6/euYMF4GZCYu6axxvz+vXrO73OysrS3r179d133yk2NtYzRaHICwn2V5umtdV79EJPlwKgiMnKzNSB/d+rV+9nHWNeXl6666679c2+rz1YGYCC5vHGfPLkydccHzt2rFJTU02uBrise7vG+uNiulZt2uvpUgAUMb+f+13Z2dm5pqyUKVNGx48f81BVgBsRmLvk8eUSXenevbv+85///O1xGRkZunDhgtNm5GSbUCEKs6fa36Wl63YpI/OSp0sBAABFhGUb88TERPn4+PztcQkJCQoMDHTaLp3ZbUKFKKyaNKii6hFhmrtyu6dLAVAElQ4qrWLFium3335zGv/tt990yy23eKgqwH1Yx9w1j09l6dixo9NrwzB0+vRp7dq1S6NGjfrb80eOHKnBgwc7jYXcM9ytNaJoie0Qrd37T+rbQz97uhQARVAJb29F1aylHV8l6t77YiRJOTk52rEjUV0f7+7h6gAUJI835oGBgU6vvby8VL16dY0fP16tWrX62/PtdrvsdrvTmM2rmFtrROHg5+utKuXLOl5XurWM6la7Vb9fuKgfk3+XJPn7+ajj/Q004o2V17xG+bDSKh1QUuXLlVYxLy/VrXarJOnoj78o7c/Mgn8TAIqEJ2N7atT/DVetWrVVu05d/XfhfP3555/q8EjHvz8ZsLibNc02g0cb8+zsbPXs2VN16tRR6dKlPVkKioDba1bUp3MGOl6/OuTyMmQLP/pKz4z5ryTp0dYNZZNNy9bvuuY1Rj33oJ58+C7H6x1LL6993urpqfpi9+GCKh1AEdOm7QP6PSVFM96cpl9//UXVa0RpxttzVIapLEChZjMMw/BkAT4+Pjpw4IAiIiLcdk3fBnFuuxYA3Kjfd17722QBwEw+Hp8f4SxyyDpT7nPk9bam3MedPP7wZ+3atXXsGMs/AQAAoGjzeGM+YcIEDRkyRGvWrNHp06dzLX0IAACAwoNVWVzz2C83xo8fr+eff14PPPCAJOnhhx92+hANw5DNZlN2NmuSAwAAoPDzWGM+btw49enTR59//rmnSgAAAIDJbtIw2xQea8yvPHPavHlzT5UAAAAAWIZHn9O9Wef/AAAA4MbQ/7nm0ca8WrVqf/svJyUlxaRqAAAAAM/xaGM+bty4XN/8CQAAgMKLwNw1jzbmXbt2VUhIiCdLAAAAACzBY40584sAAACKHi8vekBXPPYFQ1dWZQEAAADgwcQ8JyfHU7cGAACAhzBpwjWPJeYAAAAA/n8effgTAAAARQvPGbpGYg4AAABYAI05AAAATGOzmbPlx9atW9WuXTuFh4fLZrNp1apVjn1ZWVkaPny46tSpIz8/P4WHh+upp57SqVOnnK6RkpKibt26KSAgQEFBQerVq5dSU1PzVQeNOQAAAIq0tLQ01atXT2+99VaufRcvXtSePXs0atQo7dmzRx988IGSkpL08MMPOx3XrVs3ff/999qwYYPWrFmjrVu36plnnslXHcwxBwAAgGmsOMe8bdu2atu27TX3BQYGasOGDU5jb775pu68806dPHlSFSpU0IEDB7R+/Xrt3LlTjRo1kiRNnz5dDzzwgF5//XWFh4fnqQ4ScwAAACAfzp8/L5vNpqCgIElSYmKigoKCHE25JMXExMjLy0s7duzI83VJzAEAAGAasxLzjIwMZWRkOI3Z7XbZ7fZ/dN309HQNHz5cjz/+uAICAiRJycnJCgkJcTquePHiCg4OVnJycp6vTWIOAACAQichIUGBgYFOW0JCwj+6ZlZWlrp06SLDMDRz5kw3Vfr/IzEHAACAacyaYj5y5EgNHjzYaeyfpOVXmvIffvhBmzZtcqTlkhQWFqazZ886HX/p0iWlpKQoLCwsz/egMQcAAECh445pK1dcacoPHz6szz//XGXKlHHaHx0drXPnzmn37t1q2LChJGnTpk3KyclR48aN83wfGnMAAACYxoqrsqSmpurIkSOO18ePH9fevXsVHByscuXKqXPnztqzZ4/WrFmj7Oxsx7zx4OBgeXt7KyoqSm3atFHv3r01a9YsZWVlKS4uTl27ds3ziiwSjTkAAACKuF27dqlly5aO11emwMTGxmrs2LH66KOPJEn169d3Ou/zzz9XixYtJEmLFi1SXFyc7rvvPnl5ealTp06aNm1avuqgMQcAAIBpLBiYq0WLFjIMw+X+6+27Ijg4WIsXL/5HdbAqCwAAAGABJOYAAAAwjRXnmFsFiTkAAABgASTmAAAAMA2BuWsk5gAAAIAFkJgDAADANMwxd43EHAAAALAAEnMAAACYhsDcNRJzAAAAwAJIzAEAAGAa5pi7RmIOAAAAWACJOQAAAExDYO4aiTkAAABgASTmAAAAMA1zzF0jMQcAAAAsgMQcAAAApiEwd43EHAAAALAAEnMAAACYhjnmrpGYAwAAABZAYg4AAADTEJi7RmIOAAAAWACJOQAAAEzDHHPXSMwBAAAACyAxBwAAgGlIzF0jMQcAAAAsgMQcAAAApiEwd43EHAAAALAAEnMAAACYhjnmrpGYAwAAABZAYg4AAADTEJi7RmIOAAAAWACJOQAAAEzDHHPXSMwBAAAACyAxBwAAgGkIzF0jMQcAAAAsgMQcAAAApvEiMneJxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgGtYxd43EHAAAALAAEnMAAACYxovA3CUScwAAAMACSMwBAABgGuaYu0ZiDgAAAFgAiTkAAABMQ2DuGok5AAAAYAEk5gAAADCNTUTmrpCYAwAAABZAYw4AAADTeNnM2fJj69atateuncLDw2Wz2bRq1Sqn/YZhaPTo0SpXrpx8fX0VExOjw4cPOx2TkpKibt26KSAgQEFBQerVq5dSU1Pz99nkr2wAAACgcElLS1O9evX01ltvXXP/q6++qmnTpmnWrFnasWOH/Pz81Lp1a6WnpzuO6datm77//ntt2LBBa9as0datW/XMM8/kqw7mmAMAAMA0VlzHvG3btmrbtu019xmGoSlTpujf//632rdvL0lasGCBQkNDtWrVKnXt2lUHDhzQ+vXrtXPnTjVq1EiSNH36dD3wwAN6/fXXFR4enqc6SMwBAAAAF44fP67k5GTFxMQ4xgIDA9W4cWMlJiZKkhITExUUFORoyiUpJiZGXl5e2rFjR57vRWIOAAAA05gVmGdkZCgjI8NpzG63y2635+s6ycnJkqTQ0FCn8dDQUMe+5ORkhYSEOO0vXry4goODHcfkBYk5AAAACp2EhAQFBgY6bQkJCZ4u67pIzAEAAGAaL5Mi85EjR2rw4MFOY/lNyyUpLCxMknTmzBmVK1fOMX7mzBnVr1/fcczZs2edzrt06ZJSUlIc5+cFiTkAAAAKHbvdroCAAKftRhrziIgIhYWFaePGjY6xCxcuaMeOHYqOjpYkRUdH69y5c9q9e7fjmE2bNiknJ0eNGzfO871IzAEAAGAaCy7KotTUVB05csTx+vjx49q7d6+Cg4NVoUIFDRo0SBMmTFDVqlUVERGhUaNGKTw8XB06dJAkRUVFqU2bNurdu7dmzZqlrKwsxcXFqWvXrnlekUWiMQcAAEARt2vXLrVs2dLx+soUmNjYWM2bN0/Dhg1TWlqannnmGZ07d05NmzbV+vXr5ePj4zhn0aJFiouL03333ScvLy916tRJ06ZNy1cdNsMwDPe8JevwbRDn6RIAQL/vfNPTJQCAfCwWw3aeu8eU+6zoebsp93En5pgDAAAAFmCxn6EAAABQmFlxjrlVkJgDAAAAFkBiDgAAANOYtY75zYjEHAAAALCAPCXmH330UZ4v+PDDD99wMQAAACjcyMtdy1NjfmXx9L9js9mUnZ39T+oBAAAAiqQ8NeY5OTkFXQcAAACKABtzzF36R3PM09PT3VUHAAAAUKTluzHPzs7Wiy++qFtvvVWlSpXSsWPHJEmjRo3Su+++6/YCAQAAUHh42czZbkb5bsxfeuklzZs3T6+++qq8vb0d47Vr19acOXPcWhwAAABQVOS7MV+wYIHeeecddevWTcWKFXOM16tXTwcPHnRrcQAAAChcbDabKdvNKN+N+c8//6zIyMhc4zk5OcrKynJLUQAAAEBRk+/GvGbNmvriiy9yja9YsUINGjRwS1EAAAAonGw2c7abUZ6WS/yr0aNHKzY2Vj///LNycnL0wQcfKCkpSQsWLNCaNWsKokYAAACg0Mt3Yt6+fXutXr1an332mfz8/DR69GgdOHBAq1ev1v33318QNQIAAKCQYI65a/lOzCXpnnvu0YYNG9xdCwAAAFBk3VBjLkm7du3SgQMHJF2ed96wYUO3FQUAAIDC6WZdY9wM+W7Mf/rpJz3++OP68ssvFRQUJEk6d+6c7r77bi1ZskS33Xabu2sEAAAACr18zzF/+umnlZWVpQMHDiglJUUpKSk6cOCAcnJy9PTTTxdEjQAAACgkmGPuWr4T8y1btmj79u2qXr26Y6x69eqaPn267rnnHrcWBwAAABQV+W7My5cvf80vEsrOzlZ4eLhbigIAAEDhdHNm2ebI91SW1157Tf3799euXbscY7t27dLAgQP1+uuvu7U4AAAAoKjIU2JeunRpp7k6aWlpaty4sYoXv3z6pUuXVLx4cf3rX/9Shw4dCqRQAAAA3Py8btL532bIU2M+ZcqUAi4DAAAAKNry1JjHxsYWdB0AAAAoAgjMXbvhLxiSpPT0dGVmZjqNBQQE/KOCAAAAgKIo3415Wlqahg8frmXLlum3337LtT87O9sthQEAAKDwuVnXGDdDvldlGTZsmDZt2qSZM2fKbrdrzpw5GjdunMLDw7VgwYKCqBEAAAAo9PKdmK9evVoLFixQixYt1LNnT91zzz2KjIxUxYoVtWjRInXr1q0g6gQAAEAhQGDuWr4T85SUFFWuXFnS5fnkKSkpkqSmTZtq69at7q0OAAAAKCLy3ZhXrlxZx48flyTVqFFDy5Ytk3Q5SQ8KCnJrcQAAAChcvGw2U7abUb4b8549e2rfvn2SpBEjRuitt96Sj4+P4uPjNXToULcXCAAAABQF+Z5jHh8f7/hzTEyMDh48qN27dysyMlJ169Z1a3EAAABAUfGP1jGXpIoVK6pixYruqAUAAACF3E06y8QUeWrMp02blucLDhgw4IaLAQAAAIqqPDXmkydPztPFbDYbjTkAAABc4guGXMtTY35lFRYAAAAABeMfzzG3ol93TPd0CQCgRXtOeroEAFCvOyt4ugQn+V4SsAjhswEAAAAsoFAm5gAAALAm5pi7RmIOAAAAWACJOQAAAEzjRWDu0g0l5l988YW6d++u6Oho/fzzz5KkhQsXatu2bW4tDgAAACgq8t2Yv//++2rdurV8fX319ddfKyMjQ5J0/vx5TZw40e0FAgAAoPDwspmz3Yzy3ZhPmDBBs2bN0uzZs1WiRAnHeJMmTbRnzx63FgcAAAAUFfmeY56UlKRmzZrlGg8MDNS5c+fcURMAAAAKKVZlcS3fiXlYWJiOHDmSa3zbtm2qXLmyW4oCAAAAipp8N+a9e/fWwIEDtWPHDtlsNp06dUqLFi3SkCFD9NxzzxVEjQAAACgkmGPuWr6nsowYMUI5OTm67777dPHiRTVr1kx2u11DhgxR//79C6JGAAAAoNDLd2Jus9n0wgsvKCUlRd99952++uor/fLLL3rxxRcLoj4AAAAUIjabOVteZWdna9SoUYqIiJCvr6+qVKmiF198UYZhOI4xDEOjR49WuXLl5Ovrq5iYGB0+fNjtn80Nf8GQt7e3atas6c5aAAAAAFO98sormjlzpubPn69atWpp165d6tmzpwIDAzVgwABJ0quvvqpp06Zp/vz5ioiI0KhRo9S6dWvt379fPj4+bqsl3415y5Ytr/s07aZNm/5RQQAAACi8vCy2Ksv27dvVvn17Pfjgg5KkSpUq6b333tP//vc/SZfT8ilTpujf//632rdvL0lasGCBQkNDtWrVKnXt2tVtteR7Kkv9+vVVr149x1azZk1lZmZqz549qlOnjtsKAwAAAG5URkaGLly44LRd+WLMv7r77ru1ceNGHTp0SJK0b98+bdu2TW3btpUkHT9+XMnJyYqJiXGcExgYqMaNGysxMdGtNec7MZ88efI1x8eOHavU1NR/XBAAAAAKr3ynwjcoISFB48aNcxobM2aMxo4d6zQ2YsQIXbhwQTVq1FCxYsWUnZ2tl156Sd26dZMkJScnS5JCQ0OdzgsNDXXsc5cbnmN+te7du+vOO+/U66+/7q5LAgAAADdk5MiRGjx4sNOY3W7PddyyZcu0aNEiLV68WLVq1dLevXs1aNAghYeHKzY21qxyJbmxMU9MTHTr5HcAAAAUPmZNMbfb7ddsxK82dOhQjRgxwjFXvE6dOvrhhx+UkJCg2NhYhYWFSZLOnDmjcuXKOc47c+aM6tev79aa892Yd+zY0em1YRg6ffq0du3apVGjRrmtMAAAAKCgXbx4UV5ezhNsihUrppycHElSRESEwsLCtHHjRkcjfuHCBe3YscPtX66Z78Y8MDDQ6bWXl5eqV6+u8ePHq1WrVm4rDAAAAIWP1VZladeunV566SVVqFBBtWrV0tdff6033nhD//rXvyRd/g6fQYMGacKECapatapjucTw8HB16NDBrbXkqzHPzs5Wz549VadOHZUuXdqthQAAAABmmz59ukaNGqW+ffvq7NmzCg8P17PPPqvRo0c7jhk2bJjS0tL0zDPP6Ny5c2ratKnWr1/v9mncNuOvX2uUBz4+Pjpw4IAiIiLcWog7pWXm6y0BQIFYsvdHT5cAAOp1ZwVPl+Bk9Cfu/8bMaxnfuqop93GnfK9YU7t2bR07dqwgagEAAACKrHw35hMmTNCQIUO0Zs0anT59OtfC7QAAAIArXjZztptRnueYjx8/Xs8//7weeOABSdLDDz8s218m7xuGIZvNpuzsbPdXCQAAABRyeW7Mx40bpz59+ujzzz8vyHoAAABQiFltVRYryXNjfuUZ0ebNmxdYMQAAAEBRla/lEm38hAMAAIB/gHbStXw15tWqVfvb5jwlJeUfFQQAAAAURflqzMeNG5frmz8BAACAvLpZV0wxQ74a865duyokJKSgagEAAACKrDw35swvBwAAwD9lEz2lK3n+gqErq7IAAAAAcL88J+Y5OTkFWQcAAACKAOaYu5bnxBwAAABAwcnXw58AAADAP0Fi7hqJOQAAAGABJOYAAAAwDSv9uUZiDgAAAFgAiTkAAABMwxxz10jMAQAAAAsgMQcAAIBpmGLuGok5AAAAYAEk5gAAADCNF5G5SyTmAAAAgAWQmAMAAMA0rMriGok5AAAAYAEk5gAAADANU8xdIzEHAAAALIDEHAAAAKbxEpG5KyTmAAAAgAWQmAMAAMA0zDF3jcQcAAAAsAAScwAAAJiGdcxdIzEHAAAALIDEHAAAAKbxYpK5SyTmAAAAgAWQmAMAAMA0BOaukZgDAAAAFkBiDgAAANMwx9w1EnMAAADAAkjMAQAAYBoCc9dIzAEAAAALIDEHAACAaUiFXeOzAQAAACyAxBwAAACmsTHJ3CUScwAAAMACSMwBAABgGvJy10jMAQAAAAsgMQcAAIBp+OZP10jMAQAAAAugMQcAAIBpbCZt+fHzzz+re/fuKlOmjHx9fVWnTh3t2rXLsd8wDI0ePVrlypWTr6+vYmJidPjw4Rt6/9dDYw4AAIAi6/fff1eTJk1UokQJrVu3Tvv379ekSZNUunRpxzGvvvqqpk2bplmzZmnHjh3y8/NT69atlZ6e7tZamGMOAAAA01htivkrr7yi8uXLa+7cuY6xiIgIx58Nw9CUKVP073//W+3bt5ckLViwQKGhoVq1apW6du3qtlpIzAEAAFDoZGRk6MKFC05bRkZGruM++ugjNWrUSI8++qhCQkLUoEEDzZ4927H/+PHjSk5OVkxMjGMsMDBQjRs3VmJioltrpjEHAACAaWw2mylbQkKCAgMDnbaEhIRc9Rw7dkwzZ85U1apV9cknn+i5557TgAEDNH/+fElScnKyJCk0NNTpvNDQUMc+d2EqCwAAAAqdkSNHavDgwU5jdrs913E5OTlq1KiRJk6cKElq0KCBvvvuO82aNUuxsbGm1HoFiTkAAABM42XSZrfbFRAQ4LRdqzEvV66catas6TQWFRWlkydPSpLCwsIkSWfOnHE65syZM4597kJjDgAAgCKrSZMmSkpKcho7dOiQKlasKOnyg6BhYWHauHGjY/+FCxe0Y8cORUdHu7UWprIAAADANDaLLcsSHx+vu+++WxMnTlSXLl30v//9T++8847eeecdSZfrHTRokCZMmKCqVasqIiJCo0aNUnh4uDp06ODWWmjMAQAAUGTdcccdWrlypUaOHKnx48crIiJCU6ZMUbdu3RzHDBs2TGlpaXrmmWd07tw5NW3aVOvXr5ePj49ba7EZhmG49YoWkJZZ6N4SgJvQkr0/eroEAFCvOyt4ugQny/eeMuU+j9YPN+U+7sQccwAAAMACmMoCAAAA01htjrmVkJgDAAAAFkBiDgAAANOQCrvGZwMAAABYAIk5AAAATMMcc9dIzAEAAAALIDEHAACAacjLXSMxBwAAACyAxBwAAACmYYq5ayTmAAAAgAWQmAMAAMA0Xswyd4nEHAAAALAAEnMAAACYhjnmrpGYAwAAABZAYg4AAADT2Jhj7hKJOQAAAGABJOYAAAAwDXPMXSMxBwAAACyAxBwAAACmYR1z10jMAQAAAAsgMQcAAIBpmGPuGok5AAAAYAEk5gAAADANiblrJOYAAACABZCYAwAAwDR886drJOYAAACABZCYAwAAwDReBOYukZgDAAAAFkBiDgAAANMwx9w1EnMAAADAAizVmGdmZiopKUmXLl3ydCkAAAAoADabOdvNyBKN+cWLF9WrVy+VLFlStWrV0smTJyVJ/fv318svv+zh6gAAAICCZ4nGfOTIkdq3b582b94sHx8fx3hMTIyWLl3qwcoAAADgTjaT/rkZWeLhz1WrVmnp0qW66667ZPvL7x5q1aqlo0ePerAyAAAAwByWaMx/+eUXhYSE5BpPS0tzatQBAABwc2Mdc9csMZWlUaNGWrt2reP1lWZ8zpw5io6O9lRZAAAAgGkskZhPnDhRbdu21f79+3Xp0iVNnTpV+/fv1/bt27VlyxZPlwcAAAA3uVnnf5vBEol506ZNtXfvXl26dEl16tTRp59+qpCQECUmJqphw4aeLg8AAAAocJZIzCWpSpUqmj17tqfLQBG2fOl7Wr70PZ0+9bMkqXKVSD3Tp5+a3NPMw5UBKMz+SPlVW5bO0bFv/qdLGRkKCg1X295DVK5ydUnSx2+/qu+2bXA6J6JOIz06LMET5QL/GI8PumaJxnzPnj0qUaKE6tSpI0n68MMPNXfuXNWsWVNjx46Vt7e3hytEURASGqoBg55XhYoVZRiGVn+0SvED+um95R+oSmRVT5cHoBBKT/tDi14cpApR9fTokIny9Q/U72d+lo+fv9NxEXXvUNveQxyvi5coYXapAExgiakszz77rA4dOiRJOnbsmB577DGVLFlSy5cv17BhwzxcHYqK5i3uVdNmzVWhYiVVrBShuAHxKlmypL79Zp+nSwNQSO1Ys1QBwWX1wDNDVa5KDQWFlFNEnUYqHRrudFyx4iVUKijYsV3duAM3E5tJ283IEon5oUOHVL9+fUnS8uXL1bx5cy1evFhffvmlunbtqilTpni0PhQ92dnZ+uzT9frzz4uqW6++p8sBUEgd2ZOoSnUa6cNp4/XjwW9VKriMGtz3sOq1fMDpuB8P7tObfR+V3a+UKtasr3s695Svf4CHqgZQUCzRmBuGoZycHEnSZ599poceekiSVL58ef3666+eLA1FzOFDSerR/XFlZmbIt2RJTZrypipXifR0WQAKqXO/nNbeTat1R5tOuuvhJ3T6WJI2LnxLxYoXV+17Wkm6PI2l6h1NFVS2nM6dOaWty/+j5a//n7qPmSovr2IefgdA/nkxydwlSzTmjRo10oQJExQTE6MtW7Zo5syZkqTjx48rNDT0uudmZGQoIyPDaeySzVt2u73A6kXhVSkiQu+tWKnUP/7Qxg2faPS/R2jO3IU05wAKhJFjKCyimpp16SVJCq0UqV9/OqG9m9Y4GvOo6JaO48uWj1DZCpX1zvNP6ccD+1Sx1u0eqRtAwbDEHPMpU6Zoz549iouL0wsvvKDIyMtN0IoVK3T33Xdf99yEhAQFBgY6ba+/ypPquDElSnirQoWKqlmrtvoPel7VqtXQ4v8u8HRZAAqpUkHBKnNrBaexMuEVdOG3sy7PCQop9/8eEj1V0OUBBYI55q5ZIjGvW7euvv3221zjr732mooVu/6v6UaOHKnBgwc7jV2ysYoL3CPHyFFWZqanywBQSN1arZZ+P/2T01hK8k8KKOP6t8V/pPyiP1MvyC8ouKDLA2AySzTmrvj4+PztMXa7Pde0lbRMo6BKQiE2fcok3d20mcqVK6e0tDSt/3iNdu/8n96aNcfTpQEopBq16aRF4wcq8aPFqtG4uU4fTdI3n3+sVv8aJEnKTP9TX65cqOp3NJVfYLDOnT2lzUvmqHRouCLqNPJs8cCNulnjbBN4rDEvXbq0bHmc/J+SklLA1QCX/zsb/cJw/frLLyrl76+qVavrrVlzdNfdTTxdGoBCqlzl6uowcKy2LntX21f9V4Flw3Rv9+dUq8l9kiSbl5d++fGYvv9ig9IvpqpU6TKqVLuh7uncQ8VL8NthoLCxGYbhkXh5/vz5eT42NjY2X9cmMQdgBUv2/ujpEgBAve6s8PcHmWjH0fOm3KdxlcAbOu/ll1/WyJEjNXDgQMeS3enp6Xr++ee1ZMkSZWRkqHXr1poxY8bfLlKSXx5LzPPbbAMAAAAFaefOnXr77bdVt25dp/H4+HitXbtWy5cvV2BgoOLi4tSxY0d9+eWXbr2/JVZl+av09HRduHDBaQMAAEDhYLOZs+VXamqqunXrptmzZ6t06dKO8fPnz+vdd9/VG2+8oXvvvVcNGzbU3LlztX37dn311Vdu/GQs0pinpaUpLi5OISEh8vPzU+nSpZ02AAAAID8yMjJyhb1Xf/fNX/Xr108PPvigYmJinMZ3796trKwsp/EaNWqoQoUKSkxMdGvNlmjMhw0bpk2bNmnmzJmy2+2aM2eOxo0bp/DwcC1YwBrSAAAAhYVZ65hf67tuEhKu/V03S5Ys0Z49e665Pzk5Wd7e3goKCnIaDw0NVXJy8o1/ENdgieUSV69erQULFqhFixbq2bOn7rnnHkVGRqpixYpatGiRunXr5ukSAQAAcBO51nfdXOub4X/88UcNHDhQGzZsyNNS3QXJEol5SkqKKleuLEkKCAhwLI/YtGlTbd261ZOlAQAAwJ1MisztdrsCAgKctms15rt379bZs2d1++23q3jx4ipevLi2bNmiadOmqXjx4goNDVVmZqbOnTvndN6ZM2cUFhbm1o/GEo155cqVdfz4cUmX5+wsW7ZM0uUk/epfGwAAAADuct999+nbb7/V3r17HVujRo3UrVs3x59LlCihjRs3Os5JSkrSyZMnFR0d7dZaPDqV5dixY6pUqZJ69uypffv2qXnz5hoxYoTatWunN998U1lZWXrjjTc8WSIAAADcyGaxr/709/dX7dq1ncb8/PxUpkwZx3ivXr00ePBgBQcHKyAgQP3791d0dLTuuusut9bi0ca8atWqOn36tOLj4yVJjz32mKZNm6aDBw9q9+7dioyMzLWOJAAAAGCmyZMny8vLS506dXL6giF389g3f0qSl5eXkpOTFRISIunyTyz79u1zzDe/UXzzJwAr4Js/AViB1b75c/cJc76jpmGlAFPu406WmGMOAAAAFHUencpis9lku+qrma5+DQAAgMKDTs81jzbmhmGoR48ejqVr0tPT1adPH/n5+Tkd98EHH3iiPAAAAMA0Hm3MY2NjnV53797dQ5UAAADAFETmLnm0MZ87d64nbw8AAABYhkcbcwAAABQtVlvH3EpYlQUAAACwABJzAAAAmIYF+FwjMQcAAAAsgMQcAAAApiEwd43EHAAAALAAEnMAAACYh8jcJRJzAAAAwAJIzAEAAGAa1jF3jcQcAAAAsAAScwAAAJiGdcxdIzEHAAAALIDEHAAAAKYhMHeNxBwAAACwABJzAAAAmIfI3CUScwAAAMACSMwBAABgGtYxd43EHAAAALAAEnMAAACYhnXMXSMxBwAAACyAxBwAAACmITB3jcQcAAAAsAAScwAAAJiHyNwlEnMAAADAAkjMAQAAYBrWMXeNxBwAAACwABJzAAAAmIZ1zF0jMQcAAAAsgMQcAAAApiEwd43EHAAAALAAEnMAAACYh8jcJRJzAAAAwAJIzAEAAGAa1jF3jcQcAAAAsAAScwAAAJiGdcxdIzEHAAAALIDEHAAAAKYhMHeNxBwAAACwABJzAAAAmIfI3CUScwAAAMACSMwBAABgGtYxd43EHAAAALAAEnMAAACYhnXMXSMxBwAAACyAxhwAAACmsZm05VVCQoLuuOMO+fv7KyQkRB06dFBSUpLTMenp6erXr5/KlCmjUqVKqVOnTjpz5swNvf/roTEHAABAkbVlyxb169dPX331lTZs2KCsrCy1atVKaWlpjmPi4+O1evVqLV++XFu2bNGpU6fUsWNHt9diMwzDcPtVPSwts9C9JQA3oSV7f/R0CQCgXndW8HQJTk78lm7KfSqV8bmh83755ReFhIRoy5Ytatasmc6fP6+yZctq8eLF6ty5syTp4MGDioqKUmJiou666y631UxiDgAAAPw/58+flyQFBwdLknbv3q2srCzFxMQ4jqlRo4YqVKigxMREt96bVVkAAABgGrPWMc/IyFBGRobTmN1ul91ud3lOTk6OBg0apCZNmqh27dqSpOTkZHl7eysoKMjp2NDQUCUnJ7u1ZhJzAAAAFDoJCQkKDAx02hISEq57Tr9+/fTdd99pyZIlJlXpjMQcAAAApjFrHfORI0dq8ODBTmPXS8vj4uK0Zs0abd26VbfddptjPCwsTJmZmTp37pxTan7mzBmFhYW5tWYScwAAABQ6drtdAQEBTtu1GnPDMBQXF6eVK1dq06ZNioiIcNrfsGFDlShRQhs3bnSMJSUl6eTJk4qOjnZrzSTmAAAAMI3VvvizX79+Wrx4sT788EP5+/s75o0HBgbK19dXgYGB6tWrlwYPHqzg4GAFBASof//+io6OduuKLBKNOQAAAIqwmTNnSpJatGjhND537lz16NFDkjR58mR5eXmpU6dOysjIUOvWrTVjxgy318I65gBQQFjHHIAVWG0d859+z/j7g9zgttKu55NbFXPMAQAAAAtgKgsAAABMZLVZ5tZBYg4AAABYAIk5AAAATGPWOuY3IxJzAAAAwAJIzAEAAGAaAnPXSMwBAAAAC6AxBwAAACyAqSwAAAAwDQ9/ukZiDgAAAFgAiTkAAABMY+PxT5dIzAEAAAALIDEHAACAeQjMXSIxBwAAACyAxBwAAACmITB3jcQcAAAAsAAScwAAAJiGdcxdIzEHAAAALIDEHAAAAKZhHXPXSMwBAAAACyAxBwAAgHkIzF0iMQcAAAAsgMQcAAAApiEwd43EHAAAALAAEnMAAACYhnXMXSMxBwAAACyAxBwAAACmYR1z10jMAQAAAAsgMQcAAIBpmGPuGok5AAAAYAE05gAAAIAF0JgDAAAAFsAccwAAAJiGOeaukZgDAAAAFkBiDgAAANOwjrlrJOYAAACABZCYAwAAwDTMMXeNxBwAAACwABJzAAAAmIbA3DUScwAAAMACSMwBAABgHiJzl0jMAQAAAAsgMQcAAIBpWMfcNRJzAAAAwAJIzAEAAGAa1jF3jcQcAAAAsAAScwAAAJiGwNw1EnMAAADAAkjMAQAAYB4ic5dIzAEAAFDkvfXWW6pUqZJ8fHzUuHFj/e9//zO9BhpzAAAAmMZm0j/5sXTpUg0ePFhjxozRnj17VK9ePbVu3Vpnz54toE/h2mjMAQAAUKS98cYb6t27t3r27KmaNWtq1qxZKlmypP7zn/+YWgeNOQAAAExjs5mz5VVmZqZ2796tmJgYx5iXl5diYmKUmJhYAJ+Aazz8CQAAgEInIyNDGRkZTmN2u112u91p7Ndff1V2drZCQ0OdxkNDQ3Xw4MECr/OvCmVj7ufN4774ZzIyMpSQkKCRI0fm+h8wkFe97qzg6RJwE+PvIRRWPiZ1n2MnJGjcuHFOY2PGjNHYsWPNKeAG2AzDMDxdBGA1Fy5cUGBgoM6fP6+AgABPlwOgCOLvIeCfyWtinpmZqZIlS2rFihXq0KGDYzw2Nlbnzp3Thx9+aEa5kphjDgAAgELIbrcrICDAabvWb5+8vb3VsGFDbdy40TGWk5OjjRs3Kjo62sySC+dUFgAAACCvBg8erNjYWDVq1Eh33nmnpkyZorS0NPXs2dPUOmjMAQAAUKQ99thj+uWXXzR69GglJyerfv36Wr9+fa4HQgsajTlwDXa7XWPGjOGBKwAew99DgLni4uIUFxfn0Rp4+BMAAACwAB7+BAAAACyAxhwAAACwABpzAAAsZt68eQoKCvJ0GQBMRmOOQs1ms113s/K3fwG4+fXo0eOaf/ccOXLE06UBsCBWZUGhdvr0acefly5dqtGjRyspKckxVqpUKcefDcNQdna2ihfnfxYA3KdNmzaaO3eu01jZsmU9VA0AKyMxR6EWFhbm2AIDA2Wz2RyvDx48KH9/f61bt04NGzaU3W7Xtm3b1KNHD6ev5JWkQYMGqUWLFo7XOTk5SkhIUEREhHx9fVWvXj2tWLHC3DcH4KZgt9ud/i4KCwvT1KlTVadOHfn5+al8+fLq27evUlNTXV5j3759atmypfz9/RUQEKCGDRtq165djv3btm3TPffcI19fX5UvX14DBgxQWlqaGW8PgBvRmKPIGzFihF5++WUdOHBAdevWzdM5CQkJWrBggWbNmqXvv/9e8fHx6t69u7Zs2VLA1QIoDLy8vDRt2jR9//33mj9/vjZt2qRhw4a5PL5bt2667bbbtHPnTu3evVsjRoxQiRIlJElHjx5VmzZt1KlTJ33zzTdaunSptm3b5vH1mAHkH7+zR5E3fvx43X///Xk+PiMjQxMnTtRnn32m6OhoSVLlypW1bds2vf3222revHlBlQrgJrRmzRqnaXNt27bV8uXLHa8rVaqkCRMmqE+fPpoxY8Y1r3Hy5EkNHTpUNWrUkCRVrVrVsS8hIUHdunXToEGDHPumTZum5s2ba+bMmfLx8SmAdwWgINCYo8hr1KhRvo4/cuSILl68mKuZz8zMVIMGDdxZGoBCoGXLlpo5c6bjtZ+fnz777DMlJCTo4MGDunDhgi5duqT09HRdvHhRJUuWzHWNwYMH6+mnn9bChQsVExOjRx99VFWqVJF0eZrLN998o0WLFjmONwxDOTk5On78uKKiogr+TQJwCxpzFHl+fn5Or728vHT1F+JmZWU5/nxlHujatWt16623Oh3HV2cDuJqfn58iIyMdr0+cOKGHHnpIzz33nF566SUFBwdr27Zt6tWrlzIzM6/ZmI8dO1ZPPPGE1q5dq3Xr1mnMmDFasmSJHnnkEaWmpurZZ5/VgAEDcp1XoUKFAn1vANyLxhy4StmyZfXdd985je3du9cxn7NmzZqy2+06efIk01YA5Nvu3buVk5OjSZMmycvr8qNey5Yt+9vzqlWrpmrVqik+Pl6PP/645s6dq0ceeUS333679u/f79T8A7g58fAncJV7771Xu3bt0oIFC3T48GGNGTPGqVH39/fXkCFDFB8fr/nz5+vo0aPas2ePpk+frvnz53uwcgA3g8jISGVlZWn69Ok6duyYFi5cqFmzZrk8/s8//1RcXJw2b96sH374QV9++aV27tzpmKIyfPhwbd++XXFxcdq7d68OHz6sDz/8kIc/gZsQjTlwldatW2vUqFEaNmyY7rjjDv3xxx966qmnnI558cUXNWrUKCUkJCgqKkpt2rTR2rVrFRER4aGqAdws6tWrpzfeeEOvvPKKateurUWLFikhIcHl8cWKFdNvv/2mp556StWqVVOXLl3Utm1bjRs3TpJUt25dbdmyRYcOHdI999yjBg0aaPTo0QoPDzfrLQFwE5tx9WRaAAAAAKYjMQcAAAAsgMYcAAAAsAAacwAAAMACaMwBAAAAC6AxBwAAACyAxhwAAACwABpzAAAAwAJozAEAAAALoDEHUGT16NFDHTp0cLxu0aKFBg0aZHodmzdvls1m07lz51weY7PZtGrVqjxfc+zYsapfv/4/quvEiROy2Wzau3fvP7oOACBvaMwBWEqPHj1ks9lks9nk7e2tyMhIjR8/XpcuXSrwe3/wwQd68cUX83RsXpppAADyo7inCwCAq7Vp00Zz585VRkaGPv74Y/Xr108lSpTQyJEjcx2bmZkpb29vt9w3ODjYLdcBAOBGkJgDsBy73a6wsDBVrFhRzz33nGJiYvTRRx9J+v+nn7z00ksKDw9X9erVJUk//vijunTpoqCgIAUHB6t9+/Y6ceKE45rZ2dkaPHiwgoKCVKZMGQ0bNkyGYTjd9+qpLBkZGRo+fLjKly8vu92uyMhIvfvuuzpx4oRatmwpSSpdurRsNpt69OghScrJyVFCQoIiIiLk6+urevXqacWKFU73+fjjj1WtWjX5+vqqZcuWTnXm1fDhw1WtWjWVLFlSlStX1qhRo5SVlZXruLffflvly5dXyZIl1aVLF50/f95p/5w5cxQVFSUfHx/VqFFDM2bMyHctAAD3oDEHYHm+vr7KzMx0vN64caOSkpK0YcMGrVmzRllZWWrdurX8/f31xRdf6Msvv1SpUqXUpk0bx3mTJk3SvHnz9J///Efbtm1TSkqKVq5ced37PvXUU3rvvfc0bdo0HThwQG+//bZKlSql8uXL6/3335ckJSUl6fTp05o6daokKSEhQQsWLNCsWbP0/fffKz4+Xt27d9eWLVskXf4BomPHjmrXrp327t2rp59+WiNGjMj3Z+Lv76958+Zp//79mjp1qmbPnq3Jkyc7HXPkyBEtW7ZMq1ev1vr16/X111+rb9++jv2LFi3S6NGj9dJLL+nAgQOaOHGiRo0apfnz5+e7HgCAGxgAYCGxsbFG+/btDcMwjJycHGPDhg2G3W43hgwZ4tgfGhpqZGRkOM5ZuHChUb16dSMnJ8cxlpGRYfj6+hqffPKJYRiGUa5cOePVV1917M/KyjJuu+02x70MwzCaN29uDBw40DAMw0hKSjIkGRs2bLhmnZ9//rkhyfj9998dY+np6UbJkiWN7du3Ox3bq1cv4/HHHzcMwzBGjhxp1KxZ02n/8OHDc13rapKMlStXutz/2muvGQ0bNnS8HjNmjFGsWDHjp59+coytW7fO8PLyMk6fPm0YhmFUqVLFWLx4sdN1XnzxRSM6OtowDMM4fvy4Icn4+uuvXd4XAOA+zDEHYDlr1qxRqVKllJWVpZycHD3xxBMaO3asY3+dOnWc5pXv27dPR44ckb+/v9N10tPTdfToUZ0/f16nT59W48aNHfuKFy+uRo0a5ZrOcsXevXtVrFgxNW/ePM91HzlyRBcvXtT999/vNJ6ZmakGDRpIkg4cOOBUhyRFR0fn+R5XLF26VNOmTdPRo0eVmpqqS5cuKSAgwOmYChUq6NZbb3W6T05OjpKSkuTv76+jR4+qV69e6t27t+OYS5cuKTAwMN/1AAD+ORpzAJbTsmVLzZw5U97e3goPD1fx4s5/Vfn5+Tm9Tk1NVcOGDbVo0aJc1ypbtuwN1eDr65vvc1JTUyVJa9eudWqIpcvz5t0lMTFR3bp107hx49S6dWsFBgZqyZIlmjRpUr5rnT17dq4fFIoVK+a2WgEAeUdjDsBy/Pz8FBkZmefjb7/9di1dulQhISG5UuMrypUrpx07dqhZs2aSLifDu3fv1u23337N4+vUqaOcnBxt2bJFMTExufZfSeyzs7MdYzVr1pTdbtfJkyddJu1RUVGOB1mv+Oqrr/7+Tf7F9u3bVbFiRb3wwguOsR9++CHXcSdPntSpU6cUHh7uuI+Xl5eqV6+u0NBQhYeH69ixY+rWrVu+7g8AKBg8/AngptetWzfdcsstat++vb744gsdP35cmzdv1oABA/TTTz9JkgYOHKiXX35Zq1at0sGDB9W3b9/rrkFeqVIlxcbG6l//+pdWrVrluOayZcskSRUrVpTNZtOaNWv0yy+/KDU1Vf7+/hoyZIji4+M1f/58HT16VHv27NH06dMdD1T26dNHhw8f1tChQ5WUlKTFixdr3rx5+Xq/VatW1cmTJ7VkyRIdPXpU06ZNu+aDrD4+PoqNjdW+ffv0xRdfaMCAAerSpYvCwsIkSePGjVNCQoKmTZumQ4cO6dtvv9XcuXP1xhtv5KseAIB70JgDuOmVLFlSW7duVYUKFdSxY0dFRUWpV69eSk9PdyTozz//vJ588knFxsYqOjpa/v7+euSRR6573ZkzZ6pz587q27evatSood69eystLU2SdOutt2rcuHEaMWKEQkNDFRcXJ0l68cUXNWrUKCUkJCgqKkpt2rTR2rVrFRERIenyvO/3339fq1atUr169TRr1ixNnDgxX+/34YcfVnx8vOLi4lS/fn1t375do0aNynVcZGSkOnbsqAceeECtWrVS3bp1nZZDfPrppzVnzhzNnTtXderUUfPmzTVv3jxHrQAAc9kMV08+AQAAADANiTkAAABgATTmAAAAgAXQmAMAAAAWQGMOAAAAWACNOQAAAGABNOYAAACABdCYAwAAABZAYw4AAABYAI05AAAAYAE05gAAAIAF0JgDAAAAFkBjDgAAAFjA/wentu4cK8CvqQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x1800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(bench_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5fffb7",
   "metadata": {},
   "source": [
    "### Squat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "352f5823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 4\n",
    "squat_model = DeepClassifier(input_size, output_size)\n",
    "squat_criterion = nn.BCELoss()\n",
    "squat_optimizer = optim.Adam(squat_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8a9e4e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "squat_train_losses = []\n",
    "squat_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    squat_model.train()\n",
    "    squat_optimizer.zero_grad()\n",
    "    squat_outputs = squat_model(squat_X_train)\n",
    "    squat_loss = squat_criterion(squat_outputs, squat_y_train)\n",
    "    squat_loss.backward()\n",
    "    squat_optimizer.step()\n",
    "    squat_train_losses.append(squat_loss.item())\n",
    "    \n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        squat_test_outputs = squat_model(squat_X_test)\n",
    "        squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "        squat_test_losses.append(squat_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), squat_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), squat_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a269f137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.029156075790524483\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    squat_test_outputs = squat_model(squat_X_test)\n",
    "    squat_test_loss = squat_criterion(squat_test_outputs, squat_y_test)\n",
    "    print(f'Test Loss: {squat_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1ea2e0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 4.5236e-01,  4.1871e-01, -2.8348e-01,  9.9989e-01,  4.6158e-01,\n",
      "         4.0100e-01, -3.2731e-01,  9.9993e-01,  4.6837e-01,  3.9995e-01,\n",
      "        -3.2711e-01,  9.9994e-01,  4.7580e-01,  3.9887e-01, -3.2720e-01,\n",
      "         9.9995e-01,  4.5358e-01,  4.0017e-01, -2.4828e-01,  9.9991e-01,\n",
      "         4.5428e-01,  3.9869e-01, -2.4842e-01,  9.9991e-01,  4.5534e-01,\n",
      "         3.9724e-01, -2.4840e-01,  9.9990e-01,  5.3008e-01,  3.9589e-01,\n",
      "        -4.0778e-01,  9.9994e-01,  5.0782e-01,  3.9474e-01, -5.4810e-02,\n",
      "         9.9989e-01,  4.8539e-01,  4.3890e-01, -3.1249e-01,  9.9994e-01,\n",
      "         4.7454e-01,  4.3796e-01, -2.1157e-01,  9.9987e-01,  6.9924e-01,\n",
      "         4.6894e-01, -5.1376e-01,  9.9987e-01,  5.6583e-01,  4.5387e-01,\n",
      "         1.0450e-01,  9.9867e-01,  6.2982e-01,  6.0557e-01, -4.3128e-01,\n",
      "         9.4870e-01,  4.8136e-01,  5.0404e-01,  4.4024e-02,  2.4518e-01,\n",
      "         4.9365e-01,  4.8623e-01, -4.8336e-03,  9.4117e-01,  4.6622e-01,\n",
      "         4.7132e-01, -8.9472e-02,  2.5062e-01,  4.6534e-01,  4.6180e-01,\n",
      "        -4.4401e-04,  9.0665e-01,  4.5504e-01,  4.5016e-01, -9.5967e-02,\n",
      "         2.4424e-01,  4.9022e-01,  4.4863e-01, -3.5104e-02,  9.0649e-01,\n",
      "         4.8712e-01,  4.4459e-01, -6.7657e-02,  2.3571e-01,  5.0687e-01,\n",
      "         4.5754e-01, -5.6842e-03,  8.8503e-01,  4.9927e-01,  4.5168e-01,\n",
      "        -8.4500e-02,  2.3391e-01,  9.3259e-01,  6.2389e-01, -1.8731e-01,\n",
      "         9.9993e-01,  8.0651e-01,  6.0461e-01,  1.8760e-01,  9.9972e-01,\n",
      "         7.4577e-01,  7.0876e-01, -6.7939e-01,  9.9739e-01,  5.5250e-01,\n",
      "         5.7792e-01,  2.5931e-01,  7.6853e-01,  8.5565e-01,  8.5121e-01,\n",
      "        -5.6631e-01,  9.8930e-01,  6.5482e-01,  7.1740e-01,  3.8205e-01,\n",
      "         7.4937e-01,  9.0086e-01,  8.6844e-01, -5.5618e-01,  9.5964e-01,\n",
      "         6.9425e-01,  7.3836e-01,  3.8631e-01,  7.2585e-01,  7.6157e-01,\n",
      "         9.1448e-01, -7.5414e-01,  9.8491e-01,  5.3472e-01,  7.3645e-01,\n",
      "         3.1944e-01,  8.3913e-01])\n",
      "실제 타겟 데이터: tensor([0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.0000', '0.0014', '0.9986', '0.0000']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.5386,  0.4936, -0.8620,  1.0000,  0.5465,  0.4904, -0.8805,  1.0000,\n",
      "         0.5537,  0.4894, -0.8803,  1.0000,  0.5605,  0.4887, -0.8805,  1.0000,\n",
      "         0.5279,  0.4903, -0.8737,  1.0000,  0.5222,  0.4895, -0.8739,  1.0000,\n",
      "         0.5169,  0.4884, -0.8740,  1.0000,  0.5758,  0.4793, -0.7725,  1.0000,\n",
      "         0.5063,  0.4809, -0.7425,  1.0000,  0.5504,  0.4912, -0.8015,  1.0000,\n",
      "         0.5298,  0.4914, -0.7936,  1.0000,  0.6303,  0.4622, -0.5538,  0.9999,\n",
      "         0.4599,  0.4637, -0.5611,  1.0000,  0.6894,  0.5161, -0.7974,  0.9454,\n",
      "         0.3746,  0.5199, -0.7758,  0.9964,  0.6724,  0.4908, -1.1604,  0.9049,\n",
      "         0.4011,  0.4838, -1.0764,  0.9924,  0.6745,  0.4820, -1.2336,  0.8410,\n",
      "         0.4017,  0.4734, -1.1466,  0.9743,  0.6684,  0.4744, -1.2153,  0.8306,\n",
      "         0.4129,  0.4709, -1.1198,  0.9713,  0.6638,  0.4796, -1.1617,  0.7519,\n",
      "         0.4171,  0.4745, -1.0718,  0.9540,  0.5872,  0.5511,  0.0187,  0.9999,\n",
      "         0.4880,  0.5496, -0.0189,  1.0000,  0.7216,  0.5704, -0.5013,  0.9894,\n",
      "         0.3647,  0.5668, -0.5149,  0.9948,  0.6857,  0.6529, -0.1105,  0.9772,\n",
      "         0.3742,  0.6534, -0.1441,  0.9959,  0.6614,  0.6653, -0.0800,  0.9511,\n",
      "         0.3963,  0.6674, -0.1148,  0.9742,  0.7338,  0.6865, -0.2494,  0.9626,\n",
      "         0.3181,  0.6820, -0.2893,  0.9890])\n",
      "실제 타겟 데이터: tensor([0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.0000', '0.0013', '0.9986', '0.0001']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.5317,  0.4466, -0.9999,  1.0000,  0.5411,  0.4354, -0.9937,  1.0000,\n",
      "         0.5510,  0.4336, -0.9935,  1.0000,  0.5605,  0.4310, -0.9937,  1.0000,\n",
      "         0.5217,  0.4365, -0.9772,  1.0000,  0.5168,  0.4354, -0.9774,  1.0000,\n",
      "         0.5116,  0.4343, -0.9774,  1.0000,  0.5778,  0.4238, -0.8291,  1.0000,\n",
      "         0.5080,  0.4295, -0.7540,  1.0000,  0.5512,  0.4523, -0.9294,  1.0000,\n",
      "         0.5242,  0.4531, -0.9079,  1.0000,  0.6472,  0.4571, -0.6168,  1.0000,\n",
      "         0.4679,  0.4596, -0.5590,  1.0000,  0.7326,  0.5256, -0.9144,  0.9969,\n",
      "         0.3775,  0.5222, -0.8665,  0.9974,  0.6915,  0.4832, -1.2994,  0.9921,\n",
      "         0.3997,  0.4725, -1.2799,  0.9955,  0.6862,  0.4660, -1.3680,  0.9744,\n",
      "         0.3999,  0.4573, -1.3528,  0.9845,  0.6754,  0.4581, -1.3316,  0.9668,\n",
      "         0.4128,  0.4525, -1.3065,  0.9782,  0.6730,  0.4617, -1.2912,  0.9205,\n",
      "         0.4177,  0.4574, -1.2696,  0.9561,  0.6158,  0.5461, -0.0018,  0.9998,\n",
      "         0.5176,  0.5441,  0.0017,  0.9999,  0.7083,  0.5935, -0.6433,  0.9892,\n",
      "         0.4177,  0.5832, -0.5656,  0.9926,  0.6416,  0.6589, -0.0646,  0.9775,\n",
      "         0.4564,  0.6513, -0.0288,  0.9943,  0.6188,  0.6672, -0.0212,  0.9448,\n",
      "         0.4760,  0.6641,  0.0124,  0.9744,  0.6729,  0.6960, -0.2297,  0.9552,\n",
      "         0.4153,  0.6878, -0.1879,  0.9834])\n",
      "실제 타겟 데이터: tensor([0., 1., 0., 0.])\n",
      "예측된 데이터: tensor(1)\n",
      "예측 확률: ['0.0000', '0.9964', '0.0000', '0.0036']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.5357,  0.5422, -1.1306,  0.9999,  0.5472,  0.5379, -1.1435,  0.9999,\n",
      "         0.5558,  0.5364, -1.1433,  0.9999,  0.5642,  0.5345, -1.1436,  0.9998,\n",
      "         0.5299,  0.5359, -1.1358,  0.9999,  0.5254,  0.5342, -1.1359,  0.9999,\n",
      "         0.5210,  0.5324, -1.1359,  0.9999,  0.5817,  0.5221, -1.0089,  0.9999,\n",
      "         0.5126,  0.5188, -0.9771,  1.0000,  0.5522,  0.5353, -1.0630,  0.9999,\n",
      "         0.5277,  0.5390, -1.0542,  0.9999,  0.6500,  0.5115, -0.7789,  0.9999,\n",
      "         0.4668,  0.5011, -0.7393,  1.0000,  0.7338,  0.5391, -0.9696,  0.9792,\n",
      "         0.3841,  0.5369, -0.9413,  0.9970,  0.6990,  0.5520, -1.2519,  0.9033,\n",
      "         0.4175,  0.5478, -1.2327,  0.9837,  0.6886,  0.5585, -1.3303,  0.8376,\n",
      "         0.4263,  0.5516, -1.3030,  0.9484,  0.6763,  0.5490, -1.3192,  0.8381,\n",
      "         0.4407,  0.5480, -1.2904,  0.9474,  0.6749,  0.5464, -1.2512,  0.7689,\n",
      "         0.4415,  0.5464, -1.2309,  0.9116,  0.6116,  0.5376, -0.0085,  0.9998,\n",
      "         0.5153,  0.5378,  0.0086,  0.9999,  0.6693,  0.5834, -0.5742,  0.9455,\n",
      "         0.4459,  0.5757, -0.5828,  0.9807,  0.6335,  0.6564, -0.2212,  0.9039,\n",
      "         0.4637,  0.6546, -0.2059,  0.9833,  0.6149,  0.6679, -0.2016,  0.7822,\n",
      "         0.4795,  0.6673, -0.1834,  0.9122,  0.6609,  0.6924, -0.4271,  0.9221,\n",
      "         0.4259,  0.6846, -0.4016,  0.9766])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0000', '0.0000', '0.0000', '1.0000']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.5227,  0.3116, -0.9431,  1.0000,  0.5334,  0.3000, -0.9500,  1.0000,\n",
      "         0.5411,  0.2989, -0.9499,  1.0000,  0.5500,  0.2974, -0.9501,  0.9999,\n",
      "         0.5198,  0.3001, -0.9254,  1.0000,  0.5163,  0.2995, -0.9256,  1.0000,\n",
      "         0.5131,  0.2988, -0.9256,  1.0000,  0.5772,  0.2942, -0.8042,  0.9999,\n",
      "         0.5213,  0.2963, -0.6692,  1.0000,  0.5382,  0.3191, -0.8797,  1.0000,\n",
      "         0.5212,  0.3188, -0.8327,  1.0000,  0.6542,  0.3400, -0.5663,  0.9999,\n",
      "         0.4768,  0.3384, -0.4733,  1.0000,  0.7398,  0.4094, -0.8768,  0.9900,\n",
      "         0.3873,  0.3991, -0.7358,  0.9979,  0.6910,  0.3525, -1.3006,  0.9580,\n",
      "         0.4169,  0.3440, -1.2017,  0.9928,  0.6844,  0.3380, -1.3821,  0.8845,\n",
      "         0.4223,  0.3295, -1.2887,  0.9682,  0.6727,  0.3316, -1.3551,  0.8643,\n",
      "         0.4313,  0.3235, -1.2565,  0.9575,  0.6700,  0.3361, -1.2978,  0.7655,\n",
      "         0.4335,  0.3285, -1.1994,  0.9212,  0.6064,  0.4647, -0.0104,  0.9997,\n",
      "         0.5080,  0.4613,  0.0101,  0.9998,  0.6046,  0.5700, -0.2303,  0.9865,\n",
      "         0.4757,  0.5648, -0.1742,  0.9892,  0.5818,  0.6569,  0.2436,  0.9782,\n",
      "         0.4774,  0.6544,  0.2872,  0.9929,  0.5674,  0.6686,  0.2786,  0.9115,\n",
      "         0.4926,  0.6663,  0.3221,  0.9336,  0.5979,  0.6959,  0.0372,  0.9682,\n",
      "         0.4567,  0.6933,  0.0912,  0.9843])\n",
      "실제 타겟 데이터: tensor([1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.9941', '0.0057', '0.0000', '0.0002']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_70819/3174289499.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = squat_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = squat_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    squat_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = squat_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7d1e3234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.974\n",
      "정밀도: 0.991\n",
      "재현율: 0.974\n",
      "F1 점수: 0.983\n"
     ]
    }
   ],
   "source": [
    "squat_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = squat_model(squat_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(squat_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(squat_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e57f97ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x2400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(squat_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50889ab8",
   "metadata": {},
   "source": [
    "### Deadlift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d9c8d471",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 생성\n",
    "input_size = 132\n",
    "output_size = 4\n",
    "deadlift_model = DeepClassifier(input_size, output_size)\n",
    "deadlift_criterion = nn.BCELoss()\n",
    "deadlift_optimizer = optim.Adam(deadlift_model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2a73ae3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "deadlift_train_losses = []\n",
    "deadlift_test_losses = []\n",
    "\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    deadlift_model.train()\n",
    "    deadlift_optimizer.zero_grad()\n",
    "    deadlift_outputs = deadlift_model(deadlift_X_train)\n",
    "    deadlift_loss = deadlift_criterion(deadlift_outputs, deadlift_y_train)\n",
    "    deadlift_loss.backward()\n",
    "    deadlift_optimizer.step()\n",
    "    deadlift_train_losses.append(deadlift_loss.item())\n",
    "    \n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "        deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "        deadlift_test_losses.append(deadlift_test_loss.item())\n",
    "\n",
    "plt.plot(range(1, epochs+1), deadlift_train_losses, label='Train Loss')\n",
    "plt.plot(range(1, epochs+1), deadlift_test_losses, label='Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f733762f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.19785548746585846\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    deadlift_test_outputs = deadlift_model(deadlift_X_test)\n",
    "    deadlift_test_loss = deadlift_criterion(deadlift_test_outputs, deadlift_y_test)\n",
    "    print(f'Test Loss: {deadlift_test_loss.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "262be563",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "샘플 1:\n",
      "입력 데이터: tensor([ 0.5574,  0.4807, -0.3261,  0.9990,  0.5749,  0.4843, -0.3330,  0.9986,\n",
      "         0.5777,  0.4837, -0.3333,  0.9983,  0.5810,  0.4831, -0.3335,  0.9990,\n",
      "         0.5730,  0.4835, -0.3924,  0.9986,  0.5739,  0.4825, -0.3926,  0.9982,\n",
      "         0.5747,  0.4814, -0.3928,  0.9988,  0.5972,  0.4709, -0.3080,  0.9984,\n",
      "         0.5830,  0.4686, -0.5380,  0.9984,  0.5535,  0.4713, -0.2864,  0.9993,\n",
      "         0.5527,  0.4707, -0.3612,  0.9993,  0.5941,  0.4338, -0.1175,  0.9999,\n",
      "         0.5056,  0.4281, -0.5914,  1.0000,  0.5647,  0.5094,  0.0765,  0.5164,\n",
      "         0.4207,  0.5100, -0.5872,  0.9899,  0.5431,  0.5712,  0.1024,  0.5888,\n",
      "         0.3721,  0.5984, -0.5637,  0.9771,  0.5462,  0.5862,  0.1129,  0.4901,\n",
      "         0.3594,  0.6157, -0.6323,  0.9580,  0.5470,  0.5868,  0.0440,  0.5120,\n",
      "         0.3727,  0.6179, -0.6314,  0.9601,  0.5434,  0.5818,  0.0761,  0.5016,\n",
      "         0.3828,  0.6102, -0.5643,  0.9393,  0.3857,  0.4611,  0.1571,  0.9995,\n",
      "         0.3227,  0.4492, -0.1566,  0.9996,  0.4500,  0.5547,  0.0834,  0.4662,\n",
      "         0.4190,  0.5474, -0.1857,  0.9129,  0.3924,  0.6629,  0.1673,  0.5233,\n",
      "         0.4070,  0.6717, -0.0539,  0.8803,  0.3612,  0.6708,  0.1716,  0.5251,\n",
      "         0.3812,  0.6847, -0.0424,  0.8283,  0.4432,  0.6963,  0.0775,  0.6177,\n",
      "         0.4715,  0.7137, -0.1211,  0.8841])\n",
      "실제 타겟 데이터: tensor([0., 0., 0., 1.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.0610', '0.0002', '0.0013', '0.9376']\n",
      "\n",
      "샘플 2:\n",
      "입력 데이터: tensor([ 0.4894,  0.1973, -0.4686,  0.9997,  0.4845,  0.1852, -0.4396,  0.9992,\n",
      "         0.4853,  0.1850, -0.4396,  0.9993,  0.4859,  0.1848, -0.4395,  0.9994,\n",
      "         0.4749,  0.1844, -0.4908,  0.9995,  0.4682,  0.1835, -0.4909,  0.9995,\n",
      "         0.4605,  0.1827, -0.4910,  0.9995,  0.4639,  0.1870, -0.2499,  0.9984,\n",
      "         0.4275,  0.1850, -0.4832,  0.9990,  0.4867,  0.2083, -0.3911,  0.9997,\n",
      "         0.4741,  0.2079, -0.4586,  0.9998,  0.4894,  0.2613, -0.0011,  0.9997,\n",
      "         0.3588,  0.2528, -0.5512,  0.9999,  0.5038,  0.3468,  0.1483,  0.7564,\n",
      "         0.3648,  0.3479, -0.5920,  0.9924,  0.5532,  0.4142,  0.0331,  0.8609,\n",
      "         0.4179,  0.4308, -0.6223,  0.9812,  0.5664,  0.4358,  0.0061,  0.7983,\n",
      "         0.4129,  0.4523, -0.6914,  0.9678,  0.5634,  0.4370, -0.0576,  0.8151,\n",
      "         0.4338,  0.4521, -0.7168,  0.9663,  0.5570,  0.4302,  0.0015,  0.8017,\n",
      "         0.4384,  0.4461, -0.6304,  0.9455,  0.4524,  0.4237,  0.1764,  0.9980,\n",
      "         0.3758,  0.4212, -0.1768,  0.9990,  0.4492,  0.5389,  0.3476,  0.7580,\n",
      "         0.3831,  0.5448, -0.0966,  0.9881,  0.4377,  0.6520,  0.6332,  0.8233,\n",
      "         0.3541,  0.6678,  0.0939,  0.9874,  0.4194,  0.6705,  0.6515,  0.7737,\n",
      "         0.3382,  0.6886,  0.0992,  0.9336,  0.5213,  0.6807,  0.5175,  0.8895,\n",
      "         0.4286,  0.7045, -0.1218,  0.9826])\n",
      "실제 타겟 데이터: tensor([0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(2)\n",
      "예측 확률: ['0.0615', '0.3723', '0.5094', '0.0568']\n",
      "\n",
      "샘플 3:\n",
      "입력 데이터: tensor([ 0.6286,  0.4080, -0.7369,  0.9993,  0.6381,  0.3980, -0.7065,  0.9995,\n",
      "         0.6384,  0.3974, -0.7066,  0.9994,  0.6386,  0.3968, -0.7070,  0.9996,\n",
      "         0.6318,  0.3965, -0.7613,  0.9996,  0.6280,  0.3944, -0.7615,  0.9995,\n",
      "         0.6243,  0.3918, -0.7614,  0.9996,  0.6138,  0.3904, -0.5152,  0.9990,\n",
      "         0.5929,  0.3803, -0.7632,  0.9990,  0.6118,  0.4116, -0.6591,  0.9998,\n",
      "         0.6034,  0.4097, -0.7316,  0.9998,  0.5621,  0.4149, -0.2478,  0.9998,\n",
      "         0.4617,  0.4024, -0.7099,  0.9993,  0.5373,  0.4767, -0.0623,  0.7927,\n",
      "         0.4040,  0.4686, -0.6700,  0.9857,  0.5030,  0.5440, -0.0597,  0.8699,\n",
      "         0.4008,  0.5501, -0.5627,  0.9828,  0.5019,  0.5583, -0.0668,  0.8109,\n",
      "         0.3992,  0.5665, -0.6293,  0.9698,  0.4996,  0.5581, -0.1464,  0.8230,\n",
      "         0.4159,  0.5689, -0.6358,  0.9689,  0.4958,  0.5529, -0.0911,  0.8184,\n",
      "         0.4200,  0.5622, -0.5647,  0.9495,  0.3748,  0.4259,  0.1399,  0.9974,\n",
      "         0.3031,  0.4183, -0.1391,  0.9970,  0.4491,  0.5246, -0.0217,  0.6830,\n",
      "         0.3595,  0.5318, -0.2506,  0.9700,  0.4248,  0.6392,  0.0943,  0.6649,\n",
      "         0.3564,  0.6625, -0.1628,  0.9734,  0.4061,  0.6585,  0.0997,  0.5930,\n",
      "         0.3278,  0.6809, -0.1648,  0.9222,  0.5018,  0.6728, -0.0507,  0.7533,\n",
      "         0.3974,  0.6752, -0.3801,  0.9683])\n",
      "실제 타겟 데이터: tensor([1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(3)\n",
      "예측 확률: ['0.3180', '0.0026', '0.0282', '0.6511']\n",
      "\n",
      "샘플 4:\n",
      "입력 데이터: tensor([ 0.4327,  0.1751, -0.5830,  1.0000,  0.4515,  0.1614, -0.5491,  1.0000,\n",
      "         0.4595,  0.1620, -0.5491,  1.0000,  0.4692,  0.1627, -0.5492,  1.0000,\n",
      "         0.4215,  0.1602, -0.5453,  1.0000,  0.4109,  0.1599, -0.5456,  1.0000,\n",
      "         0.4026,  0.1599, -0.5458,  1.0000,  0.4845,  0.1713, -0.4124,  1.0000,\n",
      "         0.3920,  0.1676, -0.3990,  1.0000,  0.4501,  0.1921, -0.5132,  1.0000,\n",
      "         0.4131,  0.1921, -0.5110,  1.0000,  0.5641,  0.2668, -0.3222,  1.0000,\n",
      "         0.3145,  0.2648, -0.3119,  1.0000,  0.5684,  0.3767, -0.2610,  0.9943,\n",
      "         0.3087,  0.3726, -0.2598,  0.9958,  0.5297,  0.4743, -0.3130,  0.9843,\n",
      "         0.3296,  0.4661, -0.3326,  0.9760,  0.5288,  0.5028, -0.3754,  0.9694,\n",
      "         0.3296,  0.4953, -0.3671,  0.9499,  0.4974,  0.4977, -0.4250,  0.9701,\n",
      "         0.3539,  0.4936, -0.4053,  0.9502,  0.4908,  0.4886, -0.3289,  0.9647,\n",
      "         0.3581,  0.4837, -0.3397,  0.9499,  0.5046,  0.4496,  0.0012,  0.9999,\n",
      "         0.3725,  0.4448, -0.0021,  0.9999,  0.4801,  0.5970, -0.0785,  0.9258,\n",
      "         0.3624,  0.5894, -0.0108,  0.9421,  0.4559,  0.7487,  0.0310,  0.9416,\n",
      "         0.3452,  0.7431,  0.0831,  0.9629,  0.4387,  0.7711,  0.0264,  0.8206,\n",
      "         0.3652,  0.7647,  0.0769,  0.7768,  0.4764,  0.7949, -0.1934,  0.9613,\n",
      "         0.3057,  0.7919, -0.1411,  0.9702])\n",
      "실제 타겟 데이터: tensor([1., 0., 0., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.9590', '0.0000', '0.0033', '0.0377']\n",
      "\n",
      "샘플 5:\n",
      "입력 데이터: tensor([ 0.5571,  0.2359, -0.3926,  0.9998,  0.5596,  0.2231, -0.3656,  0.9994,\n",
      "         0.5597,  0.2226, -0.3655,  0.9993,  0.5593,  0.2220, -0.3655,  0.9994,\n",
      "         0.5528,  0.2225, -0.4369,  0.9996,  0.5478,  0.2215, -0.4369,  0.9996,\n",
      "         0.5419,  0.2203, -0.4371,  0.9996,  0.5350,  0.2205, -0.2255,  0.9984,\n",
      "         0.5075,  0.2181, -0.5150,  0.9994,  0.5463,  0.2451, -0.3269,  0.9997,\n",
      "         0.5370,  0.2442, -0.4177,  0.9998,  0.5176,  0.2914, -0.0121,  0.9997,\n",
      "         0.4055,  0.2748, -0.5895,  1.0000,  0.5213,  0.3719,  0.1110,  0.6875,\n",
      "         0.3888,  0.3730, -0.6276,  0.9951,  0.5440,  0.4397,  0.0051,  0.7970,\n",
      "         0.4198,  0.4584, -0.6307,  0.9853,  0.5503,  0.4608, -0.0157,  0.7125,\n",
      "         0.4087,  0.4796, -0.6969,  0.9722,  0.5417,  0.4616, -0.0664,  0.7308,\n",
      "         0.4295,  0.4786, -0.7219,  0.9709,  0.5336,  0.4554, -0.0215,  0.7169,\n",
      "         0.4354,  0.4720, -0.6384,  0.9540,  0.4287,  0.4399,  0.1691,  0.9982,\n",
      "         0.3532,  0.4350, -0.1692,  0.9991,  0.4562,  0.5428,  0.3238,  0.7889,\n",
      "         0.3807,  0.5499, -0.1053,  0.9845,  0.4423,  0.6502,  0.5853,  0.8485,\n",
      "         0.3570,  0.6685,  0.0765,  0.9830,  0.4220,  0.6681,  0.6020,  0.8168,\n",
      "         0.3383,  0.6881,  0.0808,  0.9103,  0.5240,  0.6785,  0.4794,  0.9108,\n",
      "         0.4339,  0.7023, -0.1313,  0.9782])\n",
      "실제 타겟 데이터: tensor([0., 0., 1., 0.])\n",
      "예측된 데이터: tensor(0)\n",
      "예측 확률: ['0.4824', '0.0258', '0.1989', '0.2929']\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/n8c37q85651bywf1rz00z90c0000gn/T/ipykernel_70819/2186968047.py:9: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터셋에서 샘플 선택\n",
    "num_samples_to_test = 5  # 확인할 샘플의 수\n",
    "for i in range(num_samples_to_test):\n",
    "    sample_index = i\n",
    "    input_sample = deadlift_X_test[sample_index]  # 입력 데이터\n",
    "    target_sample = deadlift_y_test[sample_index]  # 실제 타겟(정답) 데이터\n",
    "\n",
    "    # 입력 데이터를 Tensor로 변환\n",
    "    input_tensor = torch.tensor(input_sample, dtype=torch.float32)\n",
    "\n",
    "    # 모델에 입력하여 예측 생성\n",
    "    deadlift_model.eval()\n",
    "    with torch.no_grad():\n",
    "        prediction = deadlift_model(input_tensor.unsqueeze(0))  # unsqueeze를 사용하여 배치 차원 추가\n",
    "\n",
    "    # 예측 결과 출력\n",
    "    print(f\"샘플 {sample_index + 1}:\")\n",
    "    print(\"입력 데이터:\", input_sample)\n",
    "    print(\"실제 타겟 데이터:\", target_sample)\n",
    "    print(\"예측된 데이터:\", torch.argmax(prediction))  # 확률이 아닌 예측된 클래스를 출력\n",
    "    probabilities = prediction.squeeze().tolist()  # 텐서를 리스트로 변환\n",
    "    probabilities_str = [f\"{prob:.4f}\" for prob in probabilities]\n",
    "    print(\"예측 확률:\", probabilities_str)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "11299320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도: 0.463\n",
      "정밀도: 0.985\n",
      "재현율: 0.463\n",
      "F1 점수: 0.630\n"
     ]
    }
   ],
   "source": [
    "deadlift_model.eval()\n",
    "with torch.no_grad():\n",
    "    predicted_probs = deadlift_model(deadlift_X_test)\n",
    "\n",
    "threshold = 0.8  # 임계값 설정\n",
    "predicted_labels = (predicted_probs > threshold).numpy().astype(int)\n",
    "\n",
    "# 정확도 계산\n",
    "accuracy = accuracy_score(deadlift_y_test, predicted_labels)\n",
    "print(f\"정확도: {accuracy:.3f}\")\n",
    "\n",
    "# 정밀도 계산\n",
    "precision = precision_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"정밀도: {precision:.3f}\")\n",
    "\n",
    "# 재현율 계산\n",
    "recall = recall_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"재현율: {recall:.3f}\")\n",
    "\n",
    "# F1 점수 계산\n",
    "f1 = f1_score(deadlift_y_test, predicted_labels, average='micro')\n",
    "print(f\"F1 점수: {f1:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "41d1840a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x2400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = multilabel_confusion_matrix(deadlift_y_test, predicted_labels)\n",
    "\n",
    "def plot_multilabel_confusion_matrix(confusion_matrix):\n",
    "    num_classes = len(confusion_matrix)\n",
    "    fig, axes = plt.subplots(nrows=num_classes, ncols=1, figsize=(8, 6 * num_classes))\n",
    "\n",
    "    for i in range(num_classes):\n",
    "        sns.heatmap(confusion_matrix[i], annot=True, cmap=\"Blues\", fmt=\"d\", ax=axes[i])\n",
    "        axes[i].set_title(f\"Class {i+1} Confusion Matrix\")\n",
    "        axes[i].set_xlabel(\"Predicted label\")\n",
    "        axes[i].set_ylabel(\"True label\")\n",
    "        axes[i].set_xticklabels(['True', 'False'])\n",
    "        axes[i].set_yticklabels(['True', 'False'])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 다중 레이블 오차 행렬 시각화\n",
    "plot_multilabel_confusion_matrix(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0eb5693",
   "metadata": {},
   "source": [
    "# Save models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "38c44f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Bench Press\n",
    "with open('benchpress_model_deep_merged.pkl', 'wb') as f:\n",
    "    pickle.dump(bench_model, f)\n",
    "\n",
    "# Squat\n",
    "with open('squat_model_deep_merged.pkl', 'wb') as f:\n",
    "    pickle.dump(squat_model, f)\n",
    "\n",
    "# Deadlift\n",
    "with open('deadlift_model_deep_merged.pkl', 'wb') as f:\n",
    "    pickle.dump(deadlift_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "eca7f824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([239, 132])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ffeba6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([232, 132])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squat_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "923f2a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([285, 132])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deadlift_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc644d48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyTorch",
   "language": "python",
   "name": "pytorch_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}