linear_cpu_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7cb64a13-1494-49ef-9daf-a463795e256f",
   "metadata": {},
   "source": [
    "* 1.데이터 셋에서 주어진 특징들을 선택하여 CPU 성능 즉, \"PRP\" 변수를 예측하는 LinearRegression 기반 다중 선형 회귀 모델링을 프로그래밍 하시오.\n",
    "* - 데이터 탐색 및 시각화, 전처리, 훈련 데이터와 테스트 데이터 분리, 훈련데이터 학습과정, 교차 검증, 분석 결과 시각화 등을 포함하여 최적화 하는 전체 과정을 포함하시오"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef154c94-242d-4750-b011-64d40229b527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Vendor name Model name  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  ERP\n",
      "0  VendorName  ModelName  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  NaN\n",
      "1     adviser      32/60   125   256   6000   256     16    128  198  NaN\n",
      "2      amdahl     470v/7    29  8000  32000    32      8     32  269  NaN\n",
      "3      amdahl    470v/7a    29  8000  32000    32      8     32  220  NaN\n",
      "4      amdahl    470v/7b    29  8000  32000    32      8     32  172  NaN\n",
      "       ERP\n",
      "count  0.0\n",
      "mean   NaN\n",
      "std    NaN\n",
      "min    NaN\n",
      "25%    NaN\n",
      "50%    NaN\n",
      "75%    NaN\n",
      "max    NaN\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 210 entries, 0 to 209\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Vendor name  210 non-null    object \n",
      " 1   Model name   210 non-null    object \n",
      " 2   MYCT         210 non-null    object \n",
      " 3   MMIN         210 non-null    object \n",
      " 4   MMAX         210 non-null    object \n",
      " 5   CACH         210 non-null    object \n",
      " 6   CHMIN        210 non-null    object \n",
      " 7   CHMAX        210 non-null    object \n",
      " 8   PRP          210 non-null    object \n",
      " 9   ERP          0 non-null      float64\n",
      "dtypes: float64(1), object(9)\n",
      "memory usage: 16.5+ KB\n",
      "None\n",
      "MYCT     0\n",
      "MMIN     0\n",
      "MMAX     0\n",
      "CACH     0\n",
      "CHMIN    0\n",
      "CHMAX    0\n",
      "dtype: int64\n",
      "0\n",
      "MYCT unique values: ['MYCT' '125' '29' '26' '23' '400' '60' '50' '350' '200' '167' '143' '110'\n",
      " '320' '25' '56' '64' '133' '810' '700' '140' '220' '800' '75' '90' '105'\n",
      " '175' '300' '180' '330' '57' '480' '203' '115' '1100' '600' '900' '225'\n",
      " '185' '17' '1500' '100' '150' '92' '72' '40' '35' '38' '48' '30' '112'\n",
      " '84' '250' '160' '240' '52' '70' '59' '116' '124' '98']\n",
      "MMIN unique values: ['MMIN' '256' '8000' '16000' '32000' '1000' '512' '2000' '4000' '64' '524'\n",
      " '5000' '1500' '3100' '2300' '128' '1310' '2620' '5240' '500' '384' '3000'\n",
      " '768' '192' '96' '262']\n",
      "MMAX unique values: ['MMAX' '6000' '32000' '16000' '64000' '3000' '3500' '8000' '64' '2000'\n",
      " '5000' '6300' '6200' '2620' '10480' '20970' '4000' '12000' '512' '1000'\n",
      " '4500' '768' '24000' '1500']\n",
      "CACH unique values: ['CACH' '256' '32' '64' '128' '0' '4' '65' '8' '142' '131' '30' '9' '16'\n",
      " '6' '1' '2' '24' '48' '12' '160' '112' '96']\n",
      "CHMIN unique values: ['CHMIN' '16' '8' '32' '1' '4' '7' '5' '6' '12' '3' '2' '0' '24' '26' '52']\n",
      "CHMAX unique values: ['CHMAX' '128' '32' '16' '64' '2' '6' '8' '4' '15' '20' '12' '3' '5' '24'\n",
      " '1' '38' '10' '19' '48' '31' '112' '54' '0' '7' '14' '13' '28' '26' '52'\n",
      " '104' '176']\n",
      "MYCT     float64\n",
      "MMIN     float64\n",
      "MMAX     float64\n",
      "CACH     float64\n",
      "CHMIN    float64\n",
      "CHMAX    float64\n",
      "dtype: object\n",
      "교차 검증 R^2 점수: [ 0.63229752  0.41201351 -0.4480466   0.8422106   0.76487551 -0.04917505\n",
      "  0.88779833  0.90986424  0.85308127  0.85357074]\n",
      "평균 교차 검증 R^2 점수: 0.5658490064954826\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 54984 (\\N{HANGUL SYLLABLE HUN}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 47144 (\\N{HANGUL SYLLABLE RYEON}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\1070020085.py:89: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 54984 (\\N{HANGUL SYLLABLE HUN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 47144 (\\N{HANGUL SYLLABLE RYEON}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAHqCAYAAADyGZa5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAACWuUlEQVR4nOzdZ3gU5fv28XM3m14hoXekg1IsSEkChBJAuhQBAUERBBUsKGIXFbGgKCooohQpKqL0LgkWQPBHVwEVEZBOCi1l7+cFT+ZvBFIkYVK+n+PIoTvszpw7O7vXXvfMzjiMMUYAAAAAACBXOO0OAAAAAABAQUbjDQAAAABALqLxBgAAAAAgF9F4AwAAAACQi2i8AQAAAADIRTTeAAAAAADkIhpvAAAAAAByEY03AAAAAAC5iMYbAAAAAIBcROMNAAAAAEAuovEGAAAAACAX0XgDBcDOnTvl5eWlgICAy/55eXlp3759Wb7flZQsWfKKj/Xx8dFHH32UK/cDAKAgyOl6TV0H8g+X3QEAXD1jjG655RatX7/+sv9+6623yhiT5ftdSUpKik6fPi2X69KPjscff1xutztX7gcAQEGQ0/Waug7kH+zxBgAAAAAgF9F4AwAAAACQi2i8AQAAAADIRTTeAAAAAADkIhpvAAAAAAByEY03AAAAAAC5iMYbAAAAAIBcROMNAAAAAEAuovEGAAAAACAX0XgDAAAAAJCLaLwBAAAAAMhFNN4AAAAAAOQil90BAOSMH374QSEhIZf9t8TExGzf70rCwsIuO/38+fN65513cu1+AAAUBDldr6nrQP7gMMYYu0MAAAAAAFBQcag5AAAAAAC5iMYbAAAAAIBcROMNAAAAAEAuKvAnV3O73Tp06JACAwPlcDjsjgMAQDrGGCUkJKh06dJyOhkP/ydqOAAgL8tODS/wjfehQ4dUrlw5u2MAAJChAwcOqGzZsnbHyFOo4QCA/CArNbzAN96BgYGSLq6MoKAgm9MAAJBefHy8ypUrZ9Ur/B9qOAAgL8tODS/wjXfaoWlBQUEUbQBAnsWh1JeihgMA8oOs1HB+TAYAAAAAQC6i8QYAAAAAIBfReAMAAAAAkItovAEAAAAAyEU03gAAAAAA5CIabwAAAAAAchGNNwAAAAAAuYjGGwAAAACAXETjDQAAAABALqLxBgAAAAAgF9F4AwAAAACQi2xtvBMSEjRixAhVqFBBvr6+aty4sTZt2mT9+7PPPqsaNWrI399fRYoUUcuWLbVhwwYbEwMAUPBlVp/nz5+v1q1bKzQ0VA6HQ//73/8umUezZs3kcDjS/Q0ZMuQaPgsAAPIOWxvvu+++WytXrtSMGTO0fft2tW7dWi1bttTBgwclSdWqVdM777yj7du3a/369apYsaJat26tY8eO2RkbAIACLbP6fObMGTVt2lSvvPJKhvO55557dPjwYetv/Pjx1yI+AAB5jsMYY+xY8Llz5xQYGKivvvpK7du3t6bfeOONatu2rcaOHXvJY+Lj4xUcHKxVq1YpKioqS8tJe0xcXJyCgoJyLD8AADkhr9Wp7NTnP/74Q5UqVdJPP/2kevXqpZtPs2bNVK9ePb355pv/OUteWzcAAPxTduqUbXu8U1JSlJqaKh8fn3TTfX19tX79+kvun5SUpClTpig4OFh169a9VjEvYYzR0aNHbVs+AAC5Kbv1OSOzZs1SWFiY6tSpo9GjR+vs2bM5GTXb3G43R80BAGxhW+MdGBioRo0a6YUXXtChQ4eUmpqqmTNn6vvvv9fhw4et+y1atEgBAQHy8fHRhAkTtHLlSoWFhV1xvhcuXFB8fHy6v5z0xBNPqG7dutq5c2eOzhcAgLwgq/U5M71799bMmTO1du1ajR49WjNmzFDfvn0zfExu1nBjjIYNG6ZbbrlF+/bty7H5AgCQFbb+xnvGjBkyxqhMmTLy9vbWxIkTdccdd8jp/L9YzZs31//+9z999913io6OVo8ePTLc4/zyyy8rODjY+itXrlyO5Z0yZYrGjRunv//+W5GRkdq8eXOOzRsAgLwiK/U5M4MHD1abNm10/fXXq0+fPpo+fbq+/PLLDJve3Kzhr776qt5//3398ccfCg8P165du3Js3gAAZMbWxvu6667TunXrlJiYqAMHDmjjxo1KTk5W5cqVrfv4+/urSpUquvXWWzV16lS5XC5NnTr1ivMcPXq04uLirL8DBw7kWN5u3brppptukiSdOHFC7du3t/2wOQAAclpW6nN2NWzYUJK0d+/eK94nN2t43759VatWLUnS4cOH1aFDByUnJ+fY/AEAyEieuI63v7+/SpUqpVOnTmn58uXq1KnTFe/rdrt14cKFK/67t7e3goKC0v3llNDQUK1evVrh4eHy9PTUxx9/LD8/vxybPwAAeUl26nNm0i45VqpUqSveJzdreOnSpbVu3To1aNBAvr6++vjjj+Xp6Zlj8wcAICMuOxe+fPlyGWNUvXp17d27V48++qhq1Kihu+66S2fOnNGLL76ojh07qlSpUjp+/LgmTZqkgwcPqnv37rZlDgoK0rJly7Rp0yZFRkbalgMAgNySUX2WpJMnT+rPP//UoUOHJEm//PKLJKlkyZIqWbKk9u3bp08//VTt2rVTaGiotm3bppEjRyoiIkI33HCDbc8rLCxMa9as0c6dO9W4cWPbcgAACh9b93jHxcVp2LBhqlGjhvr166emTZtq+fLl8vT0lIeHh37++Wd169ZN1apVU4cOHXTixAnFxsaqdu3adsaWn5/fZZvurVu32pAGAICclVF9lqSvv/5a9evXty431qtXL9WvX1/vv/++JMnLy0urVq1S69atVaNGDT388MPq1q2bFi5caNtzShMcHHxJ022MoYYDAHKVbdfxvlau1TVAx40bpyeeeELvv/++Bg8enGvLAQAULFyr+squxboxxmj06NF67bXXNGPGDN1xxx25shwAQMGTL67jXZB88803Gj16tIwxuvfee/X666/bHQkAAGTBV199pVdeeUWpqanq06ePPvzwQ7sjAQAKIBrvHBAZGalHHnnEuv3II4/oueeeUwE/mAAAgHyvQ4cO1pFqxhjdc889evPNN+0NBQAocGi8c4DD4dD48eP1/PPPW9OeffZZPfroozTfAADkYR4eHnr//ff18MMPW9NGjhypF154gRoOAMgxNN45xOFw6KmnntKECROsaa+//rqGDBmi1NRUG5MBAICMOBwOvfrqq3r22WetaU8//bQee+wxmm8AQI6g8c5hI0aM0AcffCCHwyFJmjJlivr166fk5GSbkwEAgCtxOBx65pln0p2n5dVXX9V9990nt9ttYzIAQEFA450L7r77bs2aNUseHh6SpE8//VQPPPCAzakAAEBmHnroIU2ePNkaQH///fc1ZswYm1MBAPI7Gu9ccscdd2j+/Pny8vJS8eLFNXLkSLsjAQCALBg8eLBmzJghDw8PlStXTkOHDrU7EgAgn3PZHaAg69ixo5YsWaKwsDBVq1bN7jgAACCL+vTpo6CgINWoUUPly5e3Ow4AIJ+j8c5lUVFRl0xLTk5WQkKCihYtakMiAACQFR06dLhk2rlz55ScnKygoCAbEgEA8isONb/GUlNTdeeddyo8PFyHDx+2Ow4AAMiipKQk3X777YqKitKJEyfsjgMAyEdovK+xJ554QnPnztWuXbsUHh6uP/74w+5IAAAgC+6//34tWbJEP/74oyIjIxlABwBkGY33NXbvvfeqYsWKkqR9+/YpPDxcv/zyi72hAABAph544AGVLFlSkrRz505FRERo//79NqcCAOQHNN7XWOXKlRUbG6vq1atLkv766y9FRERo27ZtNicDAAAZqV27tmJjY1WhQgVJ0t69exUeHq49e/bYnAwAkNfReNugbNmyiomJUd26dSVJR48eVbNmzbRx40abkwEAgIxUqVJFsbGx1tVKDhw4oPDwcG3fvt3mZACAvIzG2ybFixfX2rVrdeutt0qSTp06paioKK1bt87mZAAAICPlypVTTEyMbrjhBknSkSNH1KxZM23atMnmZACAvIrG20ZFihTRypUr1aJFC0lSYmKioqOjFRsba3MyAACQkRIlSmjt2rVq2LChJOnkyZOKiorSli1bbE4GAMiLaLxtFhAQoMWLF6t9+/aSpJo1a+r666+3ORUAAMhM0aJFtXLlSjVr1kySVLduXescLgAA/BONdx7g4+Oj+fPna9SoUVq+fLlCQkLsjgQAALIgMDBQS5Ys0cMPP6xFixbJ39/f7kgAgDzIYYwxdofITfHx8QoODlZcXJyCgoLsjpNtFy5ckLe3t90xAAC5JL/XqdyU39cNNRwACrbs1Cn2eOdhcXFxatKkicaNG2d3FAAAkA3Hjh3TjTfeqPfee8/uKACAPMBldwBcXlJSktq3b6/Nmzdr8+bNSkhI0NixY+VwOOyOBgAAMnDmzBm1adNGO3fu1H333afExEQ9+uijdscCANiIPd55lJeXl3XCNUl66aWXNGLECLndbhtTAQCAzPj5+al169bW7VGjRumpp55SAf91HwAgAzTeedjo0aP19ttvW7cnTpyou+++W6mpqTamAgAAGXE4HBo3bpxefPFFa9rYsWP10EMP0XwDQCFF453HDR8+XNOmTZPTefGlmjZtmnr37q2kpCSbkwEAgIw88cQTeuutt6zbb775pgYPHswAOgAUQjTe+cCAAQM0d+5ceXp6SpLmzZunrl276ty5czYnAwAAGXnggQc0depUawD9ww8/VN++fZWcnGxzMgDAtUTjnU/cfvvtWrBggXx8fCRJixcvVvv27XXmzBmbkwEAgIwMHDhQs2fPlst18Zy2c+bMUbdu3Th6DQAKERrvfKRdu3ZaunSpAgICJElhYWFWIw4AAPKuHj16aMGCBdZ1vYsXL24dyQYAKPhovPOZZs2aadWqVerTp49mzpwpDw8PuyMBAIAsaN++vZYuXapBgwZp8uTJXCIUAAoRhyngp9eMj49XcHCw4uLiFBQUZHecXGWMoYgDQD5TmOpUdhWmdUMNB4D8Jzt1ij3eBcSePXvUuHFj7du3z+4oAAAgG7Zt26amTZvq4MGDdkcBAOQSGu8C4M8//1RUVJR++OEHhYeHa9euXXZHAgAAWfDLL7+oVatW+u677xQeHq7ff//d7kgAgFxA410AeHp6KjAwUJJ0+PBhRUZGasuWLTanAgAAmfHy8rJOmvr777+radOm2r17t82pAAA5jca7AChVqpTWrVunBg0aSJKOHz+u5s2b69tvv7U5GQAAyEilSpUUExOjmjVrSpIOHTqkiIgI/fTTTzYnAwDkJBrvAiIsLExr1qxRkyZNJF38oX/r1q21atUqm5MBAICMlClTRuvWrVP9+vUl/d8A+vfff29zMgBATqHxLkCCg4O1fPlytWrVSpJ09uxZtW/fXl9//bXNyQAAQEaKFSumNWvWqHHjxpKkuLg4tWrVSmvWrLE5GQAgJ9B4FzD+/v5auHChOnfuLElKSkpS165dNXv2bHuDAQCADIWEhGjFihWKioqSJJ05c0bt2rXTokWLbE4GALhaNN4FkLe3t+bNm6c+ffpIklJTU/Xbb7/ZnAoAAGTG399fixYtUseOHSVJFy5c0P79+21OBQC4Wi67AyB3eHp6avr06QoICFBISIieeOIJuyMBAIAs8PHx0eeff67+/fvrhhtu0LBhw+yOBAC4SjTeBZjT6dR7770nSXI4HDanAQAAWeXp6amZM2fK6eTgRAAoCPg0L+AcDsclTffKlSs1evRoGWNsSgUAADJzuaZ7/vz5evHFF6nhAJDPsMe7kFm/fr06deqkc+fO6fTp05o0aRKj6QAA5APLli1Tr169lJycrPj4eI0bN44j2gAgn6DjKmT27Nmj8+fPS5Lef/999e/fXykpKTanAgAAmfn555+VnJwsSRo/fryGDx8ut9ttcyoAQFbQeBcyd911l2bOnCkPDw9J0syZM9WjRw9duHDB5mQAACAjI0aM0HvvvWft5X733Xd11113MYAOAPkAjXch1Lt3b33++efy8vKSJH355Zfq2LGjzp49a3MyAACQkSFDhmj69OnWAPr06dPVs2dPBtABII+j8S6kOnfurEWLFsnPz0+StGLFCkVHRys+Pt7mZAAAICN9+/bVZ599Jk9PT0kXT7jWuXNnBtABIA+j8S7EWrVqpeXLlysoKEiSFBsbq6ioKJ04ccLmZAAAICNdunTRwoUL5evrK+niidfatm3LADoA5FE03oVc06ZNtWbNGoWGhkqSfvrpJ23ZssXmVAAAIDNt2rTR8uXLFRgYKEnasGGDdu7caXMqAMDl0HhDN954o9atW6cyZcpoxowZatWqld2RAABAFoSHh2vNmjUqWbKkPv/8czVq1MjuSACAy+A63pAk1a5dW7/88ov8/f3tjgIAALLhpptu0t69e6nhAJCHsccblssV7MmTJ2v79u02pAEAAFl1uRr+5ptvas+ePTakAQD8G403rujDDz/UkCFDFBkZqY0bN9odBwAAZNHrr7+ukSNHKiIiQjt27LA7DgAUejTeuKzU1FRNmzZNknTq1ClFRUUpJibG5lQAACAzFy5c0PTp0yVJf//9tyIjI/Xjjz/anAoACjcab1yWh4eHli1bpmbNmkmSEhMTFR0drWXLltkbDAAAZMjb21tr167VLbfcIkk6efKkWrRoodjYWJuTAUDhReONKwoMDNSSJUvUrl07SdK5c+fUsWNHffHFFzYnAwAAGSlatKhWrVqliIgISVJCQoLatGmjFStW2JwMAAonGm9kyNfXV19++aVuv/12SVJycrJ69OihGTNm2JwMAABkJDAwUEuXLlV0dLSkiwPoHTp00IIFC+wNBgCFEI03MuXl5aXZs2drwIABkiS3261+/frpvffeszcYAADIkJ+fnxYsWKBu3bpJkpKSknT77bdr1qxZNicDgMKFxhtZ4nK5NHXqVA0fPtya9tRTT+nEiRM2pgIAAJnx9vbWnDlz1K9fP0kXT6D62GOP6cyZMzYnA4DCw2V3AOQfTqdTEydOVGBgoN555x0tW7ZMoaGhdscCAACZcLlcmjZtmgICAjRv3jytWLHistf+BgDkDocxxtgdIjfFx8crODhYcXFxCgoKsjtOgXHgwAGVK1fO7hgAkO9Rp66MdZPzjDE6ePCgypYta3cUAMj3slOnONQc/8m/m263262PP/5YqampNiUCAACZcTgclzTdKSkp+uSTT1TA98UAgK1ovHHVjDF64IEHdNddd6lv375KTk62OxIAAMgCt9utgQMHasCAAbr33nsZQAeAXELjjau2fft2TZ48WZI0Z84cdevWTefPn7c5FQAAyMz333+vmTNnSpI++OAD9evXjwF0AMgFNN64ajfccIMWLFggb29vSdLChQt12223KTEx0eZkAAAgI02aNNHs2bPlcl083+6nn36q7t27M4AOADmMxhs5on379lq6dKl1htTVq1erdevWOn36tL3BAABAhnr27Kn58+dbA+hfffWVOnTowOXGACAH0XgjxzRv3lyrVq1SSEiIpIuHrzVv3lzHjh2zNxgAAMhQhw4dtHjxYvn5+UmSVq1apTZt2iguLs7mZABQMNB4I0fdeuutWrt2rYoVKyZJ+t///qfIyEgdPHjQ5mQAACAjUVFRWrlypYKDgyVJ3377rVq0aKHjx4/bnAwA8j8ab+S4evXqKSYmRmXKlJEk7d69W0OHDrU5FQAAyEzjxo21du1ahYWFSZK2bNmihx56yOZUAJD/0XgjV9SoUUOxsbGqVKmSqlSpYp31HAAA5G3169fXunXrVLp0aV1//fV688037Y4EAPmey+4AKLgqVaqk2NhYpaSkqFSpUnbHAQAAWVSrVi3FxsbK399fRYsWtTsOAOR77PFGripTpowqVKiQblpiYqK2bNliUyIAAJAVlStXVokSJdJNO3nypHbs2GFTIgDIv2i8cU2dO3dOHTt2VEREhNasWWN3HAAAkEUJCQlq27atwsPD9cMPP9gdBwDyFRpvXFPjx4/X2rVrdebMGbVr106LFi2yOxIAAMiCp59+Whs3btTp06fVsmVLrV271u5IAJBv0HjjmnrsscfUsWNHSdKFCxfUpUsXzZ071+ZUAAAgMy+88IJatGghSdYA+uLFi21OBQD5A403rikfHx99/vnnuuOOOyRJKSkpuuOOOzR16lSbkwEAgIwEBARo8eLFuu222yRJ58+fV+fOnfXZZ5/ZnAwA8j4ab1xznp6emjFjhu6++25JkjFGd999t9566y2bkwEAgIz4+Pho/vz56tmzp6SLA+i9evXStGnTbE4GAHkbjTds4eHhoSlTpmjkyJHWtBEjRujFF1+UMcbGZAAAICOenp6aNWuWBg4cKElyu90aOHCg3n77bZuTAUDeReMN2zgcDr3++ut6+umnrWlPPvmkZs6caWMqAACQGQ8PD33wwQd68MEHrWkPPPCAFi5caGMqAMi7aLxhK4fDoeeee06vvvqqJKlly5bq3r27zakAAEBmnE6nJkyYoCeffFKS1KVLF7Vt29bmVACQN7nsDgBI0iOPPKJKlSopOjpaPj4+dscBAABZ4HA49MILL6hOnTrq3LmzXC6+WgLA5bDHG3lGt27d5O/vn27awYMHlZSUZFMiAACQFT179pS3t3e6aX/++adSUlJsSgQAeQuNN/KsgwcPKjw8XJ06ddLZs2ftjgMAALLot99+06233qrevXszgA4AovFGHmWMUefOnfX7779r2bJlatu2reLj4+2OBQAAMpGSkqJ27drp8OHD+uyzz9SlSxedO3fO7lgAYCsab+RJDodDb7zxhgIDAyVJMTExatmypU6ePGlzMgAAkBGXy6U333zTOmfLkiVL1K5dOyUkJNicDADsQ+ONPCs8PFxr1qxR0aJFJUmbNm1SZGSk/v77b5uTAQCAjERHR2v58uXWAPo333zDADqAQo3GG3naTTfdpHXr1qlkyZKSpB07digiIkJ//vmnzckAAEBGIiIitHr1ahUpUkSStHHjRjVv3lxHjhyxORkAXHs03sjz6tSpo5iYGJUvX16StGfPHoWHh2vPnj02JwMAABm5+eabtW7dOpUoUUKStG3bNkVEROjAgQM2JwOAa4vGG/lC1apVFRsbq6pVq0q6eImS8PBwDjsHACCPu/766xUTE6Ny5cpJkn799VeFh4crLi7O5mQAcO3QeCPfKF++vGJiYlSnTh1JUq9evawRdAAAkHdVq1ZNsbGxqlKliiRpwIABCg4OtjkVAFw7LrsDANlRsmRJrVu3TlOmTNFjjz0mh8NhdyQAAJAFFSpUUExMjGbPnq2RI0faHQcArimHMcbYHSI3xcfHKzg4WHFxcQoKCrI7DnJRQkKCdfZUAMgvqFNXxropPKjhAPKj7NQpDjVHgbBhwwZVqlRJCxYssDsKAADIhjVr1qhixYpatWqV3VEAINfQeCPf27dvn9q2basTJ07o9ttv16xZs+yOBAAAsmD79u3q2LGjTp48qfbt2+urr76yOxIA5Aoab+R7FSpUUIcOHSRJqampuvPOOzV58mSbUwEAgMxUq1ZNrVu3liQlJSWpW7dumj17ts2pACDn0Xgj33O5XJo2bZruu+8+SZIxRkOGDNFrr71mczIAAJARb29vzZs3T3feeaekiwPoffr00QcffGBzMgDIWTTeKBCcTqfeeecdPfbYY9a0Rx99VM8884wK+PkDAQDI11wulz7++GMNGTJE0sUB9MGDB+uNN96wORkA5BwabxQYDodDL7/8ssaOHWtNe/755/Xwww/TfAMAkIc5nU69++67euSRR6xpDz/8sJ577jlqOIACgcYbBYrD4dCYMWP05ptvWtMmTJhgHYYOAADyJofDofHjx+v555+3pj377LN64oknbEwFADmDxhsF0oMPPqipU6fK4XBIkho2bGhzIgAAkBmHw6GnnnpKEyZMkHRxT/hNN91kcyoAuHouuwMAuWXgwIHy9/fX8ePHNWDAALvjAACALBoxYoQCAgLk5eWlbt262R0HAK4ajTcKtJ49e152utvtltPJAR8AAORVd99992WnU8MB5Ed8aqHQmTlzpiIjI3X69Gm7owAAgGyYNGmS2rZtqzNnztgdBQCyhcYbhcqCBQs0YMAArV+/Xi1atNDx48ftjgQAALJgxowZGj58uFasWKHo6GjFxcXZHQkAsozGG4VKhQoVVKRIEUnSTz/9pMjISB06dMjmVAAAIDOVK1dWUFCQJGn9+vWKiorSiRMnbE4FAFlD441CpX79+oqJiVHp0qUlSbt27VJ4eLj++OMPe4MBAIAMNWnSRGvXrlVoaKgkafPmzYqMjNThw4dtTgYAmaPxRqFTs2ZNxcbGqlKlSpKk3377TU2bNtXPP/9sczIAAJCRBg0aKCYmRqVKlZIk7dy5U+Hh4dq/f7/NyQAgYzTeKJQqV66s2NhY1ahRQ5J08OBBRUREaOvWrTYnAwAAGalVq5ZiY2NVoUIFSdK+ffvUtGlT/frrrzYnA4Aro/FGoVWmTBmtW7dO9erVkyQdO3ZMzZo10w8//GBvMAAAkKHrrrtO69evV/Xq1SVJf/31l8LDw7Vt2zabkwHA5dF4o1ArXry41q5dq0aNGkmSkpOT5Xa7bU4FAAAyU7ZsWcXExKhu3bqSpKSkJBljbE4FAJdH441CLyQkRCtWrFC7du301VdfqXHjxnZHAgAAWZA2gN6yZUstXbrUasIBIK9x2R0AyAsCAgK0aNEiORwOu6MAAIBsKFKkiFasWEENB5Cnsccb+P/+XbCNMXrsscc0bdo0mxIBAICsuFwNv++++/T555/blAgA0qPxBq7ghRde0Pjx4zVw4EC9/fbbdscBAABZYIzRQw89pPfee089e/bUJ598YnckAKDxBi7HGKNTp05Ztx944AG99NJLNiYCAABZYYxRXFycJMntdmvAgAGaNGmSzakAFHY03sBlOBwOvfHGG3ryySetaWPGjNHo0aM5YyoAAHmY0+nUhx9+qPvvv9+aNnz4cL3yyis2pgJQ2NF4A1fgcDj0wgsvpCvU48aN0wMPPMAlxwAAyMOcTqfeeustPfHEE9a0xx9/XGPGjGEAHYAtaLyBTIwaNSrdIWrvvPOOBg0apJSUFBtTAQCAjDgcDr344ot6+eWXrWkvvfSSRowYwQA6gGuOxhvIgvvuu0+ffPKJnM6Lb5mPP/5Yd9xxh5KSkmxOBgAAMvL444+nO0nqxIkTdffddys1NdXGVAAKGxpvIIv69eunefPmydPTU5K0adMmnThxwuZUAAAgM8OHD9e0adOsAfTvv//eOgEbAFwLNN5ANnTr1k1ff/21rrvuOq1evVqlSpWyOxIAAMiCAQMGaO7cuapevbpWrVqlokWL2h0JQCHiMAX8DBPx8fEKDg5WXFycgoKC7I6DAiI5Odna8w0AV4M6dWWsG+QGajiAnJKdOsUeb+A/+HfBvnDhgh588EEdOXLEpkQAACAr/l3Dz5w5o/vvv1+nTp2yKRGAwoDGG7hKKSkpuuOOOzRx4kRFRETowIEDdke6Irfb6Oe/47XhtxP6+e94ud0F+oAXAAAydP78eXXp0kXvvPOOmjdvrqNHj9odCUAB5bI7AJDf/f333/rxxx8lSb/++qvCw8O1atUqValSxeZk6W3ef1KffLdfe48mKiklVV4uD1UpHqD+jSvoxgr8zg0AUPgcOHBAW7dulSRt3bpVERERWrVqlcqWLWtzMgAFDXu8gatUtmxZxcbGWo32/v37FR4erp07d9qc7P9s3n9SLy7erR0H4xTk41LZIn4K8nFp56E4vbh4tzbvP2l3RAAArrmqVasqJibGarR/+eUXhYeHa9++fTYnA1DQ0HgDOaBChQqKiYlR7dq1JV3cCx4ZGanNmzfbnOzi4eWffLdfp88mq2Kon/y9XfJwOuTv7VKFon6KO5es6d/t57BzAEChVL16dcXGxuq6666TJP3xxx8KDw/Xrl27bE4GoCCh8QZySKlSpbRu3TrddNNNkqQTJ06oRYsWWr9+va25fj2aoL1HE1U80FsOhyPdvzkcDhUL8Naeo4n69WiCTQkBALBXxYoVFRMTo1q1akmSDh8+rMjISG3ZssXmZAAKChpvIAeFhoZq9erVCg8Pl3TxEgOtW7fWypUrbcsUdzZZSSmp8vH0uOy/+3h6KCklVXFnk69xMgAA8o7SpUtr3bp1atCggSTp+PHjat68ub799lubkwEoCGi8gRwWFBSkZcuWqXXr1pKkc+fO6dVXX5Ux9hzKHeznKS+Xh84np172388nXzzRWrAf1zQFABRuYWFhWrNmjZo0aSLp4gD6W2+9ZXMqAAUBZzUHcoGfn5++/vpr3XHHHfrrr7/0+eefX3KY97VSrXigqhQP0M5DcfLz8kiXwxijY4kXVKd0sKoVD7QlHwAAeUlwcLCWL1+uLl26KCUlRZ988ondkQAUADTeQC7x9vbWvHnzdObMGQUFBdmWw+l0qH/jCnpx8W7tP3lWxQK85eN5cQ/4scQLCvb1VL/GFeR02jMwAABAXuPv76+FCxcqOTlZvr6+dscBUABwqDmQi1wul4KDg9NNO3LkiD766KNrmuPGCkU1pn1N1S4drPjzKfrr1FnFn09RndLBGtO+JtfxBgDgX7y9vRUQEJBu2p9//qlZs2bZlAhAfsYeb+AaOnnypFq3bq1t27bpwIEDevrpp6/ZIeg3Viiq+uWK6NejCYo7m6xgP09VKx7Inm4AALLg8OHDioqK0t69e3Xs2DGNGDHC7kgA8hEab+AaWrBggbZt2yZJevbZZ5WQkKBXX331mjXfTqdDNUrad9g7AAD51ezZs7V3715J0siRI5WYmKgxY8bYdg4XAPkLh5oD19DAgQM1YcIE6/brr7+uoUOHyu1225gKAABkZuTIkXr22Wet20899ZQef/xx265aAiB/ofEGrrERI0bogw8+sEbIJ0+erH79+iklJcXmZAAA4EocDoeeeeYZvf7669a08ePHa9iwYQygA8gUjTdgg7vvvluzZs2Sh4eHJGnWrFnq3r27Lly4YHMyAACQkYceekiTJ0+2BtDfe+89DRgwgAF0ABmi8QZscscdd2j+/Pny8vKSdPH33x07dtTZs2dtTgYAADIyePBgzZgxwxpAnzFjhnr27MkAOoArovEGbNSxY0ctXrxYfn5+kqQVK1Zo8uTJNqcCAACZ6dOnjz7//HNrAH3+/Pn69NNPbU4FIK+i8QZs1rJlS61cuVLBwcHq27evHnzwQbsjAQCALOjcubMWLVokPz8/DRs2TAMGDLA7EoA8isuJAXlA48aNtXHjRlWuXFlOJ+NhAADkF61atdLmzZtVrVo1Li0G4Ir4hg/kEdWqVZPLlX4sbOvWrdq/f79NiQAAQFbUqFHjkoHzjRs36u+//7YpEYC8hsYbyKN2796tli1bqmnTpvr111/tjgMAALJo8+bNatWqlcLDwxlAByCJxhvIs4YPH67jx4/rr7/+Unh4uLZt22Z3JAAAkAljjO69917Fx8dr7969Cg8P1549e+yOBcBmNN5AHjV79mzVrVtXknT06FE1a9ZMGzdutDkVAADIiMPh0Jdffqlq1apJkg4cOKDw8HBt377d5mQA7ETjDeRRxYsX19q1a9WwYUNJ0qlTpxQVFaV169bZnAwAAGSkXLlyiomJ0fXXXy9JOnLkiJo1a6ZNmzbZnAyAXWi8gTysSJEiWrlypZo1ayZJSkxMVHR0tJYuXWpvMAAAkKESJUrom2++0S233CJJOnnypKKiohQTE2NzMgB2oPEG8rjAwEAtWbJE7dq1kySdP39enTp10hdffGFzMgAAkJGiRYtq1apVioyMlCQlJCQoOjpay5cvtzkZgGuNxhvIB3x9ffXll1+qe/fukqTk5GT17NmTk7UAAJDHpQ2gt23bVpJ07tw5de7cWYcOHbI5GYBricYbyCe8vLw0e/Zs3XXXXZKkcePGqWrVqjanAgAAmfHz89OCBQvUrVs3SdLEiRNVunRpm1MBuJZcdgcAkHUeHh768MMPdfvtt1uHngMAgLzPy8tLc+bM0erVq9WmTRu74wC4xtjjDeQzTqfzsk339u3bZYyxIREAAMgKl8t12aabS40BBR+NN1AALF68WA0aNNCIESPkdrvtjgMAALJozpw5qlu3rp566ikG0IECjMYbyOcOHz6s7t27KyUlRRMnTtTdd9+t1NRUu2MBAIBM7N27V3feeaeMMRo7dqxGjhxJ8w0UUDTeQD5XqlQpvfvuu3I6L76dp02bpt69eyspKcnmZAAAICNVqlTR66+/bt1+6623dM899zCADhRANN5AATBgwADNnTtXnp6ekqR58+apa9euOnfunM3JAABARh544AFNnTrVGkCfOnWq+vbtq+TkZJuTAchJ2Wq8z5w5o6FDh6pMmTIqVqyYevXqpWPHjuVWNgDZcPvtt2vBggXy8fGRdPF33+3bt1diYqLNyQDkNuozkL8NHDhQs2fPlst18YJDc+bMUbdu3XT+/HmbkwHIKdlqvJ966inNmDFDt912m/r06aM1a9Zo8ODBuZUNQDa1a9dOS5cuVUBAgCRp7dq1atWqlU6dOmVzMgC5ifoM5H89evTQggUL5O3tLUlauHChbrvtNgbQgQLCYbJxBodKlSpp/Pjx6t69uyRp8+bNuvXWW3Xu3DlrhC6viY+PV3BwsOLi4hQUFGR3HOCa2LBhg6Kjo3X69GlJUqNGjfTtt9/K4XDYGwzAJXKiTuXH+pwV1HAURmvWrFHHjh115swZSVJ0dLSWLl1qcyoAl5OdOpWtPd5//fWXmjRpYt2+8cYb5enpqUOHDv23pAByRcOGDbVu3ToVL15cnp6eevrpp2m6gQKM+gwUHC1atNCqVasUEhIiX19fPfHEE3ZHApADsjUM7na7rZM3WTNwuTjzIpAH3XDDDYqJidEvv/yi6Ohou+MAyEXUZ6BgufXWW7V27VodO3ZM4eHhdscBkAOy1XgbYxQVFZXusLWzZ8+qQ4cO8vLysqZt2bIl5xIC+M+qV6+u6tWrXzL92LFjKlasmA2JAOQG6jNQ8NSrV++SacYYHT9+nBoO5EPZaryfeeaZS6Z16tQpx8IAyH2vvPKKXnvtNS1fvlwNGjSwO85/5nYb/Xo0QXFnkxXs56lqxQPldHI4PQon6jNQODzxxBOaPn26Vq1apZo1a9odB0A2ZOvkavkRJ2YB/s+cOXN0xx13SJKCgoK0ZMmSdL8LzS827z+pT77br71HE5WUkiovl4eqFA9Q/8YVdGOFonbHA7KFOnVlrBvg/0yePFlDhgyRJIWFhWnFihWqX7++zamAwi3XTq6WmfPnz+u1117LyVkCyEFt27a1Gu34+Hi1bt1aq1atsjlV9mzef1IvLt6tHQfjFOTjUtkifgrycWnnoTi9uHi3Nu8/aXdEIM+hPgP5X9euXa1G+/jx42revLm+//57m1MByKpsN97Hjh3TokWLtGLFCuukLcnJyXrrrbdUsWJFjRs3LsdDAsgZwcHBWr58uVq1aiXp4m9A27dvr6+//trmZFnjdht98t1+nT6brIqhfvL3dsnD6ZC/t0sVivop7lyypn+3X253gT6QB7gs6jNQsBUrVkxr1qxR48aNJUlxcXFq1aqV1qxZY3MyAFmRrcZ7/fr1qlq1qjp27Ki2bduqcePG2rVrl2rXrq3Jkyfr2Wef1YEDB3IrK4Ac4O/vr4ULF6pz586SpKSkJHXt2lWzZ8+2N1gW/Ho0QXuPJqp4oPcll0dzOBwqFuCtPUcT9evRBJsSAvagPgOFQ0hIiFasWKGoqChJ0pkzZ9SuXTstWrTI5mQAMpOtxvvJJ59Uu3bttG3bNj300EPatGmTunTpopdeekm7du3SkCFD5Ovrm1tZAeQQb29vzZs3T3369JEkpaamqk+fPvrwww9tTpaxuLPJSkpJlY+nx2X/3cfTQ0kpqYo7m3yNkwH2oj4DhYe/v78WLVqkjh07SpIuXLigLl26aO7cuTYnA5CRbJ1cLTQ0VLGxsapVq5bOnTungIAAzZ8/P0+fOZUTswBX5na7NXToUE2ZMsWaNmXKFN1zzz02prqyn/+O10NztyrIxyV/70svynDmQoriz6fojZ51VaMk73fkDzlRp/Jjfc4KajhwZcnJyerfv791xJrD4dDs2bPVs2dPm5MBhUeunVzt1KlTCgsLkyT5+vrKz89PderU+e9JAdjK6XTq/fff18MPPyzp4u/HIiIibE51ZdWKB6pK8QAdS7ygf48ZGmN0LPGCqhYPULXigTYlBOxBfQYKH09PT82YMUN33323JKls2bK69dZbbU4F4EqydR1vSdq1a5f+/vtvSRe/6P7yyy86c+ZMuvvccMMNOZMOQK5zOBx69dVXVaxYMUVHR6t69ep2R7oip9Oh/o0r6MXFu7X/5FkVC/CWj6eHzien6ljiBQX7eqpf4wpczxuFEvUZKHw8PDw0ZcoUlSlTRr1791aFChXsjgTgCrJ1qLnT6ZTD4bhkT5Mka7rD4bDOppoXcJga8N+lpKTI6XTK6czRKw9etctdx7tq8QD14zreyIdyok7lx/qcFdRw4L9LSkqSp6fnJScjBZBzslOnsrXH+/fff7+qYADyj9TUVPXr108ul0sfffSRXK5sHyCTa26sUFT1yxXRr0cTFHc2WcF+nqpWPJA93Si0qM8A/ik5OVndunVT+fLl9fbbb+e5AXSgMMrWN2kOXwEKj/vvv986YcuZM2f06aefytvb2+ZU/8fpdHACNeD/oz4DSGOMUb9+/axLjJ05c0YffvhhnhpABwqjbA1/nTlzRkOHDlWZMmVUrFgx9erVS8eOHcutbABs1Lp1a3l5eUmSdXbks2fP2pwKwOVQnwGkcTgcat++vTw8Ll5685NPPlGvXr2UlJRkczKgcMtW4/3UU09pxowZuu2229S7d2+tWbNGgwcPzq1sAGzUuXNnLVq0SH5+fpKk5cuXKzo6WvHx8TYnA/Bv1GcA/9S3b1999tln8vT0lCR98cUX6ty5s86dO2dzMqDwytbJ1SpVqqTx48ere/fukqTNmzfr1ltv1blz5/Ls4SucmAW4OuvXr1f79u2thvvmm2/W0qVLFRoaanMyoGDIiTqVH+tzVlDDgauzfPlydenSxWq4IyMjtXDhQgUGctlNICfk2nW8//rrLzVp0sS6feONN8rT01OHDh36b0kB5HlNmzbVmjVrrEZ706ZNatasmXXZIgD2oz4DuJw2bdpo2bJlVqO9bt06tWzZUidPnrQ5GVD4ZKvxdrvd1iEraVwuV767PAmA7Lnxxhu1bt06lSxZUpK0Y8cOhYeH68CBAzYnAyBRnwFcWUREhFavXq2iRS9ebnPjxo1q1qyZjh49anMyoHDJ1vFnxhhFRUWlO2zt7Nmz6tChg3USJknasmVLziUEkCfUrl1bsbGxatmypfbv36+AgAAOVQPyCOozgIzcfPPN1t7uI0eOKDAwUP7+/nbHAgqVbDXezzzzzCXTOnXqlGNhAORtVapUUWxsrIYPH64PPvhAISEhdkcCIOozgMzVqVNHsbGxGjVqlKZNm0bjDVxj2Tq5Wn7EiVmAa8MYI4fDYXcMIN+hTl0Z6wa4NqjhwH+TaydXA4DLiYuLU1RUlGJiYuyOAgAAsuHYsWOKjIzUjz/+aHcUoECj8QZwVc6ePavbbrtNa9euVXR0tJYvX253JAAAkAVxcXFq06aNYmNj1aJFC61fv97uSECBReMN4Ko4HA7rJGvnzp1Thw4dNH/+fJtTAQCAzPyzhickJKh169ZauXKlzamAgonGG8BV8fX11YIFC9StWzdJUnJysnr06KEZM2bYnAwAAGQkKChIS5cuVZs2bSRdHEC/7bbbtGDBAnuDAQVQthrv5ORkJSUlZfkvJSUlt3IDyEO8vLw0Z84c9e/fX5KUmpqqfv366b333rM5GVA4UJ8B/Fd+fn766quv1LVrV0lSUlKSbr/9ds2aNcvmZEDBkq3LidWuXVtly5ZVZidCdzgcMsbozJkz2rhx41UFBJA/uFwuffTRRwoICNCkSZMkSffdd58SExP16KOP2pwOKNiozwCuhre3t+bOnauBAwdqxowZSk1N1Z133qnExETde++9dscDCoRsNd7+/v5as2ZNlu9/8803ZzsQgPzL6XTq7bffVmBgoMaNGydJGjVqlOLj4/X8889zqRIgl1CfAVwtl8uljz/+WAEBAXrvvfdkjNGQIUOUkJCgRx55xO54QL6XrUPNs/ulmS/ZQOHjcDj08ssv66WXXrKm/fzzz3K73TamAgo26jOAnOB0OjVp0iSNGjXKmrZ79+5Mj6YBkLls7fEGgKwaPXq0AgICtGzZMs2aNUseHh52RwIAAJlwOBwaN26cgoKCtG3bNk2ZMoXBOiAH0HgDyDX333+/hg0bJqeTCygAAJBfOBwOjRkzRm63mxoO5BDeSQBy1b8L9p49ezRo0CCdP3/epkQAACAr/l3Dt23bpqFDhyo5OdmmRED+la093p6enmrcuHGWf+cRGhr6n0IBKJj+/PNPtWzZUn/++af279+vBQsWKCAgwO5YQL5HfQaQ23799Ve1atVKR48e1eHDhzVnzhz5+PjYHQvIN7LVeG/YsCG3cgAoBPbv368TJ05IklavXq3WrVtryZIlCgkJsTcYkM9RnwHktt9++01xcXGSpK+++kodOnTQggUL5O/vb3MyIH9wmGycpvDBBx/UsWPHsjzzKlWq6Pnnn/9PwXJKfHy8goODFRcXp6CgIFuzIG9xu41+PZqguLPJCvbzVLXigXI6OXlIbvvhhx/Utm1bnT59WpJUv359LV++XMWKFbM3GGCTnKhT+bE+ZwU1HMhbVq9erU6dOunMmTOSpCZNmmjx4sUKDg62ORlgj+zUqWw13nXr1tXXX3+dpfsaY9SjRw9t3Lgxq7PPFRRtXM7m/Sf1yXf7tfdoopJSUuXl8lCV4gHq37iCbqxQ1O54Bd7//vc/tW7d2moUatasqZUrV6pMmTI2JwOuvZyoU/mxPmcFNRzIe7777ju1a9fO2vvdoEEDLV++XGFhYTYnA6697NSpbB1q7nQ6VaFChSzfn2v+IS/avP+kXly8W6fPJqt4oLd8PL11PjlVOw/F6cXFuzWmfU2a71xWr149xcTEqGXLljp48KB2796t8PBwrV69WpUqVbI7HpDvUJ8BXCuNGzfW2rVr1bp1ax0/flxbtmxRZGSkVq5cqdKlS9sdD8izsnVW8+xew49r/iGvcbuNPvluv06fTVbFUD/5e7vk4XTI39ulCkX9FHcuWdO/2y+3my+lua1GjRqKjY1V5cqVJUm///67mjZtqt27d9ucDMh/qM8ArqX69esrJibGarR37dql8PBw/fHHH/YGA/IwLieGQuXXownaezRRxQO9L/ni6XA4VCzAW3uOJurXowk2JSxcKlWqpNjYWNWqVUuSdOjQIS1ZssTmVAAAIDM1a9ZUbGysdaTab7/9ptWrV9ucCsi7snWoOZDfxZ1NVlJKqnw8vS/77z6eHjqeeEFxZ7k+5bVSunRprVu3Tm3atFHLli310EMP2R0JAABkQeXKlRUbG6uWLVuqX79+GjRokN2RgDwrW433uXPnsnwWVH4/hrwo2M9TXi4PnU9Olb/3pZv/+eSLJ1oL9vO0IV3hFRYWppiYGPn5+XEILPAfUJ8B2KVMmTL68ccfuawYkIlsNd6TJ0/WuXPnsnz/Nm3aZDsQkJuqFQ9UleIB2nkoTn5eHumaPGOMjiVeUJ3SwapWPNDGlIXT5Qr2ypUrdeHCBd122202JALyD+ozADtdroYvWLBAwcHBat68uQ2JgLwnW413REREbuUArgmn06H+jSvoxcW7tf/kWRUL8JaP58U94McSLyjY11P9Glfget55wPr169W5c2clJSVp5syZ6tmzp92RgDyL+gwgL1m+fLl69OghDw8Pff7552rfvr3dkQDbcXI1FDo3ViiqMe1rqnbpYMWfT9Ffp84q/nyK6pQO5lJiecjMmTN19uxZpaSk6I477tDUqVPtjgQAALJg2rRpSk5O1vnz59W5c2d99tlndkcCbMfJ1VAo3VihqOqXK6JfjyYo7myygv08Va14IHu685BJkyYpNTVVH374oYwxuvvuu5WYmKgHH3zQ7mgAACADM2bMkCTNnTtXKSkp6tWrlxITE3XXXXfZnAywD403Ci2n06EaJYPsjoEr8PDw0JQpUxQYGKgJEyZIkkaMGKHExEQ98cQTnIQNAIA8ytPTU7NmzZK/v78++ugjud1uDRw4UGfOnNHw4cPtjgfYgkPNAeRZDodDr7/+up5++mlr2pNPPqnRo0dzZmYAAPIwDw8PffDBB+mOVLv//vv18ssv25gKsA+NN4A8zeFw6LnnntOrr75qTXvllVc0fPhwud1uG5MBAICMOJ1OTZgwQU8++aQ17YknnmAAHYUSjTeAfOGRRx7Re++9Zx1iPnnyZG3ZssXmVAAAICMOh0MvvPCCxo0bZ02bMGGCfvnlFxtTAdcejTeAfGPIkCGaPn26PD09NWPGDN100012RwIAAFnw2GOPadKkSfLy8tLnn3+uGjVq2B0JuKYcpoAf5xEfH6/g4GDFxcUpKIgTaQEFwZ9//qny5cvbHQPIEdSpK2PdAAUPNRwFSXbqFHu8AeQ7lyvYn3/+uRISEmxIAwAAsupyNXz27Nk6d+6cDWmAa4fGG0C+N3XqVHXv3l0tW7bUyZMn7Y4DAACy6I033lDv3r3Vvn17BtBRoNF4A8jX4uPjNXr0aEnSxo0b1axZMx05csTmVAAAIDNHjhzRc889J0lau3atWrVqpVOnTtmcCsgdNN4A8rWgoCCtWbNGJUuWlCRt375d4eHh+vPPP21OBgAAMlKiRAmtWrVKRYoUkSRt2LCBAXQUWDTeAPK9OnXqKCYmxvrd2J49exQeHq69e/fanAwAAGTk5ptv1rp161SiRAlJ0rZt2xQREaG//vrL5mRAzqLxBlAgVK1aVbGxsapataqki2dNDQ8P144dO2xOBgAAMnL99dcrJiZG5cqVkyT9+uuvCg8P1759+2xOBuQcGm8ABUb58uUVExOjOnXqSJL+/vtvRUZG6scff7Q5GQAAyEi1atUUGxur6667TpL0xx9/KDw8XLt27bI5GZAzaLwBFCglS5bUN998o5tuukmSdPLkSfXo0UPJyck2JwMAABmpUKGCYmNjVbt2bUnS4cOH1atXL7ndbpuTAVePxhtAgRMaGqrVq1crPDxcgYGBmjdvnjw9Pe2OBQAAMlGqVCl98803uvHGGxUaGqo5c+bI6aRlQf7nsjsAAOSGoKAgLVu2TL/88ovq169vdxwAAJBFYWFhWr16tQ4cOKBatWrZHQfIEQwfASiw/Pz8Lmm63W63vvvuO5sSAQCArAgODrbO2ZImJSVFP/zwg02JgKtD4w2g0DDG6MEHH1TTpk01efJku+MAAIAscrvdGjhwoJo2barZs2fbHQfINhpvAIXGwoUL9c4778gYoyFDhui1116zOxIAAMiCmTNnasaMGUpNTVWfPn30wQcf2B0JyBYabwCFRocOHTRq1Cjr9qOPPqpnnnlGxhgbUwEAgMz07dtXQ4YMkXTxCLbBgwfrjTfesDkVkHU03gAKDYfDoXHjxmns2LHWtOeff14PP/wwzTcAAHmY0+nUu+++q0ceecSa9vDDD+u5556jhiNfoPEGUKg4HA6NGTNGEyZMsKZNmDBB9957r1JTU21MBgAAMuJwODR+/Hg999xz1rRnn31Wjz76KM038jwabwCF0ogRI/Thhx/K4XBIkj744AP169dPycnJNicDAABX4nA49PTTT6c7zPz111/X0KFD5Xa7bUwGZIzGG0ChNWjQIH366adyuVySpE8//VSPPfaYzakAAEBmRo4cqSlTplgD6JMnT073UzIgr6HxBlCo9erVS/Pnz5e3t7euu+46Pfroo3ZHAgAAWXDPPfdo5syZ8vDwUJ06dTRs2DC7IwFX5LI7AADYrUOHDlq6dKkqV66sUqVK2R0HAABkUe/evVW0aFHVq1dPoaGhdscBrojGGwAkNW/e/JJpiYmJOn/+vMLCwmxIBAAAsiI6OvqSaadOnZLT6VRwcLANiYBLcag5AFzG+fPn1alTJ0VGRurQoUN2xwEAAFmUkJCgtm3bKioqSidOnLA7DiCJxhsALuv+++/XmjVrtGvXLoWHh+uPP/6wOxIAAMiCgQMHasOGDdq8ebMiIyN1+PBhuyMBNN4AcDmjR49WpUqVJEm//fabmjZtqp9//tnmVAAAIDPPPfecdc6WnTt3Kjw8XPv377c5FQo7Gm8AuIzKlSsrNjZWNWrUkCQdPHhQERER2rp1q83JAABARmrVqqXY2FhVqFBBkrRv3z41bdpUv/76q83JUJjReAPAFZQpU0br1q1TvXr1JEnHjh1Ts2bN9MMPP9gbDAAAZOi6667T+vXrVb16dUnSX3/9pfDwcG3bts3mZCisaLwBIAPFixfX2rVr1ahRI0nS6dOn1bJlS61du9bmZAAAICNly5ZVTEyM6tatK0k6evSomjVrpo0bN9qcDIURjTcAZCIkJEQrVqxQixYtJElnzpxRu3bt2PMNAEAelzaA3rBhQ0kXLzMWFRWlHTt22JwMhQ2NNwBkQUBAgBYvXqzbbrtNktSkSRPrEHQAAJB3FSlSRCtXrlSzZs0kSS1btrTO4QJcKzTeAJBFPj4+mj9/vp577jl99dVX8vHxsTsSAADIgsDAQC1ZskTPPvus5syZI5fLZXckFDIOY4yxO0Ruio+PV3BwsOLi4hQUFGR3HAAFVGJiogICAuyOgXyIOnVlrBsA1wI1HP9VduoUe7wB4CodOnRIdevW1csvv2x3FAAAkA2//fabatSooXfffdfuKCjgaLwB4CqcPXtWrVq10m+//aYnnnhCo0ePVgE/kAgAgALh1KlTatmypQ4ePKhhw4bplVdesTsSCjAabwC4Cn5+furfv791e9y4cXrggQfkdrttTAUAADITEhKiO+64w7r9+OOPa8yYMQygI1fQeAPAVRo1alS6Q9TeeecdDRo0SCkpKTamAgAAGXE4HHrxxRfT/VTspZde0ogRIxhAR46j8QaAHDB06FB98skncjovfqx+/PHH6t27t5KSkmxOBgAAMvL444/rnXfesW5PnDhR99xzj1JTU21MhYKGxhsAcki/fv00b948eXp6SpI+++wzdenSRefOnbM5GQAAyMiwYcM0bdo0awD9o48+YgAdOYrGGwByULdu3fT1119b1/hesmSJ2rVrp/Pnz9ucDAAAZGTAgAGaO3euNYA+b948de3alZ+OIUfQeANADouOjtby5csVGBgoSbrhhhvk7e1tcyoAAJCZ22+/XQsWLLAG0Bs0aCCXy2VzKhQEbEUAkAsiIiK0evVqffrpp3r99dflcDjsjgQAALKgXbt2Wrp0qVauXKnnnnvO7jgoIBymgJ8vPz4+XsHBwYqLi1NQUJDdcQBAbrfb+g0ZQJ26MtYNgLyGGo5/yk6dYqsBgGto48aNqlevnvbt22d3FAAAkA1r167VTTfdpIMHD9odBfkQjTcAXCM7duxQdHS0tm/frvDwcO3atcvuSAAAIAs2bNigDh066KefflJ4eLh+++03uyMhn6HxBoBrJDQ0VKVLl5YkHT58WBEREdqyZYvNqQAAQGaKFy+uEiVKSJJ+//13hYeHa/fu3TanQn5C4w0A10ipUqX0zTff6MYbb5QknThxQs2bN9e3335rczIAAJCRSpUqKTY2VrVq1ZIkHTp0SBEREfrpp59sTob8gsYbAK6hsLAwrV69Wk2bNpV08aQcrVu31sqVK21OBgAAMlK6dGmtW7dODRo0kCQdP35czZs313fffWdzMuQHNN4AcI0FBwdr2bJlat26tSTp7Nmzuu222/TVV1/ZnAwAAGQkLCxMa9asUZMmTSRJcXFxatWqlVatWmVzMuR1NN4AYAN/f399/fXX6tKliyQpKSlJ3bp106effmpzMgAAkJHg4GAtX75cLVu2lHRxAL19+/b6+uuvbU6GvIzGGwBs4u3trXnz5qlPnz6SpNTUVJ0/f97mVAAAIDP+/v5auHChOnbsKElKSUlRUlKSzamQl7nsDgAAhZnL5dL06dMVEBCg2rVra+DAgXZHAgAAWeDj46PPP/9cd911l1q1aqXbb7/d7kjIw2i8AcBmTqdT7733nhwOh91RAABANnh6emrGjBnUcGSKQ80BIA+4XMGeNWuWRo0aJWOMDYkAAEBWXK6Gv/vuuxo7diw1HBb2eANAHvTll1+qf//+Sk1NVXx8vN599105nYyVAgCQ102fPl3Dhg2TJCUkJGjcuHHsEQd7vAEgLzpx4oTcbrckafLkyerXr59SUlJsTgUAADJz/Phx6//Hjx+vYcOGWTUdhReNNwDkQXfffbdmzpwpDw8PSRcPO+/evbsuXLhgczIAAJCRhx56SO+//761l/u9997TgAEDGEAv5Gi8ASCP6t27t7744gt5eXlJkhYsWKCOHTvq7NmzNicDAAAZuffeezV9+nRrAH3GjBnq2bMnA+iFGI03AORhnTp10qJFi+Tn5ydJWrFihdq0aaO4uDibkwEAgIz07dtXn332mTWAPn/+fHXu3JkB9EKKxhsA8rhWrVppxYoVCgoKkiStX79eUVFROnHihM3JAABARrp06aKFCxfK19dXkrRs2TK1bdtW8fHxNifDtUbjDQD5QJMmTbR27VqFhoZKknbv3q19+/bZnAoAAGSmdevW6QbQt23bpgMHDticCtcajTcA5BMNGjRQTEyMKlWqpK+//lq33HKL3ZEAAEAWNG3aVGvWrFGFChW0dOlS1a5d2+5IuMa4jjcA5CO1atXSzz//bP1eDAAA5A833nijfv31V2p4IcUebwDIZ/5dsI0xeuGFF7Rt2zabEgEAgKy4XA0fM2aM9uzZY1MiXCs03gCQz73wwgt6+umn1axZM23cuNHuOAAAIAuMMXr44Yf10ksvKTw8XNu3b7c7EnIRjTcA5GMXLlzQ0qVLJUmnTp1SVFSU1q1bZ3MqAACQmcTERK1atUqSdOTIETVr1kybNm2yORVyC403AORj3t7eWrFihZo1aybpYhGPjo62mnEAAJA3BQYG6ptvvrFOlnry5ElFRUUpJibG5mTIDTTeAJDPBQYGasmSJWrXrp0k6fz58+rUqZO++OILm5MBAICMFC1aVKtWrVJkZKQkKSEhQdHR0Vq+fLnNyZDTaLwBoADw9fXVl19+qe7du0uSkpOT1aNHD33yySc2JwMAABlJG0Bv27atJOncuXPq0KGD5s+fb3My5CQabwAoILy8vDR79mzdddddkiS3260BAwbo3XfftTkZAADIiJ+fnxYsWKBu3bpJ+r8B9BkzZticDDmFxhsAChAPDw99+OGHuv/++61pr776qhITE21MBQAAMuPl5aU5c+aof//+kqTU1FSNGzdOSUlJNidDTnDZHQAAkLOcTqfeeustBQYGatq0aVq1apUCAgLsjgUAADLhcrn00UcfKSAgQEuWLNGKFSsuufY38ieHMcbYHSI3xcfHKzg4WHFxcQoKCrI7DgBcU8ePH1dYWJjdMZAB6tSVsW4AFFbGGJ04cYIansdlp05xqDkAFGD/LtgXLlzQW2+9pdTUVJsSAQCAzDgcjktq+JkzZ/TOO++ogO83LbBovAGgkEhJSVHv3r01YsQI9e7dm9+MAQCQT1y4cEFdunTR/fffr3vuuYcB9HyIxhsAConNmzdr4cKFkqR58+apa9euOnfunM2pAABAZmJiYrR69WpJ0tSpU9W3b18lJyfbnArZQeMNAIVEw4YNtWDBAvn4+EiSFi9erPbt23PGcwAA8rhWrVpp9uzZcrkunht7zpw56tatm86fP29zMmQVjTcAFCLt2rXT0qVLrbOcr127Vq1atdKpU6dsTgYAADLSo0cPLViwQN7e3pKkhQsX6rbbbmMAPZ+g8QaAQqZZs2ZatWqVQkJCJEk//PCDWrRooaNHj9obDAAAZKh9+/ZasmSJ/P39JUmrV69WmzZtdPr0aXuDIVM03gBQCDVs2FDr1q1T8eLFJUn/+9//FBkZqYMHD9qcDAAAZKRFixbpBtC/++47tWjRQseOHbM3GDJE4w0AhdQNN9ygmJgYlS1bVpL0888/6/HHH7c5FQAAyMytt96qtWvXqlixYpKkn376Sc8//7zNqZARGm8AKMSqV6+u2NhYXXfddbr55ps1adIkuyMBAIAsqFevnmJiYlSmTBk1a9ZM48ePtzsSMuCyOwAAwF4VK1ZUbGysvLy8FBQUZHccAACQRTVq1NC3336rokWLytfX1+44yAB7vAEAKlWqlEJDQ9NNO3r0qDZs2GBTIgAAkBUVKlRQYGBgumkHDhzQ1q1bbUqEy6HxBgBc4tSpU2rdurV1AhcAAJA//P3334qKilKzZs30/fff2x0H/x+NNwDgEi+++KK2bt2qs2fPqn379lq4cKHdkQAAQBY8+eST2rNnj06fPq1WrVppzZo1dkeCaLwBAJcxduxYderUSZKUlJSkrl27as6cOTanAgAAmXnzzTcVFRUlSTpz5ozatWunxYsX25wKNN4AgEv4+Pjos88+U+/evSVJKSkp6t27tz788EObkwEAgIwEBARo0aJF6tChgyTpwoUL6ty5s+bNm2dzssKNxhsAcFmenp6aPn267rnnHkmSMUb33HOP3nzzTXuDAQCADPn4+OiLL75Qr169JF0cQL/jjjv00Ucf2Zys8KLxBgBckYeHhyZPnqyHHnrImjZy5EiNHTtWxhgbkwEAgIx4enpq5syZGjRokCTJ7XZr0KBBmjhxos3JCicabwBAhhwOh1577TU988wz1rSnnnpKX375pY2pAABAZjw8PPTBBx9oxIgR1rQHH3yQE67ZgMYbAJAph8OhZ599Vq+99pokqXfv3tbJ1wAAQN7lcDj0xhtv6KmnnpIkDR06VM2bN7c5VeHjsjsAACD/ePjhh1WrVi21bNlSHh4edscBAABZ4HA49Pzzz6thw4Zq27atHA6H3ZEKHfZ4AwCypW3btvL09Ew3bc+ePbpw4YJNiQAAQFa0b99eTmf6FvDnn39WSkqKTYkKDxpvAMBV+fnnn9WkSRN16tRJZ8+etTsOAADIoi1btqhhw4bq1auXkpKS7I5ToNF4AwD+s5SUFHXs2FHHjh3T8uXLFR0drfj4eLtjAQCATJw7d04dOnRQfHy8vvjiCwbQcxmNNwDgP3O5XProo48UFBQkSYqNjVVUVJROnDhhczIAAJARX19fffTRR/L19ZUkLVu2TO3atVNCQoLNyQomGm8AwFVp2rSp1qxZo9DQUEnSjz/+qGbNmunvv/+2ORkAAMhImzZttGzZMgUGBkqS1q1bp5YtW+rkyZM2Jyt4aLwBAFftxhtv1Lp161SyZElJ0o4dOxQeHq79+/fbnAwAAGQkIiJCq1evVtGiRSVJGzduVLNmzXTkyBGbkxUsNN4AgBxRu3ZtxcbGqnz58pKkvXv3Kjw8XHv27LE5GQAAyMjNN9+sdevWqUSJEpKk7du3Kzw8XAcOHLA5WcFB4w0AyDFVqlTR+vXrVa1aNUnSgQMHFBERodOnT9sbDAAAZKhOnTqKjY1VuXLlJF28VGhkZKTOnTtnc7KCgcYbAJCjypUrp5iYGF1//fWSpIceekghISH2hgIAAJmqWrWq1q9frypVqkiSHn/8cevka7g6LrsDAAAKnhIlSuibb77RF198oXvuucfuOAAAIIvKly+v2NhYLV++XP3797c7ToHBHm8AQK4oWrToZZvu48eP25AGAABkVcmSJS/bdFPD/zsabwDANbNkyRJVrFhRX3zxhd1RAABANsydO1eVKlXSihUr7I6SL9F4AwCuia1bt6pbt246c+aMevTooenTp9sdCQAAZMG3336rvn37KjExUR06dNCXX35pd6R8h8YbAHBN1K5dWz179pQkud1u9e/fX++9957NqQAAQGZuuukmdezYUZKUlJSk7t27a+bMmTanyl9ovAEA14TL5dJHH32kYcOGWdPuu+8+vfrqqzamAgAAmfH29tbcuXN15513SpJSU1PVr18/TZ482eZk+QeNNwDgmnE6nXr77bf1+OOPW9NGjRqlp556SsYYG5MBAICMuFwuffzxxxo6dKgkyRijIUOG6LXXXrM5Wf5A4w0AuKYcDodefvllvfTSS9a0sWPHauTIkTTfAADkYU6nU5MmTdKoUaOsaY8++qieeeYZangmaLwBALYYPXq0Jk6caN1+6623dP/999uYCAAAZMbhcGjcuHEaO3asNe3555/XU089ZWOqvI/GGwBgm/vvv19Tp06V0+mUp6en2rdvb3ckAACQCYfDoTFjxmjChAmSJF9fX7Vp08bmVHmby+4AAIDCbeDAgQoICJDD4VDbtm3tjgMAALJoxIgRCgwMVJkyZRQeHm53nDyNxhsAYLsePXpcdnpSUpK8vLyucRoAAJBVgwYNumSaMUbJycnU8H/gUHMAQJ70yiuvqHnz5jp9+rTdUQAAQDaMGTNGt912m86cOWN3lDyDxhsAkOd88MEHevzxx/Xdd9+pRYsWOnbsmN2RAABAFrz22mt6+eWXtXLlSrVp00ZxcXF2R8oTaLwBAHnOzTffrGLFikmSfvrpJ0VGRurgwYM2pwIAAJlp3LixgoODJUnffvutWrRooePHj9ucyn403gCAPKdevXqKiYlRmTJlJEm7d+9WeHi4fv/9d5uTAQCAjDRu3Fhr165VWFiYJGnLli2KjIzUoUOHbE5mLxpvAECeVKNGDcXGxqpy5cqSpN9//13h4eH6+eefbU4GAAAyUr9+fcXExKh06dKSpF27dikiIkJ//PGHvcFsROMNAMizKlWqpNjYWNWqVUuSdPDgQUVEROh///ufvcEAAECGatasqdjYWFWqVEmStG/fPoWHh+uXX36xOZk9aLwBAHla6dKltW7dOjVo0ECSdOzYMTVv3lwbNmywORkAAMhI5cqVFRsbqxo1akiS/vrrL0VERGjbtm02J7v2aLwBAHleWFiY1qxZo8aNG0uSPD09rRO3AACAvKtMmTKKiYlRvXr1JEleXl6FsobTeAMA8oXg4GCtWLFCt99+u1asWGGNngMAgLytWLFiWrt2rTp37qzVq1erQoUKdke65lx2BwAAIKv8/f312WefXTLdGCOHw2FDIgAAkBUhISH68ssvL5leWGo4e7wBAPlaamqq+vfvr2nTptkdBQAAZENycrK6d++uzz//3O4ouY7GGwCQbxljNHToUM2YMUMDBw7U22+/bXckAACQBampqbrzzjv1xRdfqGfPnvrkk0/sjpSraLwBAPmav7+/9f8PPPCAXnrpJRvTAACArEqr4W63WwMGDNCkSZNsTpR7aLwBAPmWw+HQG2+8oaeeesqaNmbMGI0ePVrGGBuTAQCAjHh4eOiDDz7QAw88YE0bPny4xo0bZ2Oq3EPjDQDI1xwOh55//nmNHz/emjZu3Djdf//9crvdNiYDAAAZcTqdevPNNzVmzBhr2ujRo/XEE08UuAF0Gm8AQIHw6KOP6t1337VuT5o0SQMHDlRKSoqNqQAAQEYcDofGjh2bbk/3yy+/rAcffLBADaDTeAMACoyhQ4dq+vTpcjovlrdPPvlEvXr1UlJSks3JAABARh577DG988471u23335bgwYNUmpqqo2pcg6NNwCgQLnzzjv12WefydPTU5K0d+9enT171uZUAAAgM8OGDdPHH39sDaD/8ssvOn/+vM2pcgaNNwCgwOnatau+/vpr1a9fXytWrFBISIjdkQAAQBb0799fc+fOVcOGDbVkyZJ0Vy/JzxymoP1q/V/i4+MVHBysuLg4BQUF2R0HAHANud1ua9Q8r6JOXRnrBgAKr4JWw/P2MwEA4Cr8u2DHx8erf//+OnLkiE2JAABAVvy7hh8/flz9+/fXqVOnbEp0dWi8AQCFwtmzZ3Xbbbdp+vTpCg8P14EDB+yOBAAAsiAuLk5t2rTR9OnT1bx5cx09etTuSNlG4w0AKBSOHj2qP/74Q5K0Z88ehYeHa+/evfaGAgAAmTp06JD++usvSdLWrVsVERFh3c4vaLwBAIVCxYoVtX79elWpUkWStH//foWHh2vHjh02JwMAABmpWbOmYmNjVa5cOUkXz3YeHh6uffv22Zws62i8AQCFRvny5RUbG6s6depIkv7++29FRkbqxx9/tDkZAADISLVq1RQbG6vrrrtOkvTHH38oPDxcu3btsjlZ1tB4AwAKlZIlS+qbb77RTTfdJEk6efKkWrRoodjYWJuTAQCAjFSoUEGxsbGqXbu2JOnw4cOKiIjQli1bbE6WORpvAEChExoaqtWrVys8PFySlJCQoDZt2mjFihU2JwMAABkpVaqUvvnmG914442SpBMnTqh58+b69ttvbU6WMRpvAEChFBQUpGXLlqlNmzaSpHPnzumTTz6xORUAAMhMWFiYVq9eraZNm0q6eLnQTz/91OZUGXPZHQAAALv4+fnpq6++Uu/evXXu3DlNmzbN7kgAACALgoODtWzZMnXt2lUhISGaOHGi3ZEyROMNACjUvL29NXfuXCUnJ8vLy8vuOAAAIIv8/f319ddfy+FwyMPDw+44GeJQcwBAoedyueTr65tu2t69e/Xuu+/alAgAAGSFt7f3JQPn27Zty3NHsbHHGwCAfzlw4ICioqL0559/6siRI3r22WflcDjsjgUAADLx66+/qnXr1jpy5IhOnTqlhx56yO5IktjjDQDAJRYtWqQ///xTkvT888/r4YcfljHG5lQAACAzX3zxhY4cOSJJevjhh/X888/niRpO4w0AwL8MHTpUEyZMsG5PmDBB9957r1JTU21MBQAAMvP444/rueees24/88wzGjVqlO3Nt62Nd0xMjDp06KDSpUvL4XBowYIF6f7dGKOnn35apUqVkq+vr1q2bKk9e/bYExYAUKiMGDFCH374oXWI+QcffKB+/fopOTnZ5mS5Lyfq88mTJ9WnTx8FBQUpJCREgwYNUmJi4jV8FgCAwsjhcOjpp5/WG2+8YU177bXXNHToULndbtty2dp4nzlzRnXr1tWkSZMu++/jx4/XxIkT9f7772vDhg3y9/dXmzZtdP78+WucFABQGA0aNEizZ8+Wy3XxlCiffvqpunfvXuDrUE7U5z59+mjnzp1auXKlFi1apJiYGA0ePPhaPQUAQCE3cuRITZkyxRpAnzx5svr166eUlBRb8jiM3fvc/z+Hw6Evv/xSnTt3lnRxNL106dJ6+OGH9cgjj0iS4uLiVKJECX388cfq1atXluYbHx+v4OBgxcXFKSgoKLfiAwAKsIULF6p79+66cOGCJKlly5ZasGCB/P39r3reeb1O/Zf6vHv3btWqVUubNm3STTfdJElatmyZ2rVrp7/++kulS5fO0rLz+roBAOR9s2fP1p133mn9XKxz586aM2eOvL29r3re2alTefY33r///rv+/vtvtWzZ0poWHByshg0b6vvvv7/i4y5cuKD4+Ph0fwAAXI0OHTpo8eLF8vPzkyStWrVK8+bNszmVPbJSn7///nuFhIRYTbd0cbDC6XRqw4YNV5w3NRwAkNPuuOMOffHFF9YlxxYsWKAlS5Zc8xx5tvH++++/JUklSpRIN71EiRLWv13Oyy+/rODgYOuvXLlyuZoTAFA4REVFaeXKlQoODtajjz6qAQMG2B3JFlmpz3///beKFy+e7t9dLpeKFi1KDQcAXHOdOnXSokWL5OfnpxdffFFdunS55hkK3HW8R48ene5abfHx8RRuAECOaNy4sbZu3ary5ctzXe9cQA0HAOSWVq1aadeuXapQoYIty8+zjXfJkiUlSUeOHFGpUqWs6UeOHFG9evWu+Dhvb+8cOV4fAIDLsatg5xVZqc8lS5bU0aNH0z0uJSVFJ0+etB5/OdRwAEBusrOG59lDzStVqqSSJUtq9erV1rT4+Hht2LBBjRo1sjEZAACFV1bqc6NGjXT69Glt3rzZus+aNWvkdrvVsGHDa54ZAAC72brHOzExUXv37rVu//777/rf//6nokWLqnz58hoxYoTGjh2rqlWrqlKlSnrqqadUunRp68yqAAAg511tfa5Zs6aio6N1zz336P3331dycrKGDx+uXr16ZfmM5gAAFCS2Nt4//vijmjdvbt1O+11X//799fHHH2vUqFE6c+aMBg8erNOnT6tp06ZatmyZfHx87IoMAECBlxP1edasWRo+fLiioqLkdDrVrVs3TZw48Zo/FwAA8oI8cx3v3MI1QAEAeRl16spYNwCAvKxAXMcbAAAAAICCgMYbAAAAAIBcROMNAAAAAEAuovEGAAAAACAX0XgDAAAAAJCLaLwBAAAAAMhFNN4AAAAAAOQiGm8AAAAAAHIRjTcAAAAAALmIxhsAAAAAgFxE4w0AAAAAQC5y2R0gtxljJEnx8fE2JwEA4FJp9SmtXuH/UMMBAHlZdmp4gW+8ExISJEnlypWzOQkAAFeWkJCg4OBgu2PkKdRwAEB+kJUa7jAFfIjd7Xbrl19+Ua1atXTgwAEFBQUpPj5e5cqVs25LytI0HsfjeByP43E8LquPyypjjBISElS6dGk5nfwC7J/cbrcOHTqkwMBAORyOq5rX1bxGLKdgLKcgPReWk3eXwXIK13KyU8ML/B5vp9OpMmXKSJKCgoLSrdx/387qNB7H43gcj+NxPC6rj8sK9nRfntPpVNmyZXN0nv/1NWI5BWc5Bem5sJy8uwyWU3iWk9UaztA6AAAAAAC5iMYbAAAAAIBcVCgab29vbz3zzDPy9va+7O2sTuNxPI7H8Tgex+Oy+jjkLdfqNWI5eXc5Bem5sJy8uwyWw3KupMCfXA0AAAAAADsVij3eAAAAAADYhcYbAAAAAIBcROMNAAAAAEAuKjCN97PPPiuHw5HuLyQkRCEhIXI6nXI4HKpYsaKqV68ul8t1yX0v95f2uKCgIFWvXl2BgYEKCwtTcHDwFR/j6+srX19fFStW7Ir3+efyPTw8FBYWJl9fX5UvX16DBg3Sddddd8lj6tatq02bNkmSdu7cedn7ZOcvMDBQDzzwgOLi4rR69WqVKlUq3b+XLl1aP/74oyRp3LhxV7Wsf6+fKlWqyM/Pz5oWFBSkefPmKTo62lrn/3wNO3XqpO3bt+uxxx5T0aJF0/172bJl9csvv0iSkpOTNXjwYHl4eFyy3HLlyqlDhw4qXbq0HA5HuuWn/fn7+6ts2bLy9fVVrVq1FBERoaCgoHT3efTRR3Xw4EH17dtXoaGhcrlc6TL7+PgoOjpa/fr1k6+vr/Ua+/j4XLI8Ly8va1vw9/dXyZIl5e/vL5fLJS8vrwzXYdoyXS6XihYtqsDAQOvfY2NjNXToUCu70+lUaGiomjZtetl188+/f/+70+lUz5491aFDB4WFhV3xcY0aNVJoaKg8PDys7J6envL29panp6ccDocqVKggT09POZ1OuVwuFStW7Ip50p6f0+m85D6enp4qW7asQkJCFBAQoG7duunIkSPy9va+7LwqVqxove5X+vv36+NyuXTdddfpmWee0eTJky/7np87d671+ZP2Hvnn6+BwOFSvXj3rdW3QoIFmzpypcuXKXfb5ent7X3Y7+ef26eHhYa0bb29vOZ1O67FNmzZV1apV022Padt52jRPT08FBQWlu09ISIjatGlzybb+778KFSqke5yHh4eaNGmiG264wboO5q233qoSJUpcsr0GBgbK5XJZ74kFCxbo3XffzfCztFWrVhmuD4fDoZYtW14yDw8PDz3wwAPp3u+X+ytTpoz8/f2t7bNfv36KiIiwtiMPDw/r/Z22DUZGRuq6666zPucvt+2k/Q0bNuzaF8JCKiEhQSNGjFCFChXk6+urxo0bW/VSkubPn6/WrVsrNDRUDodD//vf/y6ZR7NmzS55DYcMGZLhcmNiYtJtZwsWLLjkPrt371bHjh0VHBwsf39/3Xzzzfrzzz+vOM/33nsv3XuqUaNGWrp0qSTp5MmTuv/++1W9enXrO0NaHf+3jz/+WDfccIN8fHxUvHjxTLfHihUrXnE7zs5y/4t/1lRfX19df/311vePy8lsvWfl9U5jjFHbtm2v+PpJ0okTJ1S2bFk5HA41aNBAgYGBKl68uDp37mx9/0hz/vx5DRs2TKGhoenq0z9t2rRJUVFRCgkJUZEiRdSmTRtt3br1qtbRgAEDLnntoqOjM5xnZuvx2WefVY0aNeTv768iRYqoZcuW2rBhQ7r7nDx5Un369FFQUJBCQkI0aNAgJSYmZrjcy31fr1GjhjW/3NjW0mr0iBEjrGn/5T1/ORm9Z3NqOZktQ5K+//57tWjRQv7+/goKClJERITOnTuXreVk9NpI0pQpU9SsWTPr+8Lp06ezNf+sPp//upzMtmljjJ5++mmVKlVKvr6+atmypfbs2ZPuPv9lm86OAtN4S1Lt2rV1+PBh669ChQoKDw/XhAkTJEkul0uHDh1Sw4YNJUnt27eXw+FQ+/bttXXrVnXu3FmS5OfnJ0l655139NZbbykhIUG//vqr5s+fL19fX50/f14NGzbUlClT1KZNG+uLrSQ9/vjj2r17t+rWrSuHw6G77rpLy5YtU40aNayGo3LlyqpcubKCgoLk4+OjEydOaMKECfr44481a9Ys/fbbbypWrJhKliypqKgoSdK+ffsUFRWlgwcPqmPHjjp48KCCgoI0ZswYNW7cWJLk4eEhSfriiy/0yy+/KCgoSNHR0XrqqafUqlWrdGfue+6557Rs2TJ17txZ0dHROnr0qJo0aaJ33nlHPXv21JEjR7R582Zt2rRJkydPlq+vrzw8PHTffffptddes5rJ9u3ba82aNWrTpo18fX0lSU888YS+/fZb1atXT9dff70cDocee+wxffPNN3rzzTf1+++/y+l06vPPP9fSpUvVsmVL9erVSytWrFClSpXUvHlzSVLJkiX1ySefWIVx8+bNCgkJUZ06dRQWFiZJOnr0qFq3bq0zZ87o7NmzmjdvnpxOp8LCwjRq1CirsS1durTq1q2rSZMmSZLVuERHR6tSpUqSLr4hZ86cqd27d6tDhw6KjY3V9ddfr8cee0wVK1aUy+XSlClT1LhxY3l6emr+/Pny9/eXt7e36tevL19fXzVq1EjffPONZsyYoYYNG2rmzJm6+eablZKSolKlSun777/XZ599prCwMBUrVsz6MhQeHm49h6ZNm6patWq6/vrrdcMNN6hXr17q27ev2rRpY2V/5513tHjxYnXp0kWnT5/WmTNn1LJlS0nSjh079MEHH6hNmzZasGCBvvzySzkcDn377beqWbOmxowZY20HDRo00NatW9W7d2+FhITIw8NDDRo00JNPPqnw8HBJ0rx58+Tr66t7771XkvTQQw+pU6dOKl68uDWfbdu2adiwYbrrrrus7fDOO+/UzTffLLfbLeniF4c6depo8uTJKlOmjIwxCgsL09ixY6316+npKUkaOnSoPvjgA1WuXNl6fNryHA6HDh48KC8vLy1btkyHDh1Sq1at5Ha7VadOHT300EOSpFKlSkmSmjRpYr3uEyZM0MMPP6wmTZpY2V0ul7p3764ePXpY0yIjIzVhwgS9//77euyxxxQfH69ixYql+7zZv3+/pItfoiZNmiSHw6HExESFhoZa99m5c6c+++wzbd++XV27dtWdd96pv/76S76+vukKb0hIiFq3bq0qVapIuvgZ1KdPH3Xq1EkhISGSpJtuuklDhgyxPs/cbrcmTZqkuXPnqmbNmvrxxx+1d+9eNW7cWG+//bZq1qxpFd2bbrpJ06dPl8PhUEJCgowxevDBB1WjRg0lJSVpxYoVKlmypOrUqWNluv/++63P0lmzZsnhuDhYVLJkSQUEBEiSvvvuO9WvX1+bN2/Wjz/+qGPHjuno0aMqXbq0qlevLuniF9HU1FTdeeeduv766yVdbEYeeeQR1a9fXy+88IIkycvLS1OmTFGXLl3k4+Oj1atX6/rrr1fv3r2t19ThcGj79u3pPuPj4+NVuXJlPf7449Y2O2nSJJUuXdr6khUQEKA2bdrowQcf1NSpUyVJgwcP1siRI3XrrbdKkubMmaOtW7fqpptukiQNHDhQgwYNUt++fTV27FhJ0i+//KJp06bpzTffVPny5eVyuXThwgVNnTpVq1evVuvWrVWyZElJUvfu3YVr4+6779bKlSs1Y8YMbd++Xa1bt1bLli118OBBSdKZM2fUtGlTvfLKKxnO55577kn3HWL8+PEZ3v/MmTPp6sq/7du3T02bNlWNGjX0zTffaNu2bXrqqafk4+NzxXmWLVtW48aNs95TLVq0UKdOnbRz504dOnRIhw4d0muvvaYdO3bo448/1rJlyzRo0KB083jjjTc0ZswYPf7449q5c6dWrVpl1Y8r2bRpU7rnvnLlSkkXt+OsLve/OHXqlJo0aSJPT08tXbpUu3bt0uuvv64iRYpc8TGZrfesvt6S9Oabb8rhcGR4n0GDBumGG26QdHFb++GHH7Ry5UolJydb3z/SjBw5UgsXLtRnn32mdevW6dChQ+ratav174mJiYqOjlb58uW1YcMGrV+/XoGBgWrTpo2Sk5Mvu/ysrqPo6Oh0r+Hs2bMzfF6Zrcdq1arpnXfe0fbt27V+/XpVrFhRrVu31rFjx6z79OnTRzt37tTKlSu1aNEixcTEaPDgwRkuV7r0+/r69eslKVe2tbTvsWmv4T9l9z1/ORm9Z3NqOZkt4/vvv1d0dLRat26tjRs3atOmTRo+fLjVm2THlV4bSTp79qyio6P1xBNPZHu+2Xk+/3U5mW3T48eP18SJE/X+++9rw4YN8vf3V5s2bXT+/HnrPv91m84yU0A888wzpm7dulf8d0nm+eefN5LM/PnzjSTz5ZdfGpfLZaKioowxxnTq1MlERkYap9NpJJlTp06Z0aNHm7p16xpJpnv37kaSefnll42Pj4+Jj4838fHxRpKRZDw8PIzb7TanT582np6epmHDhqZPnz4mISHBlCtXzkgyZcqUMf7+/mbHjh2mQoUKZsKECcbhcJj+/fsbY4xxOBzWvBYtWmSMMSYkJMRIMlWrVjUjR440kozL5TKfffaZMcaky+ByuYzb7TabNm0yksyff/5prYMaNWoYSSYsLMy43W4zb94843A4jK+vrylfvny69dWgQQPz6KOPmqpVq5qVK1caT09PU6RIEZOQkGDKlCljHA6Hadq0qXnwwQeNMcacPn3aSDL+/v7G7XYbY4yJjIw0Dz74oClSpIj58MMPjTHGNGzY0HoO69atS/fYkiVLpnu9JJnvv//ebN261Ugye/fuNX/99ZcpU6aM2bFjh3WftHnt2rXLSDL33HOPtS28+eabRpLx9PQ0ycnJ6eZdu3ZtY4yx5u9yucyFCxesnCVLljT333+/tbyyZcsaSeb66683xhizfPly43Q6TVxcnJk2bZoJDg42p06dSpcp7fl5eHiYChUqWM9v3rx5xsvLy+zZs8dadqdOnYzL5TIffvih2b17t/X809SrV++SecfFxRlJJjAw0AwePNhIMrVq1TJPPvlkutczKCgo3ePSHlO9enWTlJRkihUrZurUqWP69u1rPSZt3pKs7VOSeeONN0yZMmXM4cOHL/n3JUuWWNvw4MGDTZkyZcyQIUOs++3YscMYY8zSpUuNJBMSEmJCQkLMjh07TPHixY0kU7x4cWsbMsaYgIAA43A4rPfvv1/3tHU1YMCAdNtPRESEkWT27NmTYfauXbuahIQEU7VqVSPJ+Pj4mJtvvtkYY8z48eONJNOxY0fz+++/p1v2fffdZz0uMjLSOBwO4+XllW7eTqfTzJ492xhjTHJyspFkSpQoYerWrWsmT55sJJng4GBTrlw5Y4wx77//vvVZlaZdu3ZGkunQocMl749Vq1YZY4z54osv0k1LSEgwFStWtKbdfvvtZvLkySY0NDTdutu2bZt1+/HHHzdr165Nd/ufoqKijJ+fn/XZFR0dbSRZn59ut9t4e3sbp9NpSpYsaaZMmXLJa5X2Xi9Tpoy1jbrdbmsbStsW27ZtaySZWbNmXfKcP/nkE2t9BgQEmKpVq16yzQYGBpp33nnHek2LFi1qfVZltC3UqVPHep1/+umndM8/7T6///67KVOmjNm+fbtVK/bu3WuM+b/3eokSJdJtw8g9Z8+eTVcv0zRo0MCMGTMm3bQrvbbG/F+9+q/SvlP8U8+ePdN9pv5X/6yh/5ZWS9Lq28mTJ42vr6/12fBfPfjgg+a666674nb87+X+V4899php2rTpf3785dZ7moxeb2OM+emnn9J9BlxuPu+++66JjIw0q1evtr4Xpjl69Ogltd7T09P6bmaMuaSWX+67WdrncFqt+resrKP+/fubTp06ZXifjGS0HtOkfb6mbVtp37k2bdpk3Wfp0qXG4XCYgwcPXnE+mX1f/7er2dbSavTKlSsveY9f7Xs+I/98z+bWcv793frf3/v+i6y+NmnfFf75frhal/ucu5rl/HubdrvdpmTJkubVV1+1pp0+fdp4e3tb39P+6zadHQVqj/eePXtUunRpVa5cWX369LnkcK53331XklS1alVJUmxsrFJTU7V582aFhobqq6++kp+fn7WHTZIuXLggl8slSfr9999VvXp1xcTE6Pz589q4caMmTZpkjV77+Phoz5492rx5s5KTk/Xbb7+pbdu2GjZsmCpWrCjp4iFLN954o2rXri23260vvvhCxhh17dpVR48elTFGDodDqamp6tu3r2rWrKmzZ89Kkvz9/bVlyxaVLVtWKSkpatSokVJSUqxDYaWLe8HKlCmj7t27y8vLS2+88YaSkpL07bff6ueff5YktWrVSg6HQwcOHJD5/1eTO3TokLy9veXl5aWyZctq586d2rZtm9q3b68WLVooOTlZCQkJCgoK0sGDB2WM0fHjx631lDaqlpSUpGLFiqlOnTrat2+fPvroI506dUovv/yyHnjgAW3YsMHaW9alSxdFRETogQcekHRxL2P37t2tPZsBAQH65ptvNG3aNFWqVEllypTRnXfeqUcffVS1a9dO99oWLVpU33//vUJCQlS6dGlrWxg3bpwkWYd1pwkODtbvv/+uUqVKWXvJUlJStHbtWh05ckQbNmzQiRMn9NFHHykuLk733XefNSJWt25daw+p2+3WzJkzrfkePnzY+v+xY8eqePHiatasmYKDg3Xw4EFr+3zttdesw8rTlr1x40a53W7NmzdPERER8vT01BtvvCFJ2rx5c7rD5YoWLaqkpCS9//778vX11YULF6zteteuXSpevLgaN26sEiVKqHHjxoqPj5ck3XvvvSpRooSkiyODBw8eVGhoqI4dO6ZffvlFlSpVUps2bVSsWDHVqlXL2nv9zxH7N954Q5MmTbL2JqWtE+niqGvaqPIXX3yhRx991Nr2JVnvlZYtW8rpdCoxMVFVq1ZVqVKllJCQIEkqUqSITp06JbfbrXfffVeJiYny8PCw9kz++3UvWrSoJCkuLs56ztLFvbFFihRR+fLlL8metofD6XTqjz/+0F133aV27dpJuvier1ChgqSL7wtJ2rt3rzp16pRu2XXq1NGwYcPUrl07bdq0ScYY+fn5pRtV9/Pz09q1a+V2u61t8ezZs9q2bZu1xzsuLk7XX3+9unfvrkcffVSSNHnyZJ08eVKHDx/W8uXLJUmpqamSLr7H0uZdt25dnTx5Uq+++qqki9elHDFihIoVKyZjjLy8vCRdPBrm7Nmz6UZ1PTw80h0F8++R8cmTJyssLEy33HKLPvzwQ/3222/WTweMMdq1a5ck6fbbb7fW0YULF+R2u/XYY4+lW8dpr1Xa63/w4EFrG007kiAwMFBff/21Tpw4oUaNGsnLy8s6rDHtOUsXj4aoX7++Ro4cqcTERFWsWNH6SVGxYsWsvdArVqxQ+/btJUmnT5/W5MmTVadOHY0ePTrdtpC2/TgcDjVp0kT33XefJKlXr1764IMPJF089EySqlevroEDB1qvk9vtVlBQkMqVKydJ1uHnlStXznQvGnJGSkqKUlNTL9mL7Ovrm25PTVbMmjVLYWFh1naSVnv/C7fbrcWLF6tatWpq06aNihcvroYNG17xcObLSU1N1Zw5c3TmzBk1atTosveJi4tTUFCQVUtWrlwpt9utgwcPqmbNmipbtqx69OihAwcOZHm5SUlJmjlzpgYOHHjF7fjfy/2vvv76a910001W7a9fv771vstNZ8+eVe/evTVp0iTrKJV/27Vrl55//nlNnz79snsO0w5/TvsMSfvul3b0mSTVqFFD5cuX1/fffy/p4mdIaGiopk6dqqSkJJ07d05Tp05VzZo109XKf8rqOvrmm29UvHhxVa9eXUOHDtWJEyeytU4ykpSUpClTpig4ODhdvQ8JCbGOEpL+r7b/+5D0f8vs+/o/Xc22NmzYMLVv3z7da/JPOfmel678ns3J5fx7GUePHtWGDRvSfe+LjIzM9udfmuy8NjkhK59zOeH333/X33//nW5bCA4OVsOGDa3359Vs01mWI+17HrBkyRIzb948s3XrVrNs2TLTqFEjU758eRMfH2+MuTjyERoaaurVq2f8/f2NJOPn52eGDx9uli1bZkaMGGH8/PxM6dKlTWBgoJFkdu3aZRYtWmTt6ahWrZrZuHGjCQgISLcnx9PT00gyw4YNMw6Hw3h4eBhJ5qWXXjKzZ882derUMdWrVzf+/v7G29vb2oOV9vfss88aY/5vL2DaXhT9/z2hDofDVKxY0TidTlOtWjUzceJE43A4rGWVLFnSlC5d2kgyrVu3ttZB3bp1rcfr/+9Fl2SmTp1qjh07ZkqUKGEtJ+0+af91Op2mbNmy5ty5c9ZosMPhMGFhYWbUqFHW4zp37mwSExNNmzZtjCTTvHlz8/nnnxtvb29rmZMmTTIzZ840xYoVs9ZXuXLljK+vr7U8Scbb29uMHj3abNmyJd36qV69utm7d6956aWXTKtWrYzb7Tbnzp2zXsMmTZoYY4x58cUXTbVq1axtYePGjaZSpUrWXsx/bwtpGdP2ZKf9pa0nT09PU7t2bbN582bz4IMPXpJz9erVxsfHx3h4eJj+/fuboKAg07VrV+t+ac9l8uTJxuFwmCpVqpitW7eauXPnGi8vLxMUFJRuT2XaMtMeV6FCBeNyuczHH39shg4dah2xULZsWePr62s9JiAgwCxevNjMnj3bmla0aFHz0UcfmS1btpgePXpYr5/D4TAul8u639SpU03Tpk1NzZo1rdc97fmn/f17T3LaHs7evXunm48xxtxzzz2mdevW1p5ct9ttnn32Wes16N69uzl58qS5cOGC9b755/bpcDisPdX/XCeLFy82mzdvtqYVKVLEet2///57a/59+/ZNt27mzJlz2eyvvPKKdTs8PDzd616mTBnz4IMPmj179hg/P7907/F//t11113p9pBKMg0bNjQ//fSTdTvts8blclm50ra7f77P0z4/0rKk/f3zPmmfXWnrKzIy0ppX2l9YWJgZMWKEue6660z37t3Tvb/+Oa/g4OB06z7tff3PPd4vvfSS2bJlixk3bpzx8PAw1apVM926dUu3vGbNmplt27YZf39/a/1Vr1493eegy+UyoaGhJjEx0QwfPvySbXTatGnW+o+IiDBt27Y1O3bssD73/vmcJZn777/fvPfee9Zrc7l12bJlS1OnTh3rc6J8+fKmT58+ZubMmaZMmTJGkqlbt266ebtcLuPt7W3uu+8+I8k8+eSTxuVyGS8vL2u+3bt3NzVq1Ei37AceeMDaxubOnWscDke6oxOQ+xo1amQiIyPNwYMHTUpKipkxY4ZVL/8poz2gkydPNsuWLTPbtm2ztpMuXbpkOYOUfu9KWt308/Mzb7zxhvnpp5/Myy+/bBwOh/nmm28ynNc/31PBwcFm8eLFl73fsWPHTPny5c0TTzxhTXv55ZeNp6enqV69ulm2bJn5/vvvTVRUlKlevbp1RFdm5s6dazw8PK64h+dyy/2vvL2909X+yZMnGx8fH/Pxxx9n6fH/Xu//lNHrPXjwYDNo0KArzuf8+fPmhhtuMDNmzDDGXLrnLTU11bRv396qQ8YYM2vWLOPl5XXJsm6++WYzatQo6/b27dvNddddZ5xOp3E6naZ69ermjz/+uOJzzMo6mj17tvnqq6/Mtm3bzJdffmlq1qxpbr75ZpOSknLF+f7TldbjwoULrc/J0qVLm40bN1r/lvad69+KFStm3n333SsuK7Pv6/90Ndta2vfvc+fOGWMu3fN8te/5f8roPZtTy7nSMtK+A/3ze9+IESOMl5eX+fXXX7O1jKy+Njmxxzsrn3M5ucf722+/NZLMoUOH0t2ve/fupkePHsaY/75NZytXjswlDzp16pQJCgoyH374ofnqq6+sL5f79u2zDu/t2rWrCQsLMzt37jTVq1c3w4cPtw4nSvtL+wKZ1qykfQGVZB5++GHTsWNH64tf6dKlzezZs83LL79sbUhBQUFm+vTp1mNLly5thg4dalatWmVCQ0NNhQoVjMPhMLNnz7aan1KlSpnp06ebG2+8MV2j0adPH1OjRg3ToEED43A4zPr1680PP/xgzSMwMND6cD979qxp0KCB8fT0NKNGjTIBAQHWgMG0adPMLbfcYho2bGg9Rx8fH/P111+brVu3Wof8VqpUyRhjzMGDB638W7duNcb8X+OSNr1UqVImKCjIDBkyxFy4cMHs2bPHvPfee1b2nTt3mokTJ1qNYtrr8OOPP1oDHfXq1bNeP+ni4aKhoaGmQ4cOpkaNGqZEiRLm4MGDJikpyXTo0MFqbA4cOGCMSf+GSUpKMtHR0cbPz8+4XC7j4+NjHcIiXTyk9NdffzXr1q2z5iVdPJz3k08+sW6nHX6Sdri0w+EwjRo1snIuX77cOoxbkvWTgqJFi6bbHqtUqWJCQ0NNXFycueWWW0xUVJQJCgoyY8eOtb70O51O43K5zM6dO40xF4t1gwYNzC233GKCg4NNo0aNjCQzadIks3PnTjNv3jzTsWNHExwcbEJDQ82rr75q5Rg9erS17M6dO1vN4+zZs63D2pxOp2nRooVxOp3pDgv++uuvzcSJE01ERITVlHz11VfW++jTTz81e/bsSdfs/LPxvvXWW40kc9NNNxljjNV4N2zY0PrZRtpjr7vuOhMeHm5WrFhhHP+vvXsPiuo8/wD+nL2cxUVggQW5hZtLRDGAQSRoAw6k6ijGWKNoTEi1XuIlmBhISdtETcAybUOqicZ6CU6rTp022hBMBA3RGOpodMDomCoKxHZqymiqQKBc5Pv7Y/u+vz2wXIXYps9n5swou3vO7ns57/ue8z7vURQYjUZMmDABH374IZYuXYoRI0ZAVVVYLBaMGjVKHi8oKEjmuziZPvTQQ7h16xYuX74sjzFixAg0NzdrvjsABAcHy4G3Xq/Hpk2bZMiBoiiYNm0aRo4cibS0NFkn9+zZozk3GI1GnDt3Du+8847825IlS2QZE/manJyMyspKeVHG29tb1jXH/en1emRlZUFVVc1AU0wPT0hIQFVVlWxkrVYrPvroIxQWFso66+HhAavVinPnzmHp0qUyHebOnYuzZ8/C09NT890yMjJkp2rRokWagbdosM6cOQNXV1dYLBYEBgZi3bp10Ol0cHNzg06nQ0FBAaqqqrBgwQJ5vF/+8pfyQqLj73vyySe7lFGRf35+fiAi/PGPfwRgnzqnqqqsFyJvxHQ68ftCQkJkfRD5RWQPKRJ5ERUVJTtcr732GogI+fn5mvQUZdaxs75kyRLExsaitLRUptnFixdx+fJlbNmyRZYN0bGbMmUK/P39ZSPOvh1XrlyRF+z0ej3i4+Nle+mot6nHjkRfQIQR9KZzJ0+0mwsWLNC8b+bMmZg/f36P+xJt6JkzZ5CTkyP7KY5EWzJt2jS0trbKv+fl5YGIUFJSIv9WV1cHnU6Hw4cP9+m3TJkyBWlpaU5f6+64A2U0GjVtKgA8++yzeOihh/r0+YEMvN977z3YbDY0NDR0u5/nn38e6enp8v+dBwDPPPMMQkJCZDsE9G3g3dTUhAkTJiAjIwOnT5/GyZMnMWfOHERFRaGpqcnp7xhIGl29ehVE1OeQg+7SsbGxUZ4nFy9ejNDQUPzjH/8AMHiDFMf+uqO7KWvXrl2Dr6+v7LMCvU/57m+dd9SXOnu3x+nuGKINdez3AcADDzzQJWSsv7rLm8EYePclzXjg/V9m/PjxyMnJQXR0tGYQLe7E6HQ6WCwWOfCqrKxEY2Oj7IQ9/fTT8Pf3R3V1NebNm4dx48ZBp9Ph9OnTICKcPn0aBQUFct/iLoeoVI8//rh8zbED6rjpdDo58B03bhyI7DHkALBq1SoEBgbCYrHAZrNh3rx5mDBhgtxfXV0dHnvsMXkn1NXVFQUFBQCAnTt3wtfXF+PHj0daWpoc2ImOYmpqqow9IrLfKRcmTZqk6Sx3vgPa+W/iqq2Hhwd+8YtfyP2ItIyNjcWyZctkx3zp0qWafEpOTgYRaWI1xe9xd3eXd0dF/nVOTxE7vWvXLlgsFrS2tmLGjBkwm814+OGHodPpMHLkSHkCIiKEhobKY4m4UyL7Xczq6mr5f3GX2PF4w4cP13z/rVu3wsPDA0ajUQ4eRPy4YLPZYDabkZiYiNTUVDQ3N2P8+PFYsWKFZjAZEBCAZcuWAbAPDufMmQNPT095YcdZ42iz2TBy5EikpqbKfYmr9KtWrYK/vz+I/v9CikiD4OBgmEwm+Pj4oLGxUQ5uHDtnFosFiqIgMjJS3vXvfHdRDKAB4OWXX5bfU5QV8X5FURASEoJbt25hw4YNmjR23KfVagVgX3PB2bGISNMpFPn1xBNPAABqa2tBZL/zqtfrsW/fPs13d7ZPZ+VcURQcPXpU1snOMd7icz3Vb0VR5B1RMRBNSEjQ5IP4/WvXrpUXRebMmQMvLy95XnJWP/z8/LBx40YAkBe1xB32zt9JlGOxz4yMDDQ0NKCxsRE6nU527JwNvMU6FJ33K9JS5NcjjzzSbb0xGo2y4excRkUH0c/PDyaTSXawkpKSkJmZia+++goNDQ3yc2IgIy54TJ48WVMfHMubY56K/y9fvtxpeyDqtmNnfevWrQgICNB8b/GZzmW2trYWOp0OY8aM0dwFZ9+exsZG2amaN28epk+frnm9PwNv0X71dbDa+dzc0tICg8GA1157TfO+F198ERMnTuzTPoXU1FTZLgD2dV0c2xJHol44DggBwNfXF9u3b+/1WKIc/+lPf+ryWk/HHajg4GDNnWcAmnrXG2dtotBdfq9Zs0ZT/x37hMnJyQCAmJgYOQPMsb6LCztBQUGorq7W7NdZHLj4jZ37Znfu3JGvt7S0wGw2ywv9nQ00jaxWK7Zt29bje4Se0tGRzWaT7Y7oczlqa2uDXq+XFz77SvTXhbstawcPHtScr0Uei3x3NhOgv3W+J53r7FAcRxxD9IFEmyrMmzdP9ovuRue8AYYmxttZmg3mwFv0NTqfD0RfAxjcMt2d71SMt6PGxka6cuUKlZeXU11dHRHZVzSurKyUsbJvvPEGjR49mi5evEhxcXEUExOjiaMtLS2l48ePk8VioZKSEhl78O6779J9991HDz74IG3atEnGQIkYzLi4OPmImqCgIHJxcaGMjAwiIgoLC6O0tDQ6cOAABQQEUGZmpowdevvtt4mIqLa2llavXk0HDx6kjz/+WMavlZSUyLgDo9FIM2bMoKqqKkpLSyO9Xq+JjxDxI1evXqUvvviCIiIiZCylXq+noqIiGjVqlIxtcozXra+vJ09PTxo+fLhML7PZTIGBgXTgwAGqrKykwsJCIiIKCAig5cuXk8Viodu3b9Ojjz4q9yPS0mAw0PHjx+UqqZ2J+EmRT8I333xDwcHBBID0ej2tW7eOkpKS5GPdiOxx4iIGNjExkW7dukUpKSlUVlZGsbGxlJWVRR0dHXTz5k25yjWRfUVqcTzH+NGxY8dSaGgo+fn50ahRo8jX15d8fHyouLiYjEYjeXt7y9WahUuXLhEAam9vl3G8jo++qK+vp+rqauro6CBVVamoqIja29vp6tWrMk8MBgOFhobSN998Qy0tLXTp0iW6du0a6fV6amxsJFVV6dixY07Tr6Ojg+7cuSNXRLVarfSXv/xFliHxuc6xevX19dTW1kYZGRnk6uoqHxnhmE6tra2kKAq1tbVRTk4OEdnj2cSjRYTY2FiZByIuOiMjgyorK+Vq6ElJSVRSUkIeHh40ZswY+Z22bdtGy5cvl9/vhRdeICKizZs3U3l5Obm6umqeHKDX62nmzJny2C0tLUREMp5ZlM3m5mZSFIVaWlrkdy8oKJCPjhFeffVVzXlB+fcjoioqKmT+iBXMHQUEBFBlZSWVlZXR/fffT4qi0JgxY+jAgQPyPQBknRQrhjuugCt0dHRQSEgIHTx4kIjs9Vuv11NcXJyM0+u8qjoA+dvFkwDa2tooKiqKrFarTEcvLy9Z/8U6Fzdu3KDhw4fT/v37SVVVamtr67J/4amnnqLVq1cTkX319WnTplFAQABlZ2eTm5ubXBNj586d8vFkISEhMu5Rp9PJRzuWlZURkT2uSjyGRzxVoK6ujmJiYshoNFJ9fT2dOnWKEhMTacSIEXJdCCKSMfvt7e1ERJr4s7a2Nuro6JBlROTpyJEjaeHChVRZWSlj+bOzszX5rqoqubu7a3775cuXZby/sGPHDvk58Ti3tWvXUmFhIfn4+NCVK1eGNE6Ndc/V1ZX8/f3pn//8J5WUlHRZl6E/RLlwPB/2h6qqFB8f3+VxU87KVG86OjpkXa+vr6cpU6bItqRzbLt4YoPjcb/++mu6ceNGn45bWFhIvr6+cn0EobfjDtSkSZMGJY36Iycnhz7//HNZjx37hKL9ePfdd+ncuXPy9Z07dxIR0WOPPUZ//etfqaysTJ67BNH3++ijj+TfRFvu2DcTj2UUxP8d1xdyNJA0+tvf/tal3zMYHMui6HOdPXtWvl5WVkYdHR3y6UF90djYSFevXpXfdTDKWmpqKp0/f16Tx+PHj5ftgFi/xtHd1nlHjuk0VMcRxxCPTB2KetQ5b4ZST2k2GMLCwsjPz09TPx37GkSDV6Z7NCjD9/8AL7zwAo4dO4aamhqUl5fjkUcegclkgqurK3bs2AEiwve+9z28/vrr8qqkiEU1Go3Izc3F7t27ERgYKOP3XnnlFeTn58NqtSIyMhKHDx+WcZqZmZmaKcliWmtxcTFqamowdepU6HQ6xMfHQ1VVxMfHIzExEW5ubkhOTsaJEyfg5eUl4wezsrJw7do1eHl5ySm3S5YsQXx8PIgIAQEBiI+Px9///nd4eXnBbDZDp9Nhw4YN8vt6eXnhyJEjqKmpwb59+6AoiowBdXNzk//OzMzEkSNH8Nlnn2HDhg3ySu7cuXOxcOFC+X8R23vlyhX4+PhAURRs3LgR27Ztk3fWpk2bJuOlJ06ciB/+8If4/e9/j9zcXLi7u8s4chcXF/j7+yM4OBhGoxFZWVk4fPgwli1bprnj9P3vf1+uaKwoCpYtW4bU1FR4enpi6tSpMJvNMJvN+NWvfiXvdn3wwQe4dOkSWltbYbVaQUTw8fFBbm4ufH194e3tDU9PTxw5cgTvv/++/D7h4eH40Y9+hPDwcHn8goICnDhxQt6dV1UVe/bswXPPPQciQnp6OgwGA/Ly8lBVVYUnnnhCToU1Go1wc3OT+5s/fz6Ki4vl9GofHx8cO3YMRUVFSExMhMlkwk9/+lMQ2adJu7i4gMg+3T02NhY2m02mzZo1a+R3j4mJQW5uLnbt2iXvCiuKIqeUT506FXq9Hi4uLvK7i7uPTz31lKwPYtu7dy/2798vy9HMmTORl5cnV4QmssfVlpSUgMg+w4CIkJ2dLV93cXFBfn4+3nzzTbmflStXIicnR07FN5vNeOmll7BlyxZYrVaoqoqUlBRUV1fD19dX3kENDAzEtm3bcOjQITmDQOQFEeHhhx+Gn58f9u7di08//RSJiYkIDAyEqqpYvXq1vKurKApcXV2xb98+GXe9ceNGGAwGma5RUVGIjIzEjh07sHv3bnmsmJgYfPbZZ7h27Zq86y/KpdiSk5PxwQcf4Msvv5RPStDr9TJPxf937dqFTz75RJZZ+vcdbTElVGxJSUlQVRUmk0lelU9JSZG/e/r06di8eTPWrl0rf192djY2b96M6OhoWW9VVUVeXp6c6aCqKoxGI373u9/JcAWDwYAf/OAH8mkFRITnnntOTt8mss8q2LRpE3Jzc+Vq5UajEa+//jq8vb3l+gnTp09HTU0NPv/8c1nfjUYjcnJyNL933bp1sizMmjVLlgcRBkBEWL58OUpKSjBjxgwMGzYMmzdvxvbt2zXnhLfeegu//vWvYbVaZax6cnIy1q9fL/OeiPD222+jqKgIRPa4/enTp+OVV16R6ZKZmYlDhw7JdTzEXZGMjAxNfqxYsUKuQG82m7F79278+c9/Rnl5uZwlUlhYCH9/f9hsNoSFhQ3a3UDWN4cPH8aHH36I6upqlJaWIiYmBgkJCXL2xM2bN1FRUYFDhw6ByL72Q0VFBa5fvw7A3sa9+uqrOHPmDGpqavDee+8hPDwcSUlJPR63oaEBFRUV8vwiYrm//PJLAMCBAwdgNBqxfft2VFVV4c0334Rer8eJEye63WdOTg6OHz8u61ROTg4URUFpaSlu376NhIQEPPDAA7hy5QquX78uN8c7eLNmzUJUVBTKy8tx/vx5pKWlYcyYMb1O171z5w6Cg4Px4x//WPP3vh53IE6fPq1pU/fu3Quz2Yw9e/Z0+5ne0r23/HaGernjK+68ubu749ixY5o0cJwi/swzzyA4OBhlZWU4c+YMEhMTNdPEv/jiC5hMJqxYsQIXL17EhQsX8OSTT8LDw6PLFNi+plFDQwOysrJw8uRJ1NTU4OjRo3jwwQcRERGBf/3rXwNKx8bGRrz00ks4efIkamtrcebMGSxatAgmk0k+nQQApk2bhnHjxuHUqVP49NNPERER0SW8ojNn/XWr1Yq6urohLWuOU80HWued6anODtZxejoGYJ+Z5u7ujj/84Q+oqqrCz372M7i4uPR7OntPeQPY166oqKiQ/chPPvkEFRUVuHnz5qD+noEep7dzQ35+PiwWi1wPYdasWV3a7IGU6f74zgy809PT4e/vD1VVERgYiPT0dE2n1nETg5DRo0fLabEmk0kT/+hsMxgMGD16NDw8PDTTKKOjo/Hxxx9jzZo1CA4OhouLC8LCwhAXFyenKs2ePRvXr1/vsqBYf7YtW7YAgKaj2nnz9vaWaZCcnKwZuHS3rVy5UrNIk8FgwNy5c+VjRK5duwYPDw+5UJt4BJnjNNsVK1bIjqrYj2NsZk9pGhcXh+LiYhlz3nkLCwuTU3672yZNmuR0KvBANrHYSXevr1mzBmPHjpULnvS0H1G2+npskaY6nQ42m63X94v457v9veHh4YiIiOhSNocNG6YZjA3WJh5h5+npKRcuE4sdhoSEdDvF3Nnvnz17NioqKrosktfTJi4evPPOO3JhwIFu4lFq999/v9NF2ES+RkdH44033tAszNU5H4xGIywWS59/v9jMZrMm1KA/m7u7u7zA52wT60v0lJaqqsLHx0eGjdxteZw8ebIcIDvbPDw8sGHDBsycObNLiEBAQIBmvYO+lEUikosQ9mVTFAVBQUFycUdRBxMTE3Hp0qV71Qz+z9q/fz/Cw8Ohqir8/PywatUq3Lp1S74uFvDrvK1btw6AvY1LSkqCl5cXTCYTbDYbsrOzcfv27R6P6xia4eycANinLtpsNri4uCAmJsbpFG5Hixcv1tSp1NRU2Rnt7nhE9sfcCbdv38bixYthsVjg5eWF2bNnax5f1R1xcbVzGe7rcQfq/fffl21qZGRkr1Pie0v33vLbGaK+DbydbYWFhfJ9zc3NWLlyJTw9PWE2m2Xfz1FpaSkmTZoEDw8PeHp6IiUlRfPo0P6mUVNTE6ZMmQIfHx8YjUaEhIRg6dKl+Oqrr3rcZ0/p2NzcjNmzZyMgIACqqsLf3x+PPvqoZnE1wH6RY8GCBRg+fDjc3d2xaNEiTey8M87662KAOJRlzXHgPdA670xPdXawjtPTMYSf//znsl1KTEzs8QJfd3rKG8D+uLHe6sBg/J6BHqe3c0NHRwdefvlljBgxAiaTCampqV3OdwMp0/2hAP9+nhRjjDHGGGOMMcYG3Xc2xpsxxhhjjDHGGPtPwANvxhhjjDHGGGNsCPHAmzHGGGOMMcYYG0I88GaMMcYYY4wxxoYQD7wZY4wxxhhjjLEhxANvxhhjjDHGGGNsCPHAmzHGGGOMMcYYG0I88GaMMcYYY4wxxoYQD7wZY4wxxhhjjLEhxANvxhhjjDHGGGNsCPHAm7HvoOPHj1NkZCTFxsZqtujoaHr22WeJiCghIaHL67GxsWSz2ailpcXpftevX0+KopCiKGQwGCg0NJSef/55amxsJCKi2tpa+bqiKOTl5UXJycl04sSJfu2HMcYY+1/FbThj302Ge/0FGGODr7m5mebPn0/r16/X/L22tpZycnKIiEhRFKqsrOzy2cmTJxOAbvcdFRVFR48epfb2diovL6fFixdTU1MT/eY3v5HvOXr0KEVFRdGNGzcoLy+P0tLS6PLlyzRixIh+7Ycxxhj7X8NtOGPfTXzHmzHWLwaDgfz8/CgoKIjS09Np4cKFVFRUpHmPt7c3+fn50dixY+knP/kJ1dfX06lTp/q9H8YYY4wNHm7DGbt3eODNGLsrw4YNo9bWVqevNTc3029/+1siIlJVdcD7YYwxxtjg4zacsW8PTzVnjA3Y2bNnad++fZSSkqL5+8SJE0mn01FTUxMBoLi4OEpNTe33fhhjjDE2NLgNZ+zbxQNvxli/nD9/noYPH0537tyh1tZWmjFjBr311lua9+zfv58iIyPpwoUL9OKLL9Lu3bvJaDT2ez+MMcYYGzzchjN27/DAmzHWL6NGjaKioiIyGAwUEBDgdPrZfffdRxERERQREUHt7e00e/ZsunDhAplMpn7thzHGGGODh9twxu4djvFmjPWLqqpks9koNDS0Tw3t448/TgaDgbZu3XpX+2GMMcbY3eE2nLF7hwfejLEhpSgKZWZmUn5+PjU1Nd3rr8MYY4yxPuI2nLHBwwNvxtiQe/rpp6mtrY3jvxhjjLH/MtyGMzY4OMabMdZn69evp/Xr13f7emhoKAHo8nez2Uxff/11n/fDGGOMscHFbThj9xbf8WaMMcYYY4wxxoYQ3/Fm7DvIw8ODiouLqbi4uMtrU6dOJSIii8VC48ePd/p5nY6vyTHGGGP3ArfhjH03KXA2p4QxxhhjjDHGGGODgi+JMcYYY4wxxhhjQ4gH3owxxhhjjDHG2BDigTdjjDHGGGOMMTaEeODNGGOMMcYYY4wNIR54M8YYY4wxxhhjQ4gH3owxxhhjjDHG2BDigTdjjDHGGGOMMTaEeODNGGOMMcYYY4wNof8D6d6oo7L+K5gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 데이터 불러오기\n",
    "columns = [\"Vendor name\", \"Model name\", \"MYCT\", \"MMIN\", \"MMAX\", \"CACH\", \"CHMIN\", \"CHMAX\", \"PRP\", \"ERP\"]\n",
    "data = pd.read_csv('machine.data.csv', names=columns)\n",
    "\n",
    "# 데이터 탐색\n",
    "print(data.head())\n",
    "print(data.describe())\n",
    "print(data.info())\n",
    "\n",
    "# 필요한 특성 선택 (모델링을 위해 문자형 변수 제거)\n",
    "features = [\"MYCT\", \"MMIN\", \"MMAX\", \"CACH\", \"CHMIN\", \"CHMAX\"]\n",
    "target = \"PRP\"\n",
    "\n",
    "# 특성과 타겟 분리\n",
    "X = data[features]\n",
    "y = data[target]\n",
    "\n",
    "# 결측치 확인 및 제거\n",
    "print(X.isnull().sum())\n",
    "print(y.isnull().sum())\n",
    "\n",
    "X = X.dropna()\n",
    "y = y.dropna()\n",
    "\n",
    "# 숫자가 아닌 값 확인 및 제거\n",
    "for col in features:\n",
    "    print(f\"{col} unique values: {X[col].unique()}\")\n",
    "\n",
    "# 숫자가 아닌 값이 있는 경우 제거 또는 변환\n",
    "X = X.apply(pd.to_numeric, errors='coerce')\n",
    "X = X.dropna()\n",
    "\n",
    "# 스케일링 전 데이터 형식 확인\n",
    "print(X.dtypes)\n",
    "\n",
    "# 특성 스케일링 (StandardScaler 사용)\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# y의 인덱스를 X의 인덱스와 일치시키기\n",
    "y = y[X.index]\n",
    "\n",
    "# 데이터 분리 (80% 훈련, 20% 테스트)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 모델 초기화\n",
    "model = LinearRegression()\n",
    "\n",
    "# 교차 검증\n",
    "cv_scores = cross_val_score(model, X_train, y_train, cv=10, scoring='r2')\n",
    "print(f'교차 검증 R^2 점수: {cv_scores}')\n",
    "print(f'평균 교차 검증 R^2 점수: {cv_scores.mean()}')\n",
    "\n",
    "# 모델 훈련\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 예측\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_test = model.predict(X_test)\n",
    "\n",
    "# 결과 시각화\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# 실제 vs 예측 (훈련 데이터)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(y_train, y_pred_train, alpha=0.7)\n",
    "plt.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', lw=2)\n",
    "plt.xlabel('실제 PRP')\n",
    "plt.ylabel('예측 PRP')\n",
    "plt.title('훈련 데이터')\n",
    "\n",
    "# 실제 vs 예측 (테스트 데이터)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(y_test, y_pred_test, alpha=0.7)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('실제 PRP')\n",
    "plt.ylabel('예측 PRP')\n",
    "plt.title('테스트 데이터')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e678737b-c709-493f-900f-55fdb846057c",
   "metadata": {},
   "source": [
    "2. (1) 단계에서 얻은 다중 선형 회귀 모델을 사용하여 학습 데이터와 테스트 데이터 각각대해 이 모델의 성능을 평가하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48862eb4-e4eb-410a-aa3f-b55698b6c707",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "훈련 데이터 MSE: 3117.6589054301776\n",
      "테스트 데이터 MSE: 5633.068155793271\n",
      "훈련 데이터 MAE: 35.12200634651681\n",
      "테스트 데이터 MAE: 48.06880134700474\n",
      "훈련 데이터 R^2: 0.8378284557995717\n",
      "테스트 데이터 R^2: 0.8893455830696771\n"
     ]
    }
   ],
   "source": [
    "train_mse = mean_squared_error(y_train, y_pred_train)\n",
    "test_mse = mean_squared_error(y_test, y_pred_test)\n",
    "train_mae = mean_absolute_error(y_train, y_pred_train)\n",
    "test_mae = mean_absolute_error(y_test, y_pred_test)\n",
    "train_r2 = r2_score(y_train, y_pred_train)\n",
    "test_r2 = r2_score(y_test, y_pred_test)\n",
    "\n",
    "print(f'훈련 데이터 MSE: {train_mse}')\n",
    "print(f'테스트 데이터 MSE: {test_mse}')\n",
    "print(f'훈련 데이터 MAE: {train_mae}')\n",
    "print(f'테스트 데이터 MAE: {test_mae}')\n",
    "print(f'훈련 데이터 R^2: {train_r2}')\n",
    "print(f'테스트 데이터 R^2: {test_r2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5b7e85-758b-42d7-af56-f77eb92d37d3",
   "metadata": {},
   "source": [
    "3. (2) 단계에서 얻은 모델의 테스트 데이터에 대해 R^2가 90% 이상이 나오도록 모델을 개선하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c346a60-5a15-4b9c-bc09-689bd8d5d2c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "테스트 데이터 R^2 점수 (Ridge Regression): 0.8885286863069447\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# Ridge Regression 모델 초기화\n",
    "model_ridge = Ridge(alpha=1.0)  # alpha는 규제 강도를 조절하는 매개변수입니다. 조정이 필요할 수 있습니다.\n",
    "\n",
    "# 모델 훈련\n",
    "model_ridge.fit(X_train, y_train)\n",
    "\n",
    "# 예측\n",
    "y_pred_train_ridge = model_ridge.predict(X_train)\n",
    "y_pred_test_ridge = model_ridge.predict(X_test)\n",
    "\n",
    "# 테스트 데이터 R^2 계산\n",
    "r2_test_ridge = r2_score(y_test, y_pred_test_ridge)\n",
    "print(f'테스트 데이터 R^2 점수 (Ridge Regression): {r2_test_ridge}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1de2f423-901f-476e-8c32-045fef27a869",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Vendor name Model name  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  ERP\n",
      "0  VendorName  ModelName  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  NaN\n",
      "1     adviser      32/60   125   256   6000   256     16    128  198  NaN\n",
      "2      amdahl     470v/7    29  8000  32000    32      8     32  269  NaN\n",
      "3      amdahl    470v/7a    29  8000  32000    32      8     32  220  NaN\n",
      "4      amdahl    470v/7b    29  8000  32000    32      8     32  172  NaN\n",
      "       ERP\n",
      "count  0.0\n",
      "mean   NaN\n",
      "std    NaN\n",
      "min    NaN\n",
      "25%    NaN\n",
      "50%    NaN\n",
      "75%    NaN\n",
      "max    NaN\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 210 entries, 0 to 209\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Vendor name  210 non-null    object \n",
      " 1   Model name   210 non-null    object \n",
      " 2   MYCT         210 non-null    object \n",
      " 3   MMIN         210 non-null    object \n",
      " 4   MMAX         210 non-null    object \n",
      " 5   CACH         210 non-null    object \n",
      " 6   CHMIN        210 non-null    object \n",
      " 7   CHMAX        210 non-null    object \n",
      " 8   PRP          210 non-null    object \n",
      " 9   ERP          0 non-null      float64\n",
      "dtypes: float64(1), object(9)\n",
      "memory usage: 16.5+ KB\n",
      "None\n",
      "MYCT     0\n",
      "MMIN     0\n",
      "MMAX     0\n",
      "CACH     0\n",
      "CHMIN    0\n",
      "CHMAX    0\n",
      "dtype: int64\n",
      "0\n",
      "MYCT unique values: ['MYCT' '125' '29' '26' '23' '400' '60' '50' '350' '200' '167' '143' '110'\n",
      " '320' '25' '56' '64' '133' '810' '700' '140' '220' '800' '75' '90' '105'\n",
      " '175' '300' '180' '330' '57' '480' '203' '115' '1100' '600' '900' '225'\n",
      " '185' '17' '1500' '100' '150' '92' '72' '40' '35' '38' '48' '30' '112'\n",
      " '84' '250' '160' '240' '52' '70' '59' '116' '124' '98']\n",
      "MMIN unique values: ['MMIN' '256' '8000' '16000' '32000' '1000' '512' '2000' '4000' '64' '524'\n",
      " '5000' '1500' '3100' '2300' '128' '1310' '2620' '5240' '500' '384' '3000'\n",
      " '768' '192' '96' '262']\n",
      "MMAX unique values: ['MMAX' '6000' '32000' '16000' '64000' '3000' '3500' '8000' '64' '2000'\n",
      " '5000' '6300' '6200' '2620' '10480' '20970' '4000' '12000' '512' '1000'\n",
      " '4500' '768' '24000' '1500']\n",
      "CACH unique values: ['CACH' '256' '32' '64' '128' '0' '4' '65' '8' '142' '131' '30' '9' '16'\n",
      " '6' '1' '2' '24' '48' '12' '160' '112' '96']\n",
      "CHMIN unique values: ['CHMIN' '16' '8' '32' '1' '4' '7' '5' '6' '12' '3' '2' '0' '24' '26' '52']\n",
      "CHMAX unique values: ['CHMAX' '128' '32' '16' '64' '2' '6' '8' '4' '15' '20' '12' '3' '5' '24'\n",
      " '1' '38' '10' '19' '48' '31' '112' '54' '0' '7' '14' '13' '28' '26' '52'\n",
      " '104' '176']\n",
      "MYCT     float64\n",
      "MMIN     float64\n",
      "MMAX     float64\n",
      "CACH     float64\n",
      "CHMIN    float64\n",
      "CHMAX    float64\n",
      "dtype: object\n",
      "모델: Linear Regression\n",
      "교차 검증 평균 R^2 점수: 0.5658490064954826\n",
      "훈련 데이터 R^2 점수: 0.8378284557995717\n",
      "테스트 데이터 R^2 점수: 0.8893455830696771\n",
      "-------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 47784 (\\N{HANGUL SYLLABLE MO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 45944 (\\N{HANGUL SYLLABLE DEL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51032 (\\N{HANGUL SYLLABLE YI}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 44208 (\\N{HANGUL SYLLABLE GYEOL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 44284 (\\N{HANGUL SYLLABLE GWA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 47784 (\\N{HANGUL SYLLABLE MO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 45944 (\\N{HANGUL SYLLABLE DEL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51032 (\\N{HANGUL SYLLABLE YI}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 44208 (\\N{HANGUL SYLLABLE GYEOL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 44284 (\\N{HANGUL SYLLABLE GWA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "모델: Ridge Regression\n",
      "교차 검증 평균 R^2 점수: 0.5730702188805401\n",
      "훈련 데이터 R^2 점수: 0.8378104131299453\n",
      "테스트 데이터 R^2 점수: 0.8885286863069447\n",
      "-------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 47784 (\\N{HANGUL SYLLABLE MO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 45944 (\\N{HANGUL SYLLABLE DEL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51032 (\\N{HANGUL SYLLABLE YI}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 44208 (\\N{HANGUL SYLLABLE GYEOL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 44284 (\\N{HANGUL SYLLABLE GWA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 47784 (\\N{HANGUL SYLLABLE MO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 45944 (\\N{HANGUL SYLLABLE DEL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51032 (\\N{HANGUL SYLLABLE YI}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 44208 (\\N{HANGUL SYLLABLE GYEOL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 44284 (\\N{HANGUL SYLLABLE GWA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "모델: Lasso Regression\n",
      "교차 검증 평균 R^2 점수: 0.5816935147501735\n",
      "훈련 데이터 R^2 점수: 0.8375648408890113\n",
      "테스트 데이터 R^2 점수: 0.8859785931305194\n",
      "-------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 47784 (\\N{HANGUL SYLLABLE MO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 45944 (\\N{HANGUL SYLLABLE DEL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51032 (\\N{HANGUL SYLLABLE YI}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 44208 (\\N{HANGUL SYLLABLE GYEOL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 44284 (\\N{HANGUL SYLLABLE GWA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 47784 (\\N{HANGUL SYLLABLE MO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 45944 (\\N{HANGUL SYLLABLE DEL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51032 (\\N{HANGUL SYLLABLE YI}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 44208 (\\N{HANGUL SYLLABLE GYEOL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 44284 (\\N{HANGUL SYLLABLE GWA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "모델: Random Forest\n",
      "교차 검증 평균 R^2 점수: 0.7946679543879636\n",
      "훈련 데이터 R^2 점수: 0.9566234133114869\n",
      "테스트 데이터 R^2 점수: 0.8657278240323845\n",
      "-------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 47784 (\\N{HANGUL SYLLABLE MO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 45944 (\\N{HANGUL SYLLABLE DEL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51032 (\\N{HANGUL SYLLABLE YI}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 44208 (\\N{HANGUL SYLLABLE GYEOL}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Temp\\ipykernel_31172\\182871790.py:99: UserWarning: Glyph 44284 (\\N{HANGUL SYLLABLE GWA}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 50696 (\\N{HANGUL SYLLABLE YE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 52769 (\\N{HANGUL SYLLABLE CEUG}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 47784 (\\N{HANGUL SYLLABLE MO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 45944 (\\N{HANGUL SYLLABLE DEL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51032 (\\N{HANGUL SYLLABLE YI}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53580 (\\N{HANGUL SYLLABLE TE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49828 (\\N{HANGUL SYLLABLE SEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53944 (\\N{HANGUL SYLLABLE TEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 45936 (\\N{HANGUL SYLLABLE DE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51060 (\\N{HANGUL SYLLABLE I}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 53552 (\\N{HANGUL SYLLABLE TEO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 44208 (\\N{HANGUL SYLLABLE GYEOL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 44284 (\\N{HANGUL SYLLABLE GWA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 49892 (\\N{HANGUL SYLLABLE SIL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\dnjsg\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 51228 (\\N{HANGUL SYLLABLE JE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최적 모델: Random Forest, 평균 교차 검증 R^2 점수: 0.7946679543879636\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# 데이터 불러오기\n",
    "columns = [\"Vendor name\", \"Model name\", \"MYCT\", \"MMIN\", \"MMAX\", \"CACH\", \"CHMIN\", \"CHMAX\", \"PRP\", \"ERP\"]\n",
    "data = pd.read_csv('machine.data.csv', names=columns)\n",
    "\n",
    "# 데이터 탐색\n",
    "print(data.head())\n",
    "print(data.describe())\n",
    "print(data.info())\n",
    "\n",
    "# 필요한 특성 선택 (모델링을 위해 문자형 변수 제거)\n",
    "features = [\"MYCT\", \"MMIN\", \"MMAX\", \"CACH\", \"CHMIN\", \"CHMAX\"]\n",
    "target = \"PRP\"\n",
    "\n",
    "# 특성과 타겟 분리\n",
    "X = data[features]\n",
    "y = data[target]\n",
    "\n",
    "# 결측치 확인 및 제거\n",
    "print(X.isnull().sum())\n",
    "print(y.isnull().sum())\n",
    "\n",
    "X = X.dropna()\n",
    "y = y.dropna()\n",
    "\n",
    "# 숫자가 아닌 값 확인 및 제거\n",
    "for col in features:\n",
    "    print(f\"{col} unique values: {X[col].unique()}\")\n",
    "\n",
    "# 숫자가 아닌 값이 있는 경우 제거 또는 변환\n",
    "X = X.apply(pd.to_numeric, errors='coerce')\n",
    "X = X.dropna()\n",
    "\n",
    "# 스케일링 전 데이터 형식 확인\n",
    "print(X.dtypes)\n",
    "\n",
    "# 특성 스케일링 (StandardScaler 사용)\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# y의 인덱스를 X의 인덱스와 일치시키기\n",
    "y = y.loc[X.index]\n",
    "\n",
    "# 데이터 분리 (80% 훈련, 20% 테스트)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 모델 초기화 및 파이프라인 설정\n",
    "models = {\n",
    "    'Linear Regression': LinearRegression(),\n",
    "    'Ridge Regression': Ridge(),\n",
    "    'Lasso Regression': Lasso(),\n",
    "    'Random Forest': RandomForestRegressor()\n",
    "}\n",
    "\n",
    "# 모델 학습 및 평가\n",
    "results = {}\n",
    "for model_name, model in models.items():\n",
    "    pipeline = Pipeline([\n",
    "        ('model', model)\n",
    "    ])\n",
    "    pipeline.fit(X_train, y_train)\n",
    "    \n",
    "    # 교차 검증\n",
    "    cv_scores = cross_val_score(pipeline, X_train, y_train, cv=10, scoring='r2')\n",
    "    results[model_name] = cv_scores.mean()\n",
    "    \n",
    "    # 예측\n",
    "    y_pred_train = pipeline.predict(X_train)\n",
    "    y_pred_test = pipeline.predict(X_test)\n",
    "    \n",
    "    # 성능 평가\n",
    "    train_r2 = r2_score(y_train, y_pred_train)\n",
    "    test_r2 = r2_score(y_test, y_pred_test)\n",
    "    \n",
    "    # 결과 출력\n",
    "    print(f\"모델: {model_name}\")\n",
    "    print(f\"교차 검증 평균 R^2 점수: {cv_scores.mean()}\")\n",
    "    print(f\"훈련 데이터 R^2 점수: {train_r2}\")\n",
    "    print(f\"테스트 데이터 R^2 점수: {test_r2}\")\n",
    "    print(\"-------------------------------------------------------\")\n",
    "    \n",
    "    # 결과 시각화 (테스트 데이터)\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.scatter(y_test, y_pred_test, color='blue', alpha=0.7)\n",
    "    plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "    plt.xlabel('실제 PRP')\n",
    "    plt.ylabel('예측 PRP')\n",
    "    plt.title(f'{model_name} 모델의 테스트 데이터 예측 결과')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 최적 모델 선택 (교차 검증 결과 기준)\n",
    "best_model = max(results, key=results.get)\n",
    "print(f\"최적 모델: {best_model}, 평균 교차 검증 R^2 점수: {results[best_model]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1646b395-9219-4987-b696-3b4881ea3930",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}