fuzzy-rules-generator/turbine_tree_co1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- TIT <= 1058.15\n",
      "|   |--- TAT <= 543.87\n",
      "|   |   |--- TAT <= 528.12\n",
      "|   |   |   |--- AH <= 92.13\n",
      "|   |   |   |   |--- value: [21.02]\n",
      "|   |   |   |--- AH >  92.13\n",
      "|   |   |   |   |--- value: [41.58]\n",
      "|   |   |--- TAT >  528.12\n",
      "|   |   |   |--- TIT <= 1028.85\n",
      "|   |   |   |   |--- value: [13.71]\n",
      "|   |   |   |--- TIT >  1028.85\n",
      "|   |   |   |   |--- value: [9.47]\n",
      "|   |--- TAT >  543.87\n",
      "|   |   |--- TAT <= 549.23\n",
      "|   |   |   |--- TIT <= 1049.65\n",
      "|   |   |   |   |--- value: [7.00]\n",
      "|   |   |   |--- TIT >  1049.65\n",
      "|   |   |   |   |--- value: [5.61]\n",
      "|   |   |--- TAT >  549.23\n",
      "|   |   |   |--- TIT <= 1056.05\n",
      "|   |   |   |   |--- value: [4.70]\n",
      "|   |   |   |--- TIT >  1056.05\n",
      "|   |   |   |   |--- value: [4.08]\n",
      "|--- TIT >  1058.15\n",
      "|   |--- TIT <= 1076.55\n",
      "|   |   |--- TAT <= 545.34\n",
      "|   |   |   |--- TIT <= 1076.45\n",
      "|   |   |   |   |--- value: [6.29]\n",
      "|   |   |   |--- TIT >  1076.45\n",
      "|   |   |   |   |--- value: [30.38]\n",
      "|   |   |--- TAT >  545.34\n",
      "|   |   |   |--- TAT <= 549.52\n",
      "|   |   |   |   |--- value: [3.87]\n",
      "|   |   |   |--- TAT >  549.52\n",
      "|   |   |   |   |--- value: [2.88]\n",
      "|   |--- TIT >  1076.55\n",
      "|   |   |--- TIT <= 1091.35\n",
      "|   |   |   |--- TAT <= 532.02\n",
      "|   |   |   |   |--- value: [12.04]\n",
      "|   |   |   |--- TAT >  532.02\n",
      "|   |   |   |   |--- value: [1.70]\n",
      "|   |   |--- TIT >  1091.35\n",
      "|   |   |   |--- TAT <= 530.62\n",
      "|   |   |   |   |--- value: [0.96]\n",
      "|   |   |   |--- TAT >  530.62\n",
      "|   |   |   |   |--- value: [1.35]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "\n",
    "data = pd.read_csv(\"data-turbine/clear-data.csv\")\n",
    "model = pickle.load(open(\"data-turbine/tree.model.sav\", \"rb\"))\n",
    "features = (\n",
    "    data\n",
    "    .drop([\"CO\"], axis=1)\n",
    "    .columns.values.tolist()\n",
    ")\n",
    "\n",
    "rules = tree.export_text(model, feature_names=features)\n",
    "print(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[if (TIT > 1058.15) and (TIT > 1076.55) and (TIT > 1091.35) and (TAT <= 530.625) -> 0.963,\n",
       " if (TIT > 1058.15) and (TIT > 1076.55) and (TIT > 1091.35) and (TAT > 530.625) -> 1.345,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (TAT > 528.115) and (TIT <= 1028.85) -> 13.709,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (TAT > 528.115) and (TIT > 1028.85) -> 9.473,\n",
       " if (TIT <= 1058.15) and (TAT > 543.87) and (TAT <= 549.235) and (TIT <= 1049.65) -> 7.003,\n",
       " if (TIT <= 1058.15) and (TAT > 543.87) and (TAT <= 549.235) and (TIT > 1049.65) -> 5.612,\n",
       " if (TIT <= 1058.15) and (TAT > 543.87) and (TAT > 549.235) and (TIT <= 1056.05) -> 4.698,\n",
       " if (TIT <= 1058.15) and (TAT > 543.87) and (TAT > 549.235) and (TIT > 1056.05) -> 4.079,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT <= 545.34) and (TIT <= 1076.45) -> 6.292,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT <= 545.34) and (TIT > 1076.45) -> 30.384,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT > 545.34) and (TAT <= 549.515) -> 3.874,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT > 545.34) and (TAT > 549.515) -> 2.883,\n",
       " if (TIT > 1058.15) and (TIT > 1076.55) and (TIT <= 1091.35) and (TAT <= 532.015) -> 12.044,\n",
       " if (TIT > 1058.15) and (TIT > 1076.55) and (TIT <= 1091.35) and (TAT > 532.015) -> 1.704,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (TAT <= 528.115) and (AH <= 92.135) -> 21.024,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (TAT <= 528.115) and (AH > 92.135) -> 41.576]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import get_rules\n",
    "\n",
    "\n",
    "rules = get_rules(model, features)\n",
    "display(len(rules))\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[if (TIT > 1058.15) and (TAT <= 530.625) -> 0.963,\n",
       " if (TIT > 1058.15) and (TAT > 530.625) -> 1.345,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (TAT > 528.115) -> 13.709,\n",
       " if (TIT <= 1058.15) and (TIT > 1028.85) and (TAT <= 543.87) and (TAT > 528.115) -> 9.473,\n",
       " if (TIT <= 1058.15) and (TAT > 543.87) and (TAT <= 549.235) -> 7.003,\n",
       " if (TIT <= 1058.15) and (TIT > 1049.65) and (TAT > 543.87) and (TAT <= 549.235) -> 5.612,\n",
       " if (TIT <= 1058.15) and (TAT > 543.87) -> 4.698,\n",
       " if (TIT <= 1058.15) and (TIT > 1056.05) and (TAT > 543.87) -> 4.079,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT <= 545.34) -> 6.292,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT <= 545.34) -> 30.384,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT > 545.34) and (TAT <= 549.515) -> 3.874,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT > 545.34) -> 2.883,\n",
       " if (TIT > 1058.15) and (TIT <= 1091.35) and (TAT <= 532.015) -> 12.044,\n",
       " if (TIT > 1058.15) and (TIT <= 1091.35) and (TAT > 532.015) -> 1.704,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (AH <= 92.135) -> 21.024,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (AH > 92.135) -> 41.576]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import normalise_rules\n",
    "\n",
    "\n",
    "rules = normalise_rules(rules)\n",
    "display(len(rules))\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[if (TIT > 1058.15) and (TAT <= 530.625) -> 0.963,\n",
       " if (TIT > 1058.15) and (TAT > 530.625) -> 1.345,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (TAT > 528.115) -> 13.709,\n",
       " if (TIT <= 1058.15) and (TIT > 1028.85) and (TAT <= 543.87) and (TAT > 528.115) -> 9.473,\n",
       " if (TIT <= 1058.15) and (TAT > 543.87) and (TAT <= 549.235) -> 7.003,\n",
       " if (TIT <= 1058.15) and (TIT > 1049.65) and (TAT > 543.87) and (TAT <= 549.235) -> 5.612,\n",
       " if (TIT <= 1058.15) and (TAT > 543.87) -> 4.698,\n",
       " if (TIT <= 1058.15) and (TIT > 1056.05) and (TAT > 543.87) -> 4.079,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT <= 545.34) -> 18.338,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT > 545.34) and (TAT <= 549.515) -> 3.874,\n",
       " if (TIT > 1058.15) and (TIT <= 1076.55) and (TAT > 545.34) -> 2.883,\n",
       " if (TIT > 1058.15) and (TIT <= 1091.35) and (TAT <= 532.015) -> 12.044,\n",
       " if (TIT > 1058.15) and (TIT <= 1091.35) and (TAT > 532.015) -> 1.704,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (AH <= 92.135) -> 21.024,\n",
       " if (TIT <= 1058.15) and (TAT <= 543.87) and (AH > 92.135) -> 41.576]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import delete_same_rules\n",
    "\n",
    "\n",
    "rules = delete_same_rules(rules)\n",
    "display(len(rules))\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AH</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4480</th>\n",
       "      <td>83.256</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>540.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24883</th>\n",
       "      <td>73.583</td>\n",
       "      <td>1099.8</td>\n",
       "      <td>538.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21557</th>\n",
       "      <td>81.089</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>534.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>64.757</td>\n",
       "      <td>1086.6</td>\n",
       "      <td>549.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21388</th>\n",
       "      <td>75.645</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>534.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>25725</th>\n",
       "      <td>85.663</td>\n",
       "      <td>1072.2</td>\n",
       "      <td>549.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5014</th>\n",
       "      <td>75.280</td>\n",
       "      <td>1058.0</td>\n",
       "      <td>549.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22584</th>\n",
       "      <td>92.874</td>\n",
       "      <td>1067.2</td>\n",
       "      <td>550.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>93.029</td>\n",
       "      <td>1099.9</td>\n",
       "      <td>524.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20828</th>\n",
       "      <td>88.840</td>\n",
       "      <td>1079.9</td>\n",
       "      <td>550.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29386 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AH     TIT     TAT\n",
       "4480   83.256  1100.0  540.65\n",
       "24883  73.583  1099.8  538.53\n",
       "21557  81.089  1100.0  534.04\n",
       "1705   64.757  1086.6  549.76\n",
       "21388  75.645  1100.0  534.21\n",
       "...       ...     ...     ...\n",
       "25725  85.663  1072.2  549.82\n",
       "5014   75.280  1058.0  549.86\n",
       "22584  92.874  1067.2  550.15\n",
       "501    93.029  1099.9  524.78\n",
       "20828  88.840  1079.9  550.02\n",
       "\n",
       "[29386 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4480     0.3527\n",
       "24883    1.2522\n",
       "21557    1.4718\n",
       "1705     1.3117\n",
       "21388    1.7835\n",
       "          ...  \n",
       "25725    2.4980\n",
       "5014     3.2652\n",
       "22584    1.2630\n",
       "501      0.7851\n",
       "20828    2.7272\n",
       "Name: CO, Length: 29386, dtype: float64"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AH</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>84.837</td>\n",
       "      <td>1088.7</td>\n",
       "      <td>550.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20343</th>\n",
       "      <td>59.574</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>542.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>81.262</td>\n",
       "      <td>1092.9</td>\n",
       "      <td>544.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>88.135</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>526.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5713</th>\n",
       "      <td>86.846</td>\n",
       "      <td>1080.2</td>\n",
       "      <td>550.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>21917</th>\n",
       "      <td>75.935</td>\n",
       "      <td>1081.1</td>\n",
       "      <td>549.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13099</th>\n",
       "      <td>78.314</td>\n",
       "      <td>1089.8</td>\n",
       "      <td>550.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26704</th>\n",
       "      <td>79.478</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>550.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4182</th>\n",
       "      <td>41.623</td>\n",
       "      <td>1100.2</td>\n",
       "      <td>539.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>69.233</td>\n",
       "      <td>1091.6</td>\n",
       "      <td>549.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7347 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AH     TIT     TAT\n",
       "18246  84.837  1088.7  550.39\n",
       "20343  59.574  1100.0  542.01\n",
       "2924   81.262  1092.9  544.91\n",
       "117    88.135  1100.0  526.21\n",
       "5713   86.846  1080.2  550.25\n",
       "...       ...     ...     ...\n",
       "21917  75.935  1081.1  549.66\n",
       "13099  78.314  1089.8  550.37\n",
       "26704  79.478  1073.0  550.19\n",
       "4182   41.623  1100.2  539.10\n",
       "2982   69.233  1091.6  549.98\n",
       "\n",
       "[7347 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "18246    1.34970\n",
       "20343    1.63430\n",
       "2924     0.78632\n",
       "117      0.72742\n",
       "5713     1.35980\n",
       "          ...   \n",
       "21917    1.45140\n",
       "13099    1.00960\n",
       "26704    2.01190\n",
       "4182     0.37685\n",
       "2982     1.15990\n",
       "Name: CO, Length: 7347, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "random_state = 9\n",
    "\n",
    "y = data[\"CO\"]\n",
    "X = data.drop([\"CO\"], axis=1).copy()\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=random_state\n",
    ")\n",
    "display(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[if (TIT = 1100.9) and (TAT = 511.04) -> 0.963,\n",
       " if (TIT = 1100.9) and (TAT = 550.6) -> 1.345,\n",
       " if (TIT = 1001.3) and (TAT = 535.993) -> 13.709,\n",
       " if (TIT = 1043.5) and (TAT = 535.993) -> 9.473,\n",
       " if (TIT = 1001.3) and (TAT = 546.552) -> 7.003,\n",
       " if (TIT = 1053.9) and (TAT = 546.552) -> 5.612,\n",
       " if (TIT = 1001.3) and (TAT = 550.6) -> 4.698,\n",
       " if (TIT = 1057.1) and (TAT = 550.6) -> 4.079,\n",
       " if (TIT = 1067.35) and (TAT = 511.04) -> 18.338,\n",
       " if (TIT = 1067.35) and (TAT = 547.428) -> 3.874,\n",
       " if (TIT = 1067.35) and (TAT = 550.6) -> 2.883,\n",
       " if (TIT = 1074.75) and (TAT = 511.04) -> 12.044,\n",
       " if (TIT = 1074.75) and (TAT = 550.6) -> 1.704,\n",
       " if (TIT = 1001.3) and (TAT = 511.04) and (AH = 24.085) -> 21.024,\n",
       " if (TIT = 1001.3) and (TAT = 511.04) and (AH = 100.2) -> 41.576]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import simplify_rules\n",
    "\n",
    "rules = simplify_rules(X_train, rules)\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AH</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>77.867015</td>\n",
       "      <td>14.461355</td>\n",
       "      <td>24.085000</td>\n",
       "      <td>68.1880</td>\n",
       "      <td>80.4700</td>\n",
       "      <td>89.3760</td>\n",
       "      <td>100.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIT</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>1081.428084</td>\n",
       "      <td>17.536373</td>\n",
       "      <td>1000.800000</td>\n",
       "      <td>1071.8000</td>\n",
       "      <td>1085.9000</td>\n",
       "      <td>1097.0000</td>\n",
       "      <td>1100.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAT</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>546.158517</td>\n",
       "      <td>6.842360</td>\n",
       "      <td>511.040000</td>\n",
       "      <td>544.7200</td>\n",
       "      <td>549.8800</td>\n",
       "      <td>550.0400</td>\n",
       "      <td>550.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>36733.0</td>\n",
       "      <td>2.372468</td>\n",
       "      <td>2.262672</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>1.1824</td>\n",
       "      <td>1.7135</td>\n",
       "      <td>2.8429</td>\n",
       "      <td>44.103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count         mean        std          min        25%        50%  \\\n",
       "AH   36733.0    77.867015  14.461355    24.085000    68.1880    80.4700   \n",
       "TIT  36733.0  1081.428084  17.536373  1000.800000  1071.8000  1085.9000   \n",
       "TAT  36733.0   546.158517   6.842360   511.040000   544.7200   549.8800   \n",
       "CO   36733.0     2.372468   2.262672     0.000388     1.1824     1.7135   \n",
       "\n",
       "           75%       max  \n",
       "AH     89.3760   100.200  \n",
       "TIT  1097.0000  1100.900  \n",
       "TAT   550.0400   550.610  \n",
       "CO      2.8429    44.103  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/user/Projects/python/fuzzy-rules-generator/.venv/lib/python3.12/site-packages/skfuzzy/control/fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SGBc8jkqulfik+ieq42SaFD25pFF5N95vUJJpm89y9V6c6jhC5Jy/psDD69qwlqW16jS5o0wzqPcZ7AjQWm0IkUctOL6AW49vEdgoEGsL0zu/pejJRaPbVcLdyY4RK8NJlWEukRddD4HQhfDGOChcWXWa3NVqIuR3h6B+WssNIfKYo1FH+feZfzOw1kDKFiirOk6WSNGTi/LZWjG7hxfHIh7y3b4rquMIYViJT7R/8D3rQ8MBqtPkPpt8WouNG4e0wk+IPCQ+OR6//X7ULFyT96u8rzpOlknRk8vqlXLl00almbPtAheiHquOI4Th7AiAx1HaP/wWlqrTqFGyITTsrw3xRZ9TnUYIg5l7dC73E+4zxXcKliZ8fkvRo8Dw1hXxdLVn+IpwklPlaQ+RB1zepd3E22oSFDTNy94G88Z4cCml3cydmqw6jRDZFno7lOXnlzOk9hBKOJVQHSdbpOhRwM7akjk9a3LmTiyLd8tNj8LEJcTA2gFQqjHU+1R1GvWs7aDbYrhzQntsXwgT9jjpMf4h/nh7ePN2pbdVx8k2KXoUqelZgH5Ny/LNzoucuhWjOo4QWbd1rFb4dFkIFvIjBYBidaDxMG2CxjsnVKcRIstmHZ7F46THTPKdhIXO9M9v098DEzaoRXnKFc7PiJXhJKbI0x7CBJ3fAsd/gTaB4FJSdRrj0mQkFKqsteJISVSdRohM23NjD2surWFkvZEUzV9UdRyDkKJHIRsrC+b2rMnlu0/4esdF1XGEyJz4B7B+EJRrBbX7qE5jfKxstGGuexe0Kz5CmJBHCY+YEDqBxsUa061cN9VxDEaKHsWqFHVicIvyLNlzmeMRD1XHESLjNn0JKQnQeT7odKrTGCeP6tBslHZvz80jqtMIkWFTD00lKTWJCT4T0OWh81uKHiPQt2lZqhdzZvjKcBKSZZhLmIDTQXBqFbSfDU5FVKcxbr5DoUhNbZgr+anqNEK81rZr29h8dTNjvMdQ2KGw6jgGJUWPEbCytGBOTy9uPnzKrK3nVccR4tWe3IWNw6BSR6jeQ3Ua42dppbXkeBQBOyerTiPEK91/ep8pB6bQskRLOpTuoDqOwUnRYyTKFXbky9YV+SH4Kgev3FcdR4gX0+thwxDt/zvOk2GtjCpUEVr4w4FFcC1YdRohXkiv1zMpdBIAfg388tSw1t+k6DEiHzcqTd2SLoxYFU5cYorqOEI878QKOLcBOn4F+QupTmNaGvSDEg20Vh2JT1SnEeI5G65s4K8bf+Hf0J+C9gVVx8kRUvQYEUsLHbN7eHHvcRJTN51VHUeIZ8Xehs1fakNaVbqoTmN6LCyh6yKIuwvb/VWnEeIZUXFRTDs0jfal29OyZEvVcXKMFD1GpmTBfIxtX4lfD0aw98Jd1XGE0Oj1sG4QWNlDu5mq05gu1zJaq44j38Plv1SnEQLQhrUCQgOws7RjrPdY1XFylBQ9Rqi3d0l8yxVk1OoTxDyV3j3CCBz7N1zaDp2/AQdX1WlMW91PoEwzrXVHgszGLtT78+KfBN8KZoLPBJxtnVXHyVFS9BghCwsdM7t78TghhckbzqiOI8zdw+taq4la70GFNqrTmD4LC+i8ABIfw5YxqtMIM3fryS1mHp7Jm+XfpEnxJqrj5DgpeoxUsQL2+HeswqqjN9l+Jkp1HGGu0tJgbX+wd4E201SnyTsKeELbaRD2K5zfrDqNMFNp+jT8g/1xtnXmy7pfqo6TK6ToMWI96hbnjUqFGfPnSR7GJamOI8zR4e/g2j7osgDsnFSnyVtq9oYKbbV7peIfqE4jzNDv537nUOQhJvlOIr9NftVxcoUUPUZMp9Mx/c3qJKemMX7tKdVxhLm5f1l7yqjeZ9o9KMKwdDro9DWkJsGmEarTCDNzPfY6847O4+2Kb9OgSAPVcXKNFD1GrrCTHZO6VGXDiTtsOHFbdRxhLtJStflkHD2g1UTVafIuRw/oMAdOrYbTa1SnEWYiNS2VcfvHUcihEEPrDFUdJ1dJ0WMCOnsVpV01D8YHneLu40TVcYQ5CF0ANw5B18Vgk091mryt2lvavEcbhsGTaNVphBn495l/c+LuCQIbBeJg7aA6Tq6SoscE6HQ6pnSthoVOx5g/T6LX61VHEnlZ9Fn4awo07A8lG6pOk/fpdNBhLugsYMNQbU4kIXLIpYeXmH98Ph9U/YBahWupjpPrlBc9CxcupFSpUtjZ2eHt7c2hQ4deufy8efOoWLEi9vb2eHp6MnToUBISEnIprToF89sS2K06O85G8eexW6rjiLwqNVnrBu5SGt4YrzqN+cjnpt3fc24DnFiuOo3Io5LTkhkXPA5PR08G1BqgOo4SSoue5cuXM2zYMAICAjh27BheXl60adOG6OgXX+L97bffGD16NAEBAZw9e5bvv/+e5cuXM3Zs3p5B8m9tq3nQrVYxJqw/zZ2Yp6rjiLxo/1cQeRK6LQZrO9VpzEvljlCjF2waCTHyi40wvO9Ofsf5B+cJbBSIraWt6jhKKC165s6dy2effcZHH31ElSpVWLJkCQ4ODvzwww8vXD4kJARfX1/effddSpUqRevWrXnnnXdee3UoL5nQqSoONpaMXHVChrmEYd0Jhz0zoPEwKFZHdRrz1G4G2DjAuoEyzCUM6uz9sywNX8on1T+hmls11XGUUVb0JCUlcfToUVq2/G9jMwsLC1q2bEloaOgLP+Pj48PRo0fTi5wrV66wadMm2rdv/9LtJCYmEhsb+8zLlDk7WDP9rRrsu3iP3w/dUB1H5BUpibCmHxSqDE1Gqk5jvuxdtNmaL++EYz+rTiPyiKTUJMYFj6NsgbL0rdFXdRyllBU99+7dIzU1FXd392fed3d3JzIy8oWfeffdd5k0aRKNGjXC2tqasmXL0qxZs1cOb02bNg1nZ+f0l6enp0H3Q4XmFQvzdj1Ppmw8w40H8arjiLxg93S4d0Eb1rKyUZ3GvJVvCbU/gK3jtBYgQmTT4vDFXI25SmCjQKwtrVXHUUr5jcyZsXv3bqZOncqiRYs4duwYf/75Jxs3bmTy5Mkv/cyYMWOIiYlJf924kTeujozrUBkXBxtGrAwnLU0ug4tsuHkEgudBs1HgUV11GgHQJhDsXbUWIGlpqtMIE3bi7gl+OPUD/bz6UdG1ouo4yikretzc3LC0tCQq6tm+UlFRUXh4eLzwM+PHj+f999/n008/pXr16nTr1o2pU6cybdo00l7yg8HW1hYnJ6dnXnmBo501s3rU4ODVB/wUck11HGGqkp9qT2sVqQm+5jVJmVGzdYSuC7UWIIeWqk4jTFRCSgLj9o+jimsVPq72seo4RkFZ0WNjY0OdOnXYuXNn+ntpaWns3LmThg1fPDdIfHw8FhbPRra0tAQwy5t6fcq68aFPKWZsOcflu09UxxGmaOdkeBQB3ZaApZXqNOJ/lW4C9f8FOybAvUuq0wgT9M3xb7j95DaBjQKxspDzGxQPbw0bNoxly5bx888/c/bsWfr160dcXBwfffQRAH369GHMmDHpy3fq1InFixfzxx9/cPXqVbZv38748ePp1KlTevFjbka2rUgRZztGrAwnVYa5RGZcC4YDi6CFPxSSy95GqeUEcCqqtQRJS1WdRpiQI5FH+OXMLwyqPYgyBcqojmM0lJZ+vXr14u7du/j7+xMZGUnNmjXZsmVL+s3NERERz1zZ8fPzQ6fT4efnx61btyhUqBCdOnUiMDBQ1S4o52BjxZyeXvRYEsrSvVfo16ys6kjCFCQ+0f4hLdEAGvRTnUa8jI2D1grkx7YQMh8aDVGdSJiA+OR4/IL9qFW4Fu9Vfk91HKOi05vZuFBsbCzOzs7ExMTkmft7AKZtOsuPwddYP7ARFT0cVccRxm7DUAj/A/oFg6v8Fmj0tvvDgcXw+R5wr6I6jTByk0Mns/7KelZ3Wo2nk+k/sWxIJvX0lni5oa0qULKgA8NWhJGcKk97iFe4tBOO/ACtJknBYyqajdWOVVBfrVWIEC8RciuEFRdWMKzOMCl4XkCKnjzCztqSuT1rci7yMQt3yU2P4iWePtJm+y3TDOp+ojqNyChrO+1m88hTsG+O6jTCSMUmxeIf4k+DIg3oWbGn6jhGSYqePKR6cWf6Ny/Hgr8ucepWjOo4whhtHQuJj7VZfy3k9DcpRWtBkxGwdxbcDlOdRhihmYdmEpccxySfSVjo5Px+EflbyWMGNC9HBXdHhq0IIzFFnvYQ/+PcJgj7FdpOgwJy2dskNR4BhatocyulJKpOI4zIrohdrL28lpH1RlIkfxHVcYyWFD15jI2VBXN7eXH1Xhxfbb+oOo4wFvEPYP1gqNAWavZWnUZklZWNNsx1/xLsnqY6jTASjxIeMTF0Ik2LN6Vrua6q4xg1KXryoEoeTgxpWYGley9z9PpD1XGEMdg4HFKToNPXoNOpTiOyw70qNB8LwV/DjcOq0wgjEHgwkOS0ZAIaBqCT8/uVpOjJo/7VpAw1ihdgxMpwnibJMJdZO/UnnP4TOswBxxe3eBEmxmcQFK2tPc2VJE2HzdmWa1vYcm0L47zHUcihkOo4Rk+KnjzKytKCOT29uP3oKTO3nlMdR6jyJFq7ylOlC1R7S3UaYSiWVtowV8xN2DlJdRqhyL2n9wg8EEirkq1oV7qd6jgmQYqePKxsofyMbFuJH4OvEXr5vuo4Irfp9dp9PDoL6DBXhrXyGrfy0CIADi6Gq/tUpxG5TK/XMzF0IhY6C/wa+MmwVgZJ0ZPHfeRTivqlXflyVThPElNUxxG5KfwPOL9Ju48nn5vqNCInePeFko1g7RfaVATCbKy/sp7dN3bj39AfVztX1XFMhhQ9eZyFhY7Z3b14EJdE4MazquOI3BJzCzaPghq9oHJH1WlETrGwgC4LIO4+bBuvOo3IJZFxkUw/OJ2OZTrSokQL1XFMihQ9ZqBEQQfGtq/M74ci2HPhruo4Iqfp9bBugNasst0M1WlETnMtDW2mwNEf4dIO1WlEDtPr9QSEBGBvZc/o+qNVxzE5UvSYid7eJWhc3o1Rq04Q81R69+RpR3+Cy39psy7bu6hOI3JDnY+g7BuwdqDWakTkWasuriLkdggTfSfibOusOo7JkaLHTOh0Oma8VYO4xBQmrj+tOo7IKQ+vwdZxUPsDKN9SdRqRW3Q66DwfkuJgi/z2n1fdfHyTWYdn8Vb5t2hUrJHqOCZJih4zUrSAPQGdq/LnsVtsOx2pOo4wtLQ0COoPDgWhTaDqNCK3OReHdtMh/Hc4t1F1GmFgafo0xgePx8XWhS/rfak6jsmSosfMvFW7GC0rF2bsmpM8iEtSHUcY0qFv4fp+6LoQbB1VpxEqeL0DFdppUxXEyTQVeclvZ3/jSNQRJvtOJp91PtVxTJYUPWZGp9Mx9c3qpKTpGR90SnUcYSj3LsKOCVD/X1C6ieo0QhWdTpuiIC0FNg1XnUYYyNWYq8w7No93K71L/SL1VccxaVL0mKHCjnZM7lKNjSfvsD78tuo4IrvSUiGoHzgVhZYBqtMI1RzdtZYjp9fAqdWq04hsSk1LxS/YD498HgypM0R1HJMnRY+Z6uRVlA41ijB+7SmiHyeojiOyI+QbuHUUui4BG7nsLdBajlTtprUgeRylOo3Ihp9O/8Spe6eY4jsFeyt71XFMnhQ9Zmxyl2pYWegYs/oker1edRyRFVFnYNdUaDgASnirTiOMSfs5YGGl3d8j57dJuvjwIgvDFvJB1Q+oWbim6jh5ghQ9Zsw1nw3T3qzBznPRrDp6U3UckVmpybDmX+BaBpqPU51GGJt8BaHTN3Bhs/ZElzApyWnJjNs/jhKOJehfs7/qOHmGFD1mrlUVd96sXYxJ689w+9FT1XFEZuydDVGntW7b1naq0whjVKm99kTX5lFaR3ZhMpadWMaFhxcIbByIraWt6jh5hhQ9goBOVclna8Wo1SdkmMtU3D4O+2ZDkxFQtJbqNMKYtZ0ONvlh7QAZ5jIRp++fZtmJZXxW4zOqFqyqOk6eIkWPwNnemhnda7Dv4j1+PRihOo54nZREWNMPCleGxiNUpxHGzr4AdJkPV3bBkR9UpxGvkZSahN9+P8q7lOfz6p+rjpPnSNEjAGhaoRDvepdg6qazRNyPVx1HvMquqXD/EnT7FqxsVKcRpqBcS60/17bx8OCq6jTiFRaGLeRa7DWmNJqCtaW16jh5jhQ9It3Y9pVxzWfDiJXhpKXJZXCjdOOQ9oh68zHgLpe9RSa0nqzd3Ly2v9ayRBidsOgwfjr9E/1r9qeCSwXVcfIkKXpEuvy2Vszu4cWhaw/4IVh+GzQ6SfGwpi8UrQ0+g1WnEabG1hG6LobrwXBwieo04h+epjzFL9iPagWr8WHVD1XHybOk6BHPaFCmIB/5lmLW1vNcin6iOo74XzsnQuwt7WktSyvVaYQpKtUIvPtp30v3LqpOI/7H18e+JjIukimNpmBlIed3TpGiRzxnZJtKFC1gz/CV4aSkymVwo3B1r/bbeYsAcCuvOo0wZS38tY7sa/pCaorqNAI4dOcQv579lcG1B1PaubTqOHmaFD3iOfY2lszu4cXJm4/4du8V1XFE4mPtPoySvuDdV3UaYepsHLSWJbePQcjXqtOYvbjkOPxD/KnrXpfelXurjpPnSdEjXqhOSRc+b1KWeTsucPZOrOo45m2bH8Tdhy4LwUJOWWEAnvXAdzDsmqZNcCmUmX1kNg8SHjDJdxIWOjm/c5r8DYuXGtqqPKXd8jF8RThJKTLMpcTFHXD0J+3JG1e57C0MqNkYbah0zb8gJUl1GrO0/9Z+Vl1YxYi6I/B09FQdxyxI0SNeytbKkrk9a3Ih6jEL/pKbHnPd04ewbiCUaQ51P1adRuQ1Vrba01zRZ7XZvUWuikmMISAkAJ+iPvSo0EN1HLMhRY94pWrFnBnwRjkW7r7MiZuPVMcxL5tHQ9IT6LIAdDrVaUReVLQmNPlS6+N265jqNGZlxqEZPE1+ykSfiejk/M41UvSI1+rfvByVizgybEU4CcmpquOYh7Mb4MQf0G6G9qSNEDml8XDwqAZB/SA5QXUas7AzYifrr6xnVP1ReOTzUB3HrEjRI17L2tKCOT1qEnE/nq+2X1AdJ++Luw8bhkCFdlqHbCFykqW11tLkwRXYFag6TZ73MOEhk0In0cyzGZ3LdlYdx+xI0SMypKKHI0NbVWDpviscufZAdZy8S6+HjUMhLQU6fS3DWiJ3FK4MzcdByHyIOKg6TZ6l1+uZfGAyqfpUAhoGyLCWAlL0iAz7vEkZanoWYMTKcOKTZFKzHHFqNZxZCx3mgKO76jTCnPgMhOL1IKgvJMWpTpMnbbm2he3Xt+PXwA83ezfVccySFD0iwywtdMzp4UVkbAIzNp9THSfveRwJm0ZA1W5Q7S3VaYS5sbDUnuaKvQM7JqpOk+fcjb9L4MFA2pRqQ9tSbVXHMVtS9IhMKVMoP6PaVuLn0OuEXLqnOk7eodfD+sFgYQXt56hOI8yVWzloOQEOfQtX9qhOk2fo9Xomhk7ESmfFOO9xquOYNSl6RKZ90LAUDcq48uWqEzxOSFYdJ28I+w0ubIFO30C+gqrTCHNW/3Mo1RjWDoAEmY3dENZeXsuem3sIaBiAi52L6jhmTYoekWkWFjpmdffiUXwSgRvPqo5j+mJuwpbR2pNaldqrTiPMnYWF1vLk6QPYJlclsisyLpIZh2bQuWxnmpdorjqO2ZOiR2SJp6sD4zpU4Y/DN9h1Llp1HNOl12vNRG3yQ9vpqtMIoXEpCW0C4di/4eJ21WlMll6vZ3zweBysHRhVf5TqOAIpekQ2vFPfkyYVCjFq9QkexUvvniw58j1c2Q1d5oN9AdVphPiv2h9AuZZaK5SnD1WnMUkrzq/gwJ0DTPaZjJONk+o4Ail6RDbodDpmvFWdp8mpTFgnnZoz7cEV2OYPdT7S/nERwpjodNB5PiTHw2a5SpFZN2JvMOfoHHpU6IFPMR/VccT/k6JHZEsRZ3smdq5KUNhttpyKVB3HdKSlQVB/7abl1pNVpxHixZyKQruZcGI5nF2vOo3JSNOn4Rfsh6udK8PrDlcdR/wPKXpEtnWrVYxWVdwZt+Yk958kqo5jGg4uhogQbV4UW0fVaYR4uRq9oFJHWD8E4mSaioz45cwvHIs+xmTfyeSzzqc6jvgfUvSIbNPpdEztVp00vR6/oFPo9XrVkYzb3Qva5G/e/aBUI9VphHg1nQ46fgX6NNgwVLv5XrzUlZgrfH3sa96r/B71POqpjiP+QYoeYRCFHG2Z0rU6m09Fsi78tuo4xis1RZvmv4AntPBXnUaIjMlfWCt8zq7TWqWIF0pJS8Fvvx9F8xdlcO3BquOIF5CiRxhMhxpF6ORVFP+1p4mKTVAdxziFfA23j0PXJWDjoDqNEBlXtavWHmXjcK1VhXjOj6d+5PT900xpNAU7KzvVccQLSNEjDGpS56rYWFkw5s+TMsz1T5GnYNc08B0MnnLZW5ig9rPBylZrmSLn9zPOPzjPovBFfFT1I7wKeamOI15Cih5hUC75bJjWrTp/nYtm5ZGbquMYj5QkbVjLrTw0G6M6jRBZ4+CqtUq5uBWO/6I6jdFITk3GL9iPUk6l+KLmF6rjiFeQokcYXMsq7nSvU5xJG85w82G86jjGYe8siD6rPa1lZas6jRBZV7Et1HwPtoyBRxGq0xiFb098y6WHl5jaaCo2ljaq44hXkKJH5Aj/TlVwsrNi1OoTpKWZ+WXwW8dg3xxo8iUUrak6jRDZ13Yq2DlrTUnT0lSnUer0vdN8d/I7Pvf6nMoFK6uOI15Dih6RI5zsrJnRvQbBl+7z68HrquOok5wAa/qCRzVoLJOUiTzCzhm6LICre7RWKmYqMTWRsfvHUtG1Ip9W/1R1HJEBUvSIHNO4fCHea1CCqZvOce1enOo4auwKhIdXodu3YGmtOo0QhlO2OdT9BLb7w/3LqtMosfD4Qm48vkGgbyDWFnJ+mwIpekSOGtOuMoUcbflyVTip5jbMFXEAQuZD83FQWC57izyo1SRtDp+1/SEtVXWaXHU8+jg/nf6JAbUGUM6lnOo4IoOk6BE5Kp+tFbO61+DI9Yf8sP+q6ji5JykOgvpB8XrgM1B1GiFyhm1+7eb8iANwYJHqNLkmPjkev/1+1ChUgw+qfKA6jsgEKXpEjvMuU5CPfUsza9t5LkU/Vh0nd+yYoE3g1nUxWFiqTiNEzinpAw37w87JcPe86jS5Yt6xeUTHRxPYKBBLOb9NihQ9Ild82aYixV3sGb4inJTUPP60x5U9cGgptJwAbnLZW5iBN/zApaR2035qiuo0OergnYP8fu53htQZQkmnkqrjiEySokfkCjtrS+b08OLkrRiW7MnDNz0mxGr3N5RqDPU/V51GiNxhba+1VrkTBsFfqU6TY54kPWF88Hjqe9TnnUrvqI4jskCKHpFrapVwoV+zsny98yJnbseqjpMzto2Dpw+hy0KwkNNLmJHidaDRUNg9AyJPqk6TI2YfmU1MYgyTfCdhoZPz2xQpP2oLFy6kVKlS2NnZ4e3tzaFDh165/KNHj+jfvz9FihTB1taWChUqsGnTplxKK7JrUIvylC2Un2ErwkhKyWPDXBe2wbF/Q5tA7VK/EOam6SgoVFEb5kpJUp3GoPbe3Mvqi6v5st6XFMtfTHUckUVZLnp27txJx44dKVu2LGXLlqVjx47s2LEjU+tYvnw5w4YNIyAggGPHjuHl5UWbNm2Ijo5+4fJJSUm0atWKa9eusWrVKs6fP8+yZcsoVky+AU2FrZUlc3p6cSn6Cd/svKg6juHEP4B1A6FcS6gtT3MIM2Vlq928f/cc7JmhOo3BxCTGMCFkAr7FfHmr/Fuq44hsyFLRs2jRItq2bYujoyODBw9m8ODBODk50b59exYuXJjh9cydO5fPPvuMjz76iCpVqrBkyRIcHBz44YcfXrj8Dz/8wIMHDwgKCsLX15dSpUrRtGlTvLyko60pqVrUmUEtyrN4z2XCbjxSHccwNo+ClKfQeT7odKrTCKFOkRrQdDTs/wpuHlWdxiCmHZpGQmoCExtORCfnt0nT6fX6TM8YV7x4cUaPHs2AAQOeeX/hwoVMnTqVW7duvXYdSUlJODg4sGrVKrp27Zr+/gcffMCjR49Yu3btc59p3749rq6uODg4sHbtWgoVKsS7777LqFGjsLR88WODiYmJJCYmpv85NjYWT09PYmJicHJyyuAeC0NLTk3jrcUhxCWmsHFQY+ysTfixzzPrYMX72qzLXm+rTiOEeqkp8H1Lbb6qf+3VbnQ2UTuu72Do7qFMbTSVTmU7qY4jsskqKx969OgRbdu2fe791q1bM2rUqAyt4969e6SmpuLu7v7M++7u7pw7d+6Fn7ly5Qp//fUXvXv3ZtOmTVy6dIkvvviC5ORkAgICXviZadOmMXHixAxlErnH2tKCOT286DB/P3O2nWdchyqqI2VN3D3YMBQqdYQavVSnyRK9Xk9KSt5+zNiUWVpaYmFqN8VbWmlPc33bBP6aot3nZoIeJDxg8oHJvOH5Bh3LdFQdRxhAloqezp07s2bNGr788stn3l+7di0dO+bcN0ZaWhqFCxdm6dKlWFpaUqdOHW7dusWsWbNeWvSMGTOGYcOGpf/57ys9Qr3y7o6MaF2BaZvP0aqKB/VLu6qOlDl6PWwYAvo06PiVSQ5rpaSkcPfuXbJwwVfkIgcHB5ydnU1raKVwJW3+nu3+2i8FJRuqTpQper2eyaGT0ev1jG843rT+7sVLZanoqVKlCoGBgezevZuGDbVv5AMHDhAcHMzw4cP55ptv0pcdNGjQC9fh5uaGpaUlUVFRz7wfFRWFh4fHCz9TpEgRrK2tnxnKqly5MpGRkSQlJWFjY/PcZ2xtbbG1tc30Porc8UmjMmw7HcWIleFsHtyYfLZZ+pZU4+QqOLseevys9R8yMXq9nkePHmFhYYGLi4v8UDdCer2epKQkYmO1KR4KFCigNlBmNewP5zZqLVn6BYNNPtWJMmzT1U3siNjBnKZzcLN3Ux1HGEiW7ukpXbp0xlau03HlypWXft3b25v69eszf/58QLuSU6JECQYMGMDo0aOfW37s2LH89ttvXLlyJf1y79dff82MGTO4fft2hjLFxsbi7Ows9/QYkWv34mj39T661ynO5K7VVMfJmNg7sKgBlGsB3V98472xS01NJSoqChcXF+ztTfeeC3Pw5MkTYmNj8fDwML2hrvuXYUkjqNkbOsxWnSZDouOj6ba2G77FfJnZZKbqOMKAsvRr9dWrhmkcOWzYMD744APq1q1L/fr1mTdvHnFxcXz00UcA9OnTh2LFijFt2jQA+vXrx4IFCxg8eDADBw7k4sWLTJ069aVXk4RpKOWWj9HtKhGw7jRtqnrQqLyR/1al18P6Qdrjue1N44f4i6SlafMkvewhAGE8/r6KnZqaanpFT8GyWjf2TSOgckco00x1olfS6/VMCJmAjaUN47zHqY4jDEzpWEKvXr24e/cu/v7+REZGUrNmTbZs2ZJ+c3NERMQzJ7inpydbt25l6NCh1KhRg2LFijF48OAM3zwtjNf7DUqy9XQkI1eFs2VoE5zsrFVHernjv8DFbfDOcnAwsfuQXkCGtYyfyR+jup/A2XWwdoA2zGXnrDrRSwVdCmLfrX0seGMBzrbGm1NkTYaHt4YNG8bkyZPJly/fMzcGv8jcuXMNEi4nyPCW8br5MJ628/bRvroHM7sb6dxLjyJgkQ9U6QJdMz4nlTFKTk7m7t27FCpUCGtrIy4yRd44Vn+fO1W7aG1ajNDtJ7d5c92btCrZism+k1XHETkgw1d6jh8/TnJycvr/v4zJ/0YilCnu4sD4jpUZtfokbap60KKy++s/lJvS0rRmonbO0Haq6jRCmJYCJbTzZt1AqNwZKrRRnegZafo0/IP9cbRxZGS9karjiByS4aJn165dL/x/IQypZ11PtpyKZPSfJ9k2xAWXfM8/kafMke/h6l54P8ioL8/ndc2aNaNmzZrMmzdPdRSRWbXe1554XDcQvjhgVMPDy88v52DkQZa2WoqjjaPqOCKHmNgdcSKv0+l0TH+rBkkpaQSsO606zn/dv6zNN1L3EyjbXHUaIUyTTgedvoGURNhsPFdTImIj+OroV/Sq2IuGRU1rPiGROVkqeuLi4hg/fjw+Pj6UK1eOMmXKPPMSIjvcneyY2Lkq68Jvs+nkHdVxIC0Vgr7Q5uJpNUl1GiFMm1MR7anHkyvhzPPthnJbaloqfsF+FLQryLA6r75fVZi+LD299emnn7Jnzx7ef/99ihQpIvfxCIPrUrMoW05F4hd0ivqlXXHLr3CCyQOL4MZB+GgT2OZXl0M85+HDhwwePJj169eTmJhI06ZN+eabbyhfvjx6vZ7ChQuzePFiunfvDkDNmjWJiorizh2tmN6/fz8tWrTg4cOHODg4qNwV81K9O5xdq7VwKeED+Qspi/LL2V8Iiw7jx7Y/4mAt3wN5XZaKns2bN7Nx40Z8fX0NnUcIQBvmmtKtGq2/2svYP0/y7ft11BTX0edg52RtZtmSPrm//Vz2NCmVy3ef5Pp2yxbKj71N5ucL+vDDD7l48SLr1q3DycmJUaNG0b59e86cOYO1tTVNmjRh9+7ddO/enYcPH3L27Fns7e05d+4clSpVYs+ePdSrV08Kntym00GHr7QJPjcMgV6/KGnjcvnRZb459g3vV3mfOu51cn37IvdlqehxcXHB1dV4bkATeZNbflumdqtG31+OERR2i261iudugNQUCOoLLiW1HkJm4PLdJ3Scvz/Xt7thYCOqFcvczeF/FzvBwcH4+GgF6a+//oqnpydBQUH06NGDZs2a8e233wKwd+9eatWqhYeHB7t376ZSpUrs3r2bpk2bGnx/RAbkL6T1rFvxvjbUVaNnrm4+JS2FcfvHUcyxGANrDczVbQt1slT0TJ48GX9/f37++Wf5DUnkqLbVitClZlEC1p6mYRk3PJztcm/j+7+CO+HwyQ6wNo82DWUL5WfDwEZKtptZZ8+excrKCm9v7/T3ChYsSMWKFTl79iwATZs2ZfDgwdy9e5c9e/bQrFmz9KLnk08+ISQkhJEjjeeGWrNTpTNU76nN1lyqETgVzbVNf3/ye84+OMsv7X7BzioXf64IpTJc9NSqVeuZ4YVLly7h7u5OqVKlnpss69ixY4ZLKMzexM5VCb18n1GrT/DTR/VyZ5jrzgnYMwMaDYXi5nPZ297GMtNXXIxZ9erVcXV1Zc+ePezZs4fAwEA8PDyYMWMGhw8fJjk5Of0qkVCk/UxYuBfWDYLeK3NlmOvcg3MsObGET6p9QvVC1XN8e8J4ZLjo6dq1aw7GEOLlCjjYMOOtGnz002GWH77B2/VL5OwGU5K0rtBuFaCptDgxVpUrVyYlJYWDBw+mFy7379/n/PnzVKlSBdDuDWvcuDFr167l9OnTNGrUCAcHBxITE/n222+pW7cu+fKZTufvPMneBTrPh996wLF/Q50PcnRzyanJjNs/jjLOZejr1TdHtyWMT4aLnoCAgJzMIcQrNa9UmF51PZm84Qy+5dzwdM3BYdU9M+DuOfhsl9ZUVBil8uXL06VLFz777DO+/fZbHB0dGT16NMWKFaNLly7pyzVr1ozhw4dTt25d8ufXhtGaNGnCr7/+ypdffqkqvvhfFVprExduHas1JHUpmWObWhy+mCuPrvB7x9+xsTSiyU9FrsjSPD03btzg5s2b6X8+dOgQQ4YMYenSpQYLJsQ/+XWsTAEHG0auOkFaWoZaxmXezaOwf652hadIjZzZhjCYH3/8kTp16tCxY0caNmyIXq9n06ZNzwy5N23alNTUVJo1a5b+XrNmzZ57TyjWZqp21Wdtf63lSw44efck35/6nr5efankWilHtiGMW4Ybjv6vxo0b8/nnn/P+++8TGRlJhQoVqFatGhcvXmTgwIH4+/vnRFaDkIajpi340j16f3eQCZ2q8KFvacOuPPkpfNsEbPLBJ9vB0kQbO2ZQnmhiaSbM5lhd2Q3/7gLtZoH35wZddUJKAj039MTByoFf2v+ClUWWnuMRJi5LV3pOnTpF/fr1AVixYgXVq1cnJCSEX3/9lZ9++smQ+YR4hm85N/o0LMn0Lee4ei/OsCv/awo8vA5dl+T5gkcIo1SmGdT7TGv5cv+yQVc9//h8bj2+RWCjQCl4zFiWip7k5GRsbbV7HXbs2EHnzp0BqFSpUvpMp0LklNHtKuHuZMfwFWGkGmqY63oIhC7U5uMpLJe9hVCm1URw9NAeJkhLNcgqj0Yd5T9n/sPAWgMpW6CsQdYpTFOWip6qVauyZMkS9u3bx/bt22nbti0At2/fpmDBggYNKMQ/OdhYMaeHF8dvPOK7fVeyv8LEJ9oPWE9vbeZlIYQ6Nvmg2xK4cQhCF2R7dfHJ8fjt96Nm4Zq8X+V9AwQUpixLRc+MGTP49ttvadasGe+88w5eXl4ArFu3Ln3YS4icVLeUK581LsOcbRe4EPU4eyvbEQBPoqHrIrDIfCsEIYSBlWgAPgO0Iefos9la1dyjc7mfcJ8pvlOwlPPb7GV6YFOv11OmTBkiIiJISUnBxcUl/Wuff/65zNAscs2wVhX461w0w1eE8+cXPlhbZqGGv7wLDn+n3ThZUC57C2E0mvvBhW2wpi98uiNL99mF3g5l+fnljPUeSwmnHJ7fS5iETP8rodfrKVeuHJGRkc8UPAClSpWicOHCBgsnxKvYWVsyp4cXZ+7EsmhXFm56TIiBtQOgdBOo96nhAwohss7aDrothsiTWkuYTHqc9Bj/EH+8PbzpVbFXDgQUpijTRY+FhQXly5fn/v37OZFHiEzx8izAF83KMv+vi5y6FZO5D28ZqxU+XRaCRZZGeoUQOalYHWg8TJsw9E54pj468/BMHic9ZpLvJCx0cn4LTZa+E6ZPn86XX37JqVOnDJ1HiEwb+EZ5yrs7MmJlOIkpGXza4/wWCPsF2k6FAnLZWwij1WQkFKoMa/pBSmKGPrLnxh6CLgUxst5IiubPvSamwvhlqejp06cPhw4dwsvLC3t7e1xdXZ95CZGbbKwsmNvTi8t3n/D1jouv/0D8A1g/CMr//9T3QgjjZWWjPc117wLsnv7axR8lPGJC6AQaF2tMt3LdciGgMCVZmqFp3rx5Bo4hRPZULuLEkJYVmLPtPK2quFOrhMvLF970pfYbY6dvcqWjsxAimzyqQbPRsCsQKnWA4nVfuujUQ1NJSk1igs8EdHJ+i3/IUtHzwQc52wVXiKz4V5MybDsTxfCV4Wwa1Bg76xc8nno6CE6tgjeXgVORXM8ohMgi3yFwfpP2NFfffWBt/9wi265tY/PVzUxvPJ3CDvJQjXhelu/uunz5Mn5+frzzzjtER0cDsHnzZk6fPm2wcEJkhpWlBXN61ODmw6fM2nr++QWeRMPGYVC5E1TvkfsBhclKTU0lLYeaYIoMsrTSWsTE3ICdk5/78r2n95hyYAotS7Skfen2CgIKU5ClomfPnj1Ur16dgwcP8ueff/LkyRMAwsPDCQgIMGhAITKjXGFHRrapyA/BVzl45X+eMNTrYcNQ7f87fCXDWiZuy5YtNGrUiAIFClCwYEE6duzI5cvatAU+Pj6MGjXqmeXv3r2LtbU1e/fuBSAxMZERI0ZQrFgx8uXLh7e3N7t3705f/qeffqJAgQKsW7eOKlWqYGtrS0REBIcPH6ZVq1a4ubnh7OxM06ZNOXbs2DPbOnfuHI0aNcLOzo4qVaqwY8cOdDodQUFB6cvcuHGDnj17UqBAAVxdXenSpQvXrl3Lkb+rPKVQBXhjPBxYBNeC09/W6/VMDp2MTqfDr4GfDGuJl8pS0TN69GimTJnC9u3bsbGxSX//jTfe4MCBAwYLJ0RWfORbmrolXRixKpy4xBTtzRMr4NwG6DgP8hdSms+oJcXD7bDcfyXFZypmXFwcw4YN48iRI+zcuRMLCwu6detGWloavXv35o8//kCv/29ftuXLl1O0aFEaN24MwIABAwgNDeWPP/7gxIkT9OjRg7Zt23Lx4n9vhI+Pj2fGjBl89913nD59msKFC/P48WM++OAD9u/fz4EDByhfvjzt27fn8WNtVvDU1FS6du2Kg4MDBw8eZOnSpYwbN+6Z7MnJybRp0wZHR0f27dtHcHAw+fPnp23btiQlJWXq78EsNegHJRpqrWMStV+4N1zZwF83/mJ8g/EUtJdWSOLldPr//cmQQfnz5+fkyZOULl0aR0dHwsPDKVOmDNeuXaNSpUokJCTkRFaDiI2NxdnZmZiYGJycnFTHETnk+v042s7bx5u1ixHYoiAsbAAVWsNb36mOZjSSk5O5e/cuhQoVwtr6/2e7vR0GS5vmfpjP90DRmln++L179yhUqBAnT57E3d2dokWL8tdff6UXOT4+PjRp0oTp06cTERGRPqt80aL/fZy5ZcuW1K9fn6lTp/LTTz/x0UcfERYWlt5m50XS0tIoUKAAv/32Gx07dmTLli106tSJGzdu4OHhAWhNmVu1asWaNWvo2rUrv/zyC1OmTOHs2bPpVySSkpIoUKAAQUFBtG7d+rntvPBYmbMHV2CxL3i9TVTz0XRb240mnk2Y3vj1T3cJ85alG5kLFCjAnTt3KF269DPvHz9+nGLFihkkmBDZUbJgPsa2r8T4tacYHj0WV2t7aDdTdSzj51ZBK0BUbDcTLl68iL+/PwcPHuTevXvp99tERERQrVo1Wrduza+//krjxo25evUqoaGhfPvttwCcPHmS1NRUKlR4dpuJiYnPNEy2sbGhRo0azywTFRWFn58fu3fvJjo6mtTUVOLj44mIiADg/PnzeHp6phc8wHP9CMPDw7l06RKOjo7PvJ+QkJA+RCdew7UMtJ6MfuNwAlJvYmdlx5j6Y1SnEiYgS0XP22+/zahRo1i5ciU6nY60tDSCg4MZMWIEffr0MXRGIbKkt3dJEg/+iOudvcR1/518DjKH1GvZOGTriktu6dSpEyVLlmTZsmUULVqUtLQ0qlWrlj481Lt3bwYNGsT8+fP57bffqF69OtWrVwfgyZMnWFpacvToUSwtn33CL3/+/On/b29v/9y9IR988AH379/n66+/pmTJktja2tKwYcNMDUs9efKEOnXq8Ouvvz73tUKFZOg1w+p+wuozvxL86BwLG8/E2dZZdSJhArJU9EydOpX+/fvj6elJamoqVapUITU1lXfffRc/Pz9DZxQiSyxiIvg4bhmr9W8QerYYs6upTiQM4f79+5w/f55ly5alD1/t37//mWW6dOnC559/zpYtW/jtt9+e+WWsVq1apKamEh0dnf75jAoODmbRokW0b689HXTjxg3u3buX/vWKFSty48YNoqKicHd3B+Dw4cPPrKN27dosX76cwoULyxB7NtyKu80syzjejE2kyYn1UKad6kjCBGTpRmYbGxuWLVvG5cuX2bBhA7/88gvnzp3jP//5z3O/OQmhRFoarO2PhYMrurZTWXX0JtvPRKlOJQzAxcWFggULsnTpUi5dusRff/3FsGHDnlkmX758dO3alfHjx3P27Fneeeed9K9VqFCB3r1706dPH/7880+uXr3KoUOHmDZtGhs3bnzltsuXL89//vMfzp49y8GDB+nduzf29v+dL6ZVq1aULVuWDz74gBMnThAcHJz+i+DfV4169+6Nm5sbXbp0Yd++fVy9epXdu3czaNAgbt68aai/pjwtTZ/G+ODxONsV4Mv6oyDsVzi3SXUsYQKy1YWtRIkStGvXjh49elC+fHlDZRIi+w4vg2v7oMsCujWoRItKhRnz50kexsnTMabOwsKCP/74g6NHj1KtWjWGDh3KrFmznluud+/ehIeH07hxY0qUeLa/2o8//kifPn0YPnw4FStWpGvXrhw+fPi55f7p+++/5+HDh9SuXZv333+fQYMGUbjwfyfBs7S0JCgoiCdPnlCvXj0+/fTT9Ke37OzsAHBwcGDv3r2UKFGCN998k8qVK/PJJ5+QkJAgV34y6Pdzv3M48jCTfSeTv87HUKEtrB+stZgR4hWy9PQWaCf/V199lf6IZ/ny5RkyZAiffvqpQQMamjy9ZQbuXYIljaDWe9BhNgDRsQm0+movjcu7seDd2ooDGgd5Iih3BAcH06hRIy5dukTZsmWztA45Vv91LeYaPdb3oFv5boz1Hqu9+TgSFnpD2Tegx49qAwqjlqV7evz9/Zk7dy4DBw6kYcOGAISGhjJ06FAiIiKYNGmSQUMKkWFpqdr8HY4e0Gpi+tuFneyY1KUqg/8Io22123SsIZ2XRc5Ys2YN+fPnp3z58ly6dInBgwfj6+ub5YJH/FdqWip+wX4UdijMkNpD/vsFRw/oMAdWf6LNuF7tTWUZhXHLUtGzePFili1b9sw4eefOnalRowYDBw6UokeoEzIfbh6Gj7eATb5nvtTZqyhbT0cyPugU3qULUsjRVlFIkZc9fvyYUaNGERERgZubGy1btmTOnDmqY+UJ/z7zb07cPcHP7X7Gwdrh2S9WewvOroONw6FUI8gvvbfE87J0T09ycjJ16z7f5bZOnTqkpKRkO5QQWRJ9VuvC7DMASjR47ss6nY7JXaphodMx5s+TZHFkV4hX6tOnDxcuXCAhIYGbN2/y008/PTP/j8iaSw8vMf/4fD6o+gG1Ctd6fgGdDjrMBQtL7f4eOb/FC2Sp6Hn//fdZvHjxc+8vXbqU3r17ZzuUEJmWmgxr/gUupaH5y6dNKJjflqlvVmfH2Sj+PHYrFwMKIbIqOS2ZccHj8HT0ZECtAS9fMJ+b1mrm/CYI/yPX8gnTkeHhrf99JFSn0/Hdd9+xbds2GjTQfqM+ePAgERERMjmhUGPfXIg8BZ9uB2u7Vy7apqoH3WoVY8L60/iUK0gRZ/tXLi+EUOu7k99x/sF5fm3/K7aWrxmWrtwRarwNm0dB6SbgLF0CxH9luOg5fvz4M3+uU6cOQPq06W5ubri5uXH69GkDxhMiA26Hwd6Z0HgYFKuToY9M6FSVkMv3GLnqBP/+uL50ZRbCSJ29f5al4Uv5tPqnVHWrmrEPtZsOV/fAugHw3p/a0JcQZOORdVMlj6znMSmJsLQZ6Czhs7/AyibDH919PpoPfzzM1G7Vedf71fOz5EXyGLTpMNdjlZSaRK8NvbCysOK39r9hbZmJfb+4A359SxvuqvtRjmUUpiVbkxMKodzuaXDvInRbkqmCB6BZxcK8U9+TKRvPcONBfA4FFEJk1eLwxVyLvcYU3ymZK3gAyreE2h/A1nHw8FqO5BOmJ0uPrCckJDB//nx27dpFdHR0eofjvx07dswg4YR4pRuHIfhraD4OPLLWWGtchyrsvXCPESvD+f2zBlhYyGVwIYxB+N1wfjj1AwNqDqCia8WsraRNIFzZBUH94YP1YCG/55u7LH0HfPLJJ8ycOZOSJUvSsWNHunTp8sxLiByXFA9BfaFoLfAdkuXV5Le1YlaPGhy8+oCfQq4ZLJ7IOc2aNWPIkCEv/bpOpyMoKCjD69u9ezc6nY5Hjx5lO5swjKcpT/Hb70fVglX5qFo2hqZsHaHLQri+Hw59a7iAwmRl6UrPhg0b2LRpE76+vobOI0TG/DUZYm7C27+DZZa+jdP5lHXjQ59SzNhyjqYVC1G2UH4DhRQq3LlzBxcXF9UxRDZ8c+wb7sTd4es3vsbKInvnN6WbgHdf2DEByrUEN+kTac6ydKWnWLFiODo6GjqLEBlzbT8cWARvjIdCFQyyylFtK1G0gD0jVoaTmmZW9/bnOR4eHtjaymzbpupw5GF+Pfsrg2oNooxzGcOstEUAOBXTWtSkpRpmncIkZanomTNnDqNGjeL69euGziPEqyU+gaAvoIQPNOhnsNXa21gyu0cNwm88YuneKwZbr8gZaWlpjBw5EldXVzw8PJgwYUL61/45vBUSEkLNmjWxs7Ojbt26BAUFodPpCAsLe2adR48epW7dujg4OODj48P58+dzZ2dEuvjkeMYHj6dW4Vq8V+U9w63YxgG6LoZbRyHkG8OtV5icLF03rFu3LgkJCZQpUwYHB4fnHqF88OCBQcIJ8Zzt4yHuLvQJ0qabN6A6JV35rEkZvtp+gTcqFaaih/ldzXya8pSrMVdzfbulnUtjb5XxSSJ//vlnhg0bxsGDBwkNDeXDDz/E19eXVq1aPbNcbGwsnTp1on379vz2229cv379pfcDjRs3jjlz5lCoUCH69u3Lxx9/THBwcHZ2S2TSnCNzeJDwgGWtlmGhM/BNxyW8wWcg7JoK5duAexXDrl+YhCwVPe+88w63bt1i6tSpuLu7y8RuIndc2glHftC6Kbsa6LL3PwxtWYG/zkYzbEUYQf19sbY0r6c9rsZcpdeGXrm+3eUdl1OlYMb/EapRowYBAQEAlC9fngULFrBz587nip7ffvsNnU7HsmXLsLOzo0qVKty6dYvPPvvsuXUGBgbStGlTAEaPHk2HDh1ISEjAzu7VM3wLwwi5FcKKCyvw8/bD08kzZzbSbCxc2Ka1rPnsL8jsY/DC5GWp6AkJCSE0NBQvLy9D5xHixZ4+gnUDoUwzqPtJjm3GztqSuT1r0nVRMAv+usTQVoa5Z8hUlHYuzfKOy5VsNzNq1KjxzJ+LFClCdHT0c8udP3+eGjVqPFO41K9f/7XrLFKkCADR0dGUKGF+E1fmttikWPxD/GlYpCE9K/bMuQ1Z20G3xbCsBeydDc3H5Ny2hFHKUtFTqVIlnj59augsQrzcljGQ+Bg6L8jxKeWrF3emf/NyLNx1iZaV3ale3DlHt2dM7K3sM3XFRZV/DqnrdLrn5gvLzjr/vnqd3XWKjJlxaAZxyXFM8p2U8yMHRWtBky9h32yo2Fb7szAbWbp2P336dIYPH87u3bu5f/8+sbGxz7yEMKhzmyD8N2g7DQrk0GXvfxjQvBwVPRwZvjKMxBR52sNUVaxYkZMnT5KYmJj+3uHDhxUmEv+0K2IX6y6vY1T9UXjk88idjTYZAYWrwJp+WisbYTayVPS0bduW0NBQWrRoQeHChXFxccHFxYUCBQrI/BjCsOIfwPrBUKEt1Oyda5u1sbJgTk8vrt6L46vtF3Ntu8Kw3n33XdLS0vj88885e/YsW7duZfbs2QByL6IReJTwiImhE2lavCldyubixLaW1lrrmgeXtRubhdnI0vDWrl27DJ1DiBfbOBxSk6DT17neKbmShxNDW1Vg9tbztKriTp2SUtCbGicnJ9avX0+/fv2oWbMm1atXx9/fn3fffVduUDYCgQcDSdGnENAwIPeLUPeq0GyMNtFppQ7g+eJ7vUTeIl3WhfE69Ses+gje+h6qd1cSISU1je5LQol5msymQY2xtzHsY/IqmWvn7l9//ZWPPvqImJgY7O0z/pi8SnnxWG25toUv93zJzCYzaVe6nZoQqSnwY1vtinLf/dp8PiJPy/LzuPv27eO9997Dx8eHW7duAfCf//yH/fv3GyycMGOPo7SrPFW6QLW3lMWwstSGuW4/esqMLeeU5RBZ9+9//5v9+/dz9epVgoKCGDVqFD179jSZgicvuvf0HoEHAmldsjVtS7VVF8TSSpu0MPYW7JyoLofINVkqelavXk2bNm2wt7fn2LFj6TcJxsTEMHWqjI+KbNLrYcMQbfLBDnNzfVjrn8oWys/ItpX4KeQaIZfvKc0iMi8yMpL33nuPypUrM3ToUHr06MHSpUtVxzJber2eiaETsdBZ4NfAT/29VW7loeUEOLgEru5Vm0XkuCwVPVOmTGHJkiUsW7bsmUutvr6+HDt2zGDhhJkK/wPOb4KO8yCfm+o0AHzkUwrv0q6MXHWCJ4kpquOITBg5ciTXrl0jISGBq1ev8tVXX+HgIMMYqqy/sp7dN3bj39AfFzsjuU+u/r+gZCNY21+bGkPkWVkqes6fP0+TJk2ee9/Z2ZlHjx5lN5MwZzG3YPMoqPE2VO6oOk06Cwsds7p78SAuicCNZ1XHEcIkRcZFMv3gdDqV6USLEi1Ux/kvCwvouhDi7sM2P9VpRA7KUtHj4eHBpUuXnnt///79lCmTM+0BhBnQ62HdAO1mwnbTVad5TomCDozrUJnfD0Ww+/zzs/+aKjN7lsEk5YVjpNfrCQgJwN7anlH1R6mO8zyXUtBmChz9CS7uUJ1G5JAsPbL+2WefMXjwYH744Qd0Oh23b98mNDSUESNGMH78eENnFObi6I9w+S/ovRrsjeSy9z+8W78EW05FMnr1SbYOaYKzg+k+SWNpaYlOp+Px48c4Ojqqv7dCPEev15OamkpsbCw6nQ4rqyz9yDYKKy+sJOR2CItbLsbZ1khnOa/zEZxdr7W8+SLEaH8OiazL0iPrer2eqVOnMm3aNOLj4wGwtbVlxIgRTJ482eAhDUkeWTdSD67CYl/t0fTO36hO80q3Hz2lzby9tKrsztxeNVXHyZbExEQePHiQJ64k5GU2NjYUKFDAZIueG49v8Na6t+hQpgMBDQNUx3m1mFuwqCFUbAdvfqs6jTCwbM3Tk5SUxKVLl3jy5AlVqlQhf/78hsyWI6ToMUJpafBzJ4iJgH4hYOuoOtFrrTp6kxErw/n2/Tq0qZpLU+fnkLS0NFJTpdWGsbKwsMDCwsJkr8Sl6dP4ZOsn3Im7w+rOq8lnnU91pNcL+x2C+kKvX43q3kKRfZn6teHjjz/O0HI//PBDpkIsXLiQWbNmERkZiZeXF/Pnz39pJ+T/9ccff/DOO+/QpUsXgoKCMrVNYUQOfQvX98MH602i4AF4q3YxtpyKZNyak9Qr5YprPhvVkbLs739UhcgJv539jSNRR/ihzQ+mUfAAeL0NZ9dpU2eUaAj5CqpOJAwkUz/pfvrpJ3bt2sWjR494+PDhS1+ZsXz5coYNG0ZAQADHjh3Dy8uLNm3aEB396htFr127xogRI2jcuHGmtieMzL2LsGMCePeF0s8/EWisdDodU9+sRkqaHr+gkzI8JMQLXI25yrxj8+hduTf1POqpjpNxOp02ZUZaKmwcqj1kIfKETA1v9e/fn99//52SJUvy0Ucf8d577+Hq6pqtAN7e3tSrV48FCxYA2qV2T09PBg4cyOjRo1/4mdTUVJo0acLHH3/Mvn37ePTo0Uuv9CQmJj7TYTk2NhZPT08Z3jIGeWAK+A0nbjPgt+N8804tOnsVVR1HCKORkpbCB1s+ICYxhpWdVmJvZYIzYBtBKxxhWJm60rNw4ULu3LnDyJEjWb9+PZ6envTs2ZOtW7dm6TfdpKQkjh49SsuWLf8byMKCli1bEhoa+tLPTZo0icKFC/PJJ5+8dhvTpk3D2dk5/eXp6ZnpnCKHhHwDt45q08CbYMED0LFGUTrUKIL/2lNEP05QHUcIo/HT6Z84de8UU3ynmGbBA1DtTaj6JmwaAY8jVacRBpDpgXxbW1veeecdtm/fzpkzZ6hatSpffPEFpUqV4smTJ5la171790hNTcXd3f2Z993d3YmMfPE32P79+/n+++9ZtmxZhrYxZswYYmJi0l83btzIVEaRQ6JOw+5p4DMQSnirTpMtk7tUw8rCgjGrZZhLCIALDy+wKGwRH1b9kJqFa6qOkz0d5oCFNawfLMNceUC27l78+4mCv+eSyGmPHz/m/fffZ9myZbi5Zaw9ga2tLU5OTs+8hGKpybCmL7iWhWZjVafJNtd8Nkx7szo7z0Wz6uhN1XGEUCo5LRm//X6UdCpJ/5r9VcfJPgdX6PQ1XNgCYb+pTiOyKdNFT2JiIr///jutWrWiQoUKnDx5kgULFhAREZHpR9bd3NywtLQkKirqmfejoqLw8Hj+MeDLly9z7do1OnXqhJWVFVZWVvz73/9m3bp1WFlZcfny5czujlBh72ztSk+3xWBtpzqNQbSq4s5btYszaf0Zbj96qjqOEMosO7GMiw8vMqXRFGwsTfepxmdUag9e78KW0RAjv9iYskwVPV988QVFihRh+vTpdOzYkRs3brBy5Urat2+fpUdebWxsqFOnDjt37kx/Ly0tjZ07d9KwYcPnlq9UqRInT54kLCws/dW5c2eaN29OWFiY3K9jCm4fh72zoMmXULSW6jQG5d+pCvlsrRi1+oQMcwmzdPr+aZaeWMpnNT6jasGqquMYVttp2pQaa/vLMJcJy9TTWxYWFpQoUYJatWq9cqKsP//8M8MBli9fzgcffMC3335L/fr1mTdvHitWrODcuXO4u7vTp08fihUrxrRp0174+Q8//PCVT2/9k0xOqFByAixtBpbW8Nlf2n/zmL0X7tLnh0NM6VqN9xqUVB1HiFyTmJrI2xvextrCml87/Iq1Rd47v7m0E355U7vPp96nqtOILMjU5IR9+vQx+KygvXr14u7du/j7+xMZGUnNmjXZsmVL+s3NERERMnFaXrF7Kjy4DJ/vzpMFD0CTCoV417sEUzedpUn5QpQoaJpPpQmRWQvDFnI99jrLOy7PmwUPQLkWUPdj2OYPZVuAa2nViUQmZasNhSmSKz2KRBzU5uR5Yzw0HqY6TY56kphCu6/3UsTJnj8+b4CFhWm2DxAio8Kiw/hgywcMqjWIT6q/fioRk5b4BBb7gFMx+HAjyC/lJkWOlsh5SfEQ1A+K1QGfQarT5Lj8tlbM6u7FoWsP+CH4quo4QuSopylP8Qv2o5pbNT6s+qHqODnPNj90XQQRoXBwseo0IpOk6BE5b+dEiL2lTUJoaZpdojOrQZmCfOxbmllbz3MpOnPzVwlhSr4+9jVRcVEE+gZiaWGpOk7uKNUIGvSDnZPg7gXVaUQmSNEjctbVvXBwCbScAG7lVafJVSPbVqRYAXuGrwwnJTVNdRwhDO7QnUP8evZXBtceTCnnUqrj5K4W/uDsqXVjT01RnUZkkBQ9IuckPoag/lCyEdT/l+o0uc7O2pLZPb04efMR3+69ojqOEAYVlxzH+ODx1HWvy7uV31UdJ/dZ20O3Jdo0HCFfq04jMkiKHpFzto6D+PvQZYHZ3uxXu4QL/2palnk7LnD2TqzqOEIYzKzDs3iY+JDJvpOx0Jnn+U3xuuA7GHZNg8hTqtOIDDDT71SR4y7ugGM/Q5spZv9Y55CW5Snjlp/hK8JJSpFhLmH69t/az+qLqxlRdwTFHYurjqNWszHa0H1QX0hJUp1GvIYUPcLwnj6EdQOg7BtQ5yPVaZSztbJkTk8vLkQ9ZsFfF1XHESJbYhJjCAgOwKeoDz0q9FAdRz0rW22YK/qsNtu8MGpS9AjD2zxae0y983ww8GSWpqpaMWcGvlGehbsvc+LmI9VxhMiyGYdm8DTlKRN9Jhp8slqTVcQLmoyEfXPg1jHVacQrSNEjDOvsBjjxB7SbDs5mftn7H75oXpYqRZwYtiKchORU1XGEyLSdETtZf2U9o71H45Hv+abQZq3xMPCoDmv6ai13hFGSokcYTtw92DAEKrYHr3dUpzE61pYWzOnpRcT9eL7aLnN7CNPyIOEBk0In0cyzGZ3KdFIdx/hYWmvDXA+vwq5A1WnES0jRIwxDr4eNwyAtBTrOk2Gtl6jg7siw1hVYuu8KR649UB1HiAzR6/VMOTCFNH0aAQ0DZFjrZQpXhubjIGQ+RBxQnUa8gBQ9wjBOrYYza6HDXHB0V53GqH3WuAy1PAswYmU48UkyqZkwfluubWH79e2MazAON3s31XGMm89AKF5Pa72TFKc6jfgHKXpE9j2OhI3DoWo3qPam6jRGz9JCx5yeNYmMTWDG5nOq4wjxSnfj7zLlwBTalmpL21JtVccxfhaW2jBX7B3YMUF1GvEPUvSI7NHrYf1gsLSB9nNUpzEZpd3yMbptJX4OvU7IpXuq4wjxQnq9nomhE7G2sGac9zjVcUxHwbLQaiIcWgpX9qhOI/6HFD0ie8J+hQtboNPXkK+g6jQmpU/DUjQsU5AvV53gcUKy6jhCPCfoUhB7bu4hoGEABewKqI5jWup9BqUaw9r+kCCzsRsLKXpE1j26AVvGgNe7UKm96jQmx8JCx8zuNXgUn0TgxrOq4wjxjDtP7jDz8Ew6l+1M8xLNVccxPRYW0GWhNlnrNrlKZiyk6BFZo9drsy7b5Ie201SnMVmerg74dazCH4dvsOtctOo4QgDasJZ/iD/5rPMxqv4o1XFMl0tJaBMIx/4NF7apTiOQokdk1ZHv4cpurZmofQHVaUza2/U8aVqhEKNWn+BRvPTuEeqtOL+CA3cOMMlnEk42TqrjmLbaH0C5lrBuIMTLNBWqSdEjMu/BFdg2XuurVa6F6jQmT6fTMeOtGiQkpzJh3WnVcYSZuxF7gzlH59CzQk98ivmojmP6dDqtJU/KU9gsV81Uk6JHZE5aKgT1h3yFoPVk1WnyDA9nOyZ0rkpQ2G22nLqjOo4wU2n6NPyC/XC1c2V43eGq4+QdTkWh3Sw4uQLOrFOdxqxJ0SMy58BiiAiBrovA1lF1mjylW61itK7izrg1p7j/JFF1HGGGfjnzC8ejjzPZdzIO1g6q4+QtNXpCpY6wYajWskcoIUWPyLi752HnJGjwBZRqpDpNnqPT6QjsVp00vZ5xa06h1+tVRxJm5ErMFb4+9jW9K/emnkc91XHyHp0OOn4F6LUehXJ+KyFFj8iY1BSte3ABT2jhrzpNnlXI0ZbAbtXZcjqSdeG3VccRZiIlLQW//X4UzV+UwbUHq46Td+UvrLXqObseTq5SncYsSdEjMiZ4HtwJg65LwNpedZo8rX31InTyKor/2tNExSaojiPMwI+nfuT0/dMENgrEzspOdZy8rWpXqNYdNo3QWlWIXCVFj3i9yJOwezr4DgZPueydGyZ1roqNlQWjV5+QYS6Ro84/OM+i8EV8XO1jahSqoTqOeWg/C6xsYf0gGebKZVL0iFdLSYI1/cCtPDQbozqN2XDJZ8P0N6uz6/xdVh65qTqOyKOSU5MZt38cpZ1L08+rn+o45sPBFTp9Axe3wfFfVKcxK1L0iFfbOxPuntW6BlvZqk5jVlpUdqdHneJM2nCGmw/jVccRedC3J77l8qPLBPoGYmNpozqOeanYFmq+p7XyeRShOo3ZkKJHvNyto7BvLjQZCUW8VKcxS+M7VcHJzoqRq06QliaXwYXhnLp3iu9OfsfnXp9TuWBl1XHMU9upYOesNSVNS1OdxixI0SNeLDlBG9byqA6Nh6lOY7ac7KyZ2d2LkMv3+eXgddVxRB6RmJrIuP3jqOhakU+rf6o6jvmyc9Za+Vzdq7X2ETlOih7xYrumwMOr2rCWpbXqNGatUXk33mtQgmmbznHtXpzqOCIPWHB8ATce3yDQNxBrCzm/lSrbHOp9Ctv94f5l1WnyPCl6xPOuh0LIAmg+DgrLZW9jMKZdZQo52jJiZTipMswlsuF49HF+Pv0zA2sNpJxLOdVxBEDLidocPkFfaK1+RI6Rokc8KykOgvpB8XrgM1B1GvH/8tlaMbuHF0cjHvLD/quq4wgTFZ8cz7j94/Aq5EWfKn1UxxF/s80PXRfDjYNwYJHqNHmaFD3iWdsD4HGkNqxlYak6jfgf9Uu78olvaWZtO8/FqMeq4wgTNO/YPO7G32VKoylYyvltXEr6QMP+sHMyRJ9TnSbPkqJH/NeV3XB4GbSaCAXLqk4jXmBEm4p4utgzfGU4KanytIfIuAN3DvD7ud8ZUmcIJZ1Kqo4jXuQNP3ApCUF9tdY/wuCk6BGahFhYOwBKNYZ6n6lOI17CztqSOT1rcupWDIt3y02PImOeJD3BP9if+h71eafSO6rjiJexttda/dwJh/1fqU6TJ0nRIzRbx8LTh9BlIVjIt4Uxq+lZgH7NyvLNXxc5fTtGdRxhAmYdmUVMYgyTfCdhoZPz26gVrwONhsGeGXDnhOo0eY589wu4sBWO/wfaBGqXVoXRG9SiPGUL5Wf4inCSUmSYS7zc3pt7+fPin4ysN5Ji+YupjiMyoukoKFRRe6gkJUl1mjxFih5zF/8A1g2Cci2h9geq04gMsrWyZE5PLy5FP+GbnRdVxxFGKiYxhgkhE2hUrBFvln9TdRyRUVY22sMkd89rV3yEwUjRY+42j4SUp9B5Puh0qtOITKha1JnBLcqzaPclwm48Uh1HGKFph6aRkJrAhIYT0Mn5bVo8qmtXfPbPhZtHVafJM6ToMWdn1sLJldBuFjgVVZ1GZEG/ZmWpVsyZ4SvCSEiWSc3Ef+24voONVzYypv4Y3PO5q44jsqLRUChSU3uaK/mp6jR5ghQ95urJXdgwFCp1hBo9VacRWWRlacGcHl7cePiUOdvOq44jjMT9p/eZfGAyLUq0oGOZjqrjiKyytNKGuR5eh7+mqE6TJ0jRY470etg4VPv/jl/JsJaJK+/uyIjWFfhu/1UOXX2gOo5QTK/XM+XAFPR6PeMbjJdhLVNXqCK0GA+hC+F6iOo0Jk+KHnN0chWcXQ8d5mr9XoTJ+6RRGeqUcGHEynDiEmVSM3O26eomdkTswK+BHwXtC6qOIwyhwRfg6a09zZX4RHUakyZFj7mJvQObhkO17lC1q+o0wkAsLXTM7uHF3ceJTN8sU9ibq+j4aAIPBtKudDtal2qtOo4wFAtL6LoInkTDjgDVaUyaFD3mRK+H9YPAyg7az1KdRhhYKbd8jGlfif8cuM7+i/dUxxG5TK/XMyFkAraWtozzHqc6jjC0gmWh1SQ4/B1c3qU6jcmSosecHP8PXNwGnb4BB1fVaUQOeM+7JD5lCzJyVTixCcmq44hctObSGvbd2sdEn4k42zqrjiNyQt1PoHRTrWVQgszGnhVS9JiLRxGwZSzUfA8qtlWdRuQQCwsdM7vXIDYhhSkbzqiOI3LJ7Se3mXl4Jt3KdaNJ8Saq44icYmGhtQpKiNF+notMk6LHHKSlwdr+YOcMbaeqTiNyWHEXB8Z3rMyKIzfZeTZKdRyRw9L0afgH++No48iX9b5UHUfktAKe0HYahP0C57eoTmNypOgxB0e+h6t7ocsCrfAReV7Pup40r1iI0X+e5GGc9O7Jy5afX87ByINM8pmEo42j6jgiN9R6D8q31u7RjJdpKjJDip687v5l2O4P9T6Fss1VpxG5RKfTMf2tGiSlpBGw7rTqOCKHRMRG8NXRr+hVsRcNizZUHUfkFp1OuzczJRE2ydW9zJCiJy9LS4WgL7S5eFpOVJ1G5DJ3JzsmdanKuvDbbDp5R3UcYWCpaan4BfvhZu/GsDrDVMcRuc2pCLSfDadWwekg1WlMhhQ9eVnoQrhxELouBtv8qtMIBTp7FaVtVQ/8gk5x70mi6jjCgP5z5j+ERYcxxXcKDtYOquMIFap3h8qdYeMwbQ4f8VpS9ORV0ee0Xi0N+0NJH9VphCI6nY4p3aqhA8b+eRK9Xq86kjCAy48uM//4fPpU6UNt99qq4whVdDqtlRA6rZeinN+vJUVPXpSaonXldSkJb/ipTiMUc8tvS2C3amw7E0VQ2C3VcUQ2paSlMG7/OIo5FmNArQGq4wjV8rlphc+5DXBiheo0Rk+Knrxo/1dwJxy6LgFre9VphBFoW60IXWsWJWDtaSJjElTHEdnw/cnvOffgHIG+gdhZ2amOI4xBlc5Qvad2U3PsbdVpjJoUPXnNnROwZzo0GgbF66hOI4zIxM7VsLO2ZNTqEzLMZaLOPTjHkvAlfFztY6oXqq46jjAm7WeCjQOsGyjDXK8gRU9ekpKodeEtVAmajlKdRhgZZwdrZrxVgz0X7rL88A3VcUQmJaUmMW7/OMoUKEM/r36q4whjY+8CnefDpR1w7GfVaYyWFD15yZ4ZcPc8dFsCVjaq0wgj1LxSYXrV9WTyhjPceBCvOo7IhCXhS7gSc4WpjaZibWmtOo4wRuVbQe0+sHUcPLyuOo1RkqInr7h5RLuXp+ko8JDL3uLl/DpWpoCDDSNXnSAtTS6Dm4ITd0/w/anv6efVj4quFVXHEcasdaB21Wdtf60FkXiGFD15QfJTWNMXitSERkNVpxFGztHOmlndaxB65T7/Dr2mOo54jYSUBMbtH0cV1yp8XO1j1XGEsbNz0pqSXtsHh5epTmN0pOjJC/6aonVR77YELK1UpxEmwKecGx80LMn0Lee4ei9OdRzxCvOPz+f2k9sENgrEykLOb5EBZZpC/c9hewDcu6Q6jVGRosfUXQ/RZl5uMR4KyWVvkXGj2lXCw8mO4SvCSJVhLqN0NOoo/znzHwbVHkSZAmVUxxGmpOUErVVFUD+tJZEAjKToWbhwIaVKlcLOzg5vb28OHTr00mWXLVtG48aNcXFxwcXFhZYtW75y+Twt8Yn2De3pDQ2+UJ1GmBgHGytm9/Di+I1HfLfviuo44h/ik+Px2+9HrcK1eK/ye6rjCFNjk09rQXTzMITMV53GaCgvepYvX86wYcMICAjg2LFjeHl50aZNG6KjX9xHZPfu3bzzzjvs2rWL0NBQPD09ad26NbdumeFMs9v9tX4rXReBhaXqNMIE1S3lymeNyzBn2wUuRD1WHUf8j7lH53I/4T5TfKdgKee3yIoSDcBnIOwKhOizqtMYBZ1e8Sxl3t7e1KtXjwULFgCQlpaGp6cnAwcOZPTo0a/9fGpqKi4uLixYsIA+ffq8dvnY2FicnZ2JiYnByckp2/mVufwX/Keb1mW3/meq0wgTlpCcSsf5+7G3tuTPL3ywtlT+u5DZC7kdwr+2/4tx3uN4u9LbquMIU5acAEubgpUtfLoTzHy6A6U/3ZKSkjh69CgtW7ZMf8/CwoKWLVsSGhqaoXXEx8eTnJyMq6vrC7+emJhIbGzsMy+TlxADawdA6aZQ9xPVaYSJs7O2ZG5PL87ciWXRrsuq45i9x0mP8Q/2x7uINz0r9lQdR5g6azttmCvyFOybqzqNckqLnnv37pGamoq7u/sz77u7uxMZGZmhdYwaNYqiRYs+Uzj9r2nTpuHs7Jz+8vT0zHZu5baMhYRY7bFEC/mtXGRfjeIF6N+sLPP/usipWzGq45i1mYdn8iT5CZN9JmOhk/NbGECx2tB4OOydCbfDVKdRyqTPqOnTp/PHH3+wZs0a7Oxe3HhvzJgxxMTEpL9u3DDx6ffPb4GwX6DtNCiQBwo4YTQGvFGeCu6ODF8RTmKKPO2hwp4bewi6FMSoeqMokr+I6jgiL2nyJRSurD38kpKoOo0ySoseNzc3LC0tiYqKeub9qKgoPDw8XvnZ2bNnM336dLZt20aNGjVeupytrS1OTk7PvExW/ANYPwjKt4Za8jSHMCwbKwvm9PTiyr0nfL3jouo4ZudRwiMmhE6gSfEmdC3XVXUckddY2UC3b+HeRdg9TXUaZZQWPTY2NtSpU4edO3emv5eWlsbOnTtp2LDhSz83c+ZMJk+ezJYtW6hbt25uRDUOm0ZoFXqnb0CnU51G5EGVizgxpGUFluy5zLGIh6rjmJWpB6eSlJrEhIYT0Mn5LXKCe1VoPgaCv4Ybh1WnUUL58NawYcNYtmwZP//8M2fPnqVfv37ExcXx0UcfAdCnTx/GjBmTvvyMGTMYP348P/zwA6VKlSIyMpLIyEiePHmiahdyx+k1cGq19rSWk1z2FjnnX03KUL14AUasCOdpkgxz5Yat17ay+dpmxnmPo5BDIdVxRF7mMxiK1oKgvpBkfk2HlRc9vXr1Yvbs2fj7+1OzZk3CwsLYsmVL+s3NERER3LlzJ335xYsXk5SURPfu3SlSpEj6a/bs2ap2Iec9iYYNw6ByJ6jeXXUakcdZWVowp4cXtx49ZdbW86rj5Hn3nt5jyoEptCrZinal26mOI/I6SyvougRibsJfk1WnyXXK5+nJbSY3T49eD8vfg4gD8MUByC+/BYrc8d2+KwRuOsvvnzWgQZmCquPkSXq9niG7hhB2N4w1XdbgavfiqTeEMLjQhbB1LHy4EUo1Up0m1yi/0iNe48RyOLcBOn4lBY/IVR/7lqZeSVe+XBVOXGKK6jh50oYrG/jrxl/4N/CXgkfkLu9+UMIHgr6ARPOZjV2KHmMWcws2jYTqPaFKZ9VphJmxsNAxq0cN7j9JYuommcLe0CLjIpl2cBody3SkRckWquMIc2NhAV0XQtw92DZedZpcI0WPsdLrYd1AsLaH9jNVpxFmqmTBfIxpX5lfD0aw98Jd1XHyDL1ez4SQCdhb2TO6/uvb7QiRI1zLQOtJcPRHuLRDdZpcIUWPsTr2M1zeCZ3ng72L6jTCjL3nXYLG5d0YtfoEMU+TVcfJE1ZfXE3w7WAm+EzA2dZZdRxhzup+AmWaw9qB8PSR6jQ5TooeY/TwOmwdB7XehwqtVacRZk6n0zHjrRo8SUhh0vozquOYvFtPbjHr8CzeKv8WjYs3Vh1HmDudDrosgKQnsGXM65c3cVL0GJu0NFjbX7u602aq6jRCAFC0gD3+naqw+thNtp+Jev0HxAul6dMYHzyeArYFGFF3hOo4Qmici0Pb6RD+G5zbpDpNjpKix9gcWgrX9mnNRO1M4JF6YTa61ylOi0qFGfPnSR7GJamOY5J+P/c7hyMPM9l3Mvlt8quOI8R/1XwXKrSF9YO1lkd5lBQ9xuTeJdgxAep/DmWaqk4jxDN0Oh3T3qxOSloa49eeUh3H5FyLuca8o/N4t9K71C9SX3UcIZ6l00GnryEtGTYOV50mx0jRYyzSUrXut05FoOUE1WmEeKHCTnZM6lKNDSfusOHEbdVxTEZqWip+wX4UdijM4NqDVccR4sUcPbRWR6f/hFN/qk6TI6ToMRYh8+HmYei6GGzyqU4jxEt1qlGEDtWLMD7oFNGPE1THMQk/n/mZE3dPMKXRFBysHVTHEeLlqr0FVbpqV3se573796ToMQZRZ2BXIPgMgBINVKcR4pV0Oh2Tu1bD0kLH2D9PYWadbDLt0sNLLDi+gA+rfkitwrVUxxHi1XQ66DAXLCxhwxBtzrg8RIoe1VKTtW63LqWhuZ/qNEJkiGs+GwK7VWfH2Sj+PHZLdRyjlZyWzNj9YynhWIL+tfqrjiNExuQrCB3nwflNEP6H6jQGJUWPavvmQOQp6LYErO1UpxEiw9pU9eDNWsWYsP40d2Keqo5jlL47+R0XHl4gsFEgtpa2quMIkXGVO0KNt2HzKK0lUh4hRY9Kt8Ng7yxoPByK1VadRohMC+hUlXw2VoxcdUKGuf7hzP0zLA1fyqfVP6WqW1XVcYTIvHbTtXtM1w3IM8NcUvSokpIIa/pC4crQ5EvVaYTIEmcHa6a/VZ19F+/x+6EbquMYjaTUJMbtH0c5l3L8q8a/VMcRImvsXbRWSJf/0vpz5QFS9KiyexrcvwRdl4CVjeo0QmRZs4qFead+CaZsPMONB/Gq4xiFRWGLuBZ7jcBGgVhbWquOI0TWlW8JdT6ErX7w4KrqNNkmRY8KNw5D8NfQbDR4VFOdRohsG9ehMq75bBixMpy0tLxxGTyrwu+G8+PpH+lfsz8VXCqojiNE9rWeot3cvHaA1irJhEnRk9uS4rWntYrWAt8hqtMIYRD5ba2Y2b0GB68+4KeQa6rjKPM05Sl++/2oWrAqH1b9UHUcIQzD1hG6LILr++HQt6rTZIsUPblt5ySIuakNa1laqU4jhMH4lHXjQ59SzNhyjst3n6iOo8Q3x77hTtwdpjSagpWFnN8iDyndGLz7aq2S7l1UnSbLpOjJTVf3wcHF0MIfCsllb5H3jGpbiaIF7BmxMpxUMxvmOhx5mF/O/sKgWoMo41xGdRwhDK9FADgV01ompaaoTpMlUvTklsTHsPYLKOED3v1UpxEiR9jbWDK7Rw3Cbzxi6d4rquPkmrjkOMYHj6eOex3eq/Ke6jhC5AwbB21OuVtHIeQb1WmyRIqe3LJtPMTdh64LwUL+2kXeVaekK581KcNX2y9wPvKx6ji5Ys6ROTxIeMBk38lY6OT8FnmYZ33wGaQ9gRx1WnWaTJOzMzdc2qHNcdB6ErjKZW+R9w1tWYFSbg4MWxFGcqppP+3xOsG3gll5YSUj6o7A09FTdRwhcl7zseBaVptrLjVZdZpMkaInpz19BGsHQpnmUPcT1WmEyBV21pbM6VGTc5GPWfDXJdVxckxsUiz+If40LNKQHhV6qI4jRO6wsoVuiyH6DOydrTpNpkjRk9O2jIakJ9Blgda9VggzUb24MwOal2PhrkucvBmjOk6OmHFoBvHJ8UzynYROzm9hTorWgsYjtFZKt4+rTpNhUvTkpHMbIfx3aDsdnIurTiNErhvwRjkqejgyfGUYiSmpquMY1K6IXay7vI5R9Ufhkc9DdRwhcl+TEeBeFdb0g+QE1WkyRIqenBJ3H9YPhgptoea7qtMIoYS1pQVzenpx7V48X2033bk9/ulhwkMmhk6kWfFmdCnbRXUcIdSwtIZu38KDy7B7quo0GSJFT07ZNBzSUqDT1zKsJcxaJQ8nhrQqz9K9lzl6/aHqOAYReDCQFH0KAT4BMqwlzJt7Fe3G5pD5EHFQdZrXkqInJ5xaDafXQPvZ4CiXvYX4vHEZvDwLMGJlOE+TTHuYa8vVLWy9thU/bz/c7N1UxxFCPZ9BUKyONmlhknE3HZaix9AeR8HG4VClK1R7S3UaIYyClaUFs3t4cfvRU2ZsOac6Tpbde3qPKQen0Lpka9qWbqs6jhDGwcJSa60Uext2TlSd5pWk6DEkvV67j8fCCjrMlWEtIf5H2UL5GdW2Ej+FXCPk8j3VcTJNr9czMXQiljpL/Br4qY4jhHFxKwctA+DgEri6V3Wal5Kix5DCf4cLm6HjPMhXUHUaIYzOhz6l8C7tyshVJ3iSaFq9e9ZdXsfuG7sJaBiAi52L6jhCGJ/6/4KSjSCov9Z6yQhJ0WMoMTdh8yio8TZU7qg6jRBGycJCx+weXjyISyJw41nVcTIsMi6SGYdm0LlsZ94o8YbqOEIYJwsLrdXS0wewdZzqNC8kRY8h6PWwdgDY5Id201WnEcKoebo6MK5DZX4/FMHu89Gq47yWXq8nICQAe2t7RtUfpTqOEMbNpRS0ngLHfoaLO1SneY4UPYZw5Ae4sgs6zwd7uewtxOu8W78Ejcu7MXr1SWLijbt3z8oLKwm5HcIkn0k42TipjiOE8avzIZRtAesGwFPjmqZCip7senBV66Be50Mo31J1GiFMgk6nY2b3GsQlpTBxvfF2ar7x+Aazj8yme4Xu+BbzVR1HCNOg02kXAZLiYfNo1WmeIUVPdqSlwdr+2k3LraeoTiOESSnibM+ETlX58/gttp6OVB3nOWn6NMYHj8fVzpURdUeojiOEaXEuBu1mwIk/4OwG1WnSSdGTHQeXwPVg6LIIbB1VpxHC5LxZuxgtK7szbs1JHsQlqY7zjF/P/srRqKNM9p1MPut8quMIYXq83oaKHWDDEIgzjmkqpOjJqnsXtUmYvPtC6caq0whhknQ6HVPfrEZqmh6/oJPo9XrVkQC4GnOVr499zXuV36OeRz3VcYQwTToddJoHaamwcZj20I9iUvRkRWoKrOkLTsWgRYDqNEKYtMKOdkzuWo1NJyNZf+KO6jikpKXgt98Pj3weDKo9SHUcIUxb/sLQcS6cWau1aFJMip6sCPkGbh+DbkvAxkF1GiFMXscaRelYowj+a08RHZugNMtPp3/i1P1TTPGdgr2VvdIsQuQJVbtB1Te1Fk2P1d6/J0VPZkWdhl1TtQZrnvVVpxEiz5jcpRpWFhaM+VPdMNeFhxdYGLaQD6t+SM3CNZVkECJP6jAHLG20Vk0Kh7mk6MmMlCRtWKtgOWg+VnUaIfIUl3w2THuzOjvPRbPq6M1c335yajJ++/0o5VSK/jX75/r2hcjTHFyh8zdwYQuE/aoshhQ9mbFvNkSfgW6LwcpWdRoh8pxWVdx5q3ZxJq0/w+1HT3N120tPLuXiw4sENgrExtImV7cthFmo2A5q9oYtY+DRDSURpOjJqNvHYe9saDwCitZSnUaIPMu/UxXy21kxctWJXBvmOn3/NMtOLOPzGp9TpWCVXNmmEGap7TRtipd1A5QMc0nRkxHJCdqwlntVaCKTlAmRk5ztrZnxVg32X7rHLwcjcnx7iamJjNs3jgouFfi0xqc5vj0hzJqdszZb85XdcOT7XN+8FD0ZsXsqPLgC3b4FS2vVaYTI85pUKERv7xJM23SW6/fjcnRbC8MWEvE4gsBGgVhbyPktRI4r1wLqfqy1cHpwJVc3LUXP60QchOBvtBuX3eWytxC5ZWz7yhTMb8OXK0+QlpYzl8HDosP46dRP9K/Zn/Iu5XNkG0KIF2g1GfIVgqD+2uSFuUSKnldJioOgvlC8rvaIuhAi1+SztWJWdy8OX3/AD8FXDb7++OR4xu0fR/VC1fmw6ocGX78Q4hVs80PXxRARCgcW59pmpeh5lR0TIfYOdF0CFpaq0whhdhqUKchHPqWZufU8l6KfGHTdXx/7muj4aAJ9A7GU81uI3FfKFxp8ATsnwd3zubJJKXpe5upeOPQttAwAt3Kq0whhtka2rUhxF3uGrwwnJTXNIOs8dOcQv537jSF1hlDKuZRB1imEyIIW46FACe1hodSUHN+cFD0vkhCrjTOWbAT1/6U6jRBmzc7akjk9vDh58xHf7s3+TY9Pkp4wPng89Tzq8U6ldwyQUAiRZdb2WkunO2EQPC/HNydFz4ts84OnD6DrQrCQvyIhVKtVwoW+Tcsyb8cFzt6Jzda6Zh+ZzaPER0zymYSFTs5vIZQrXhd8h8Du6RB5Mkc3JWf8P13cDsd+htZTwKWU6jRCiP83uGV5yhbKz7AV4SSlZG2Ya9/Nfay+uJoR9UZQ3LG4gRMKIbKs2WhwqwBr+mktn3KIFD3/6+lDWDcQyraAOh+qTiOE+B+2VpbM7uHFxajHLPjrYqY/H5MYw4SQCfgW9aV7+e45kFAIkWVWttow192zsHdmjm1Gip7/tXkUJMVrs0XqdKrTCCH+oVoxZwa+UZ6Fuy8TfuNRpj47/dB0nqY8ZYLPBHRyfgthfIrUgKajYN9cuHU0RzYhRc/fzq6HE8uh3QxwLqY6jRDiJb5oXpYqRZwYvjKchOSMTWq28/pONlzZwBjvMXjk88jhhEKILGs0FDyqa8NcyQkGX70UPQBx92D9EKjYAbzeVp1GCPEK1pYWzOnpRcT9eOZuv/Da5R8kPGDSgUk092xOxzIdcyGhECLLLK21Ya6H12DXFIOvXooevR42DAV9GnSaJ8NaQpiACu6ODG9dgWX7rnDk2oOXLqfX65lyYApp+jT8G/rLsJYQpqBwZXhjHIQsgOuhBl21FD2nVsPZddBxLuQvrDqNECKDPm1chtolXBi+Mpz4pBdParb56ma2X9+OXwM/3OzdcjmhECLLGg4Az/oQ1E9rCWUg5l30PI6EjcOh6ptQtZvqNEKITLC00DG7hxdRsQnM2Hzuua/fjb9L4MFA2pVqR5tSbRQkFEJkmYWl1pvrcSRsDzDcag22JlOj18O6QWBpAx3mqE4jhMiC0m75GN22Ej+HXifk0r309/V6PRNDJ2JjacNY77EKEwohsqxgWWg1CQ4vgyu7DbJKoyh6Fi5cSKlSpbCzs8Pb25tDhw69cvmVK1dSqVIl7OzsqF69Ops2bcr8Rk+sgItbofM34OCaxeRCCNX6NCxFwzIF+XLVCR4nJAMQdCmIPTf3ENAwgAJ2BdQGFEJkXb1PoVRjWDtAaxGVTcqLnuXLlzNs2DACAgI4duwYXl5etGnThujo6BcuHxISwjvvvMMnn3zC8ePH6dq1K127duXUqVOZ2/D2AKjZGyq2M8BeCCFUsbDQMbN7DR7FJxG48Sx3ntxh5uGZdCnbhWaezVTHE0Jkh4UFdFkITx/B1uxftdXp9Xp99lNlnbe3N/Xq1WPBggUApKWl4enpycCBAxk9evRzy/fq1Yu4uDg2bNiQ/l6DBg2oWbMmS5Ysee32YmNjcXZ2JiawAk7DDoGds+F2RgihzB+HIhizbj9lq65CZ/WYP7v8iaONo+pYQghDOPozrB8EE2KytRorA8XJkqSkJI4ePcqYMWPS37OwsKBly5aEhr74MbXQ0FCGDRv2zHtt2rQhKCjohcsnJiaSmJiY/ueYGO0vzMfBEst/N8nmHgghjIlD8RRu3nMk+c7b1Dy4TXUcIYTB5GO+ZTUaxcbi6OiY5eknlBY99+7dIzU1FXd392fed3d359y555/GAIiMjHzh8pGRkS9cftq0aUycOPG5908PPZvF1EII43dQdQAhhIF1AJjpTExMDE5OTllah9KiJzeMGTPmmStDaWlpPHjwgIIFC5r0RGWxsbF4enpy48aNLB98UyX7bn77bq77DbLvsu/mte8Z2W9Hx6wPWystetzc3LC0tCQqKuqZ96OiovDweHF/HA8Pj0wtb2tri62t7TPvFShQIOuhjYyTk5NZnRD/S/bd/PbdXPcbZN9l381LTu230qe3bGxsqFOnDjt37kx/Ly0tjZ07d9KwYcMXfqZhw4bPLA+wffv2ly4vhBBCCAFGMLw1bNgwPvjgA+rWrUv9+vWZN28ecXFxfPTRRwD06dOHYsWKMW3aNAAGDx5M06ZNmTNnDh06dOCPP/7gyJEjLF26VOVuCCGEEMLIKS96evXqxd27d/H39ycyMpKaNWuyZcuW9JuVIyIisLD47wUpHx8ffvvtN/z8/Bg7dizly5cnKCiIatWqqdoFJWxtbQkICHhu6M4cyL6b376b636D7Lvsu3nte07vt/J5eoQQQgghcoPyGZmFEEIIIXKDFD1CCCGEMAtS9AghhBDCLEjRI4QQQgizIEWPEZkwYQI6ne6ZV6VKldK/vnTpUpo1a4aTkxM6nY5Hjx49t45SpUo9t47p06fn4l5kzav2/cGDBwwcOJCKFStib29PiRIlGDRoUHoftb9FRETQoUMHHBwcKFy4MF9++SUpKSkqdidTDLHv//y8Tqfjjz/+ULE7Gfa67/d//etflC1bFnt7ewoVKkSXLl2ea0+TF485ZGzfTfGYw+v3/W96vZ527dqh0+me662YV4/7316176Z43F+3382aNXvu63379n1mHYY65sofWRfPqlq1Kjt27Ej/s5XVfw9RfHw8bdu2pW3bts80af2nSZMm8dlnn6X/OTtTdueml+377du3uX37NrNnz6ZKlSpcv36dvn37cvv2bVatWgVAamoqHTp0wMPDg5CQEO7cuUOfPn2wtrZm6tSpSvYnM7Kz73/78ccfadu2bfqfTWHm8Vd9v9epU4fevXtTokQJHjx4wIQJE2jdujVXr17F0tIyzx5zeP2+/80Ujzm8et//Nm/evBe2CsrLx/1vL9v3v5nicX/dfn/22WdMmjQp/c8ODg7p/2/QY64XRiMgIEDv5eX12uV27dqlB/QPHz587mslS5bUf/XVVwbPltMyuu9/W7Fihd7GxkafnJys1+v1+k2bNuktLCz0kZGR6cssXrxY7+TkpE9MTDR0XIPK7r7r9Xo9oF+zZo3hw+WgzO53eHi4HtBfunRJr9eb1zH/577r9aZ5zPX6jO378ePH9cWKFdPfuXPnuf3M68f9Vfuu15vmcX/dfjdt2lQ/ePDgl37dkMdchreMzMWLFylatChlypShd+/eREREZHod06dPp2DBgtSqVYtZs2aZxGVfyNy+/91l9+/fFkJDQ6levXr6pJYAbdq0ITY2ltOnT+d49uzKzr7/rX///ri5uVG/fn1++OEH9CYwBVdG9zsuLo4ff/yR0qVL4+npCZjPMX/Rvv/NFI85vHrf4+Pjeffdd1m4cOELeyrm5eP+un3/myke99d9v//666+4ublRrVo1xowZQ3x8fPrXDHnMZXjLiHh7e/PTTz9RsWJF7ty5w8SJE2ncuDGnTp3K8BDVoEGDqF27Nq6uroSEhDBmzBju3LnD3Llzczh99mRm3+/du8fkyZP5/PPP09+LjIx85oQA0v8cGRmZ8zuQDdndd9CGNN944w0cHBzYtm0bX3zxBU+ePGHQoEG5uSuZkpH9XrRoESNHjiQuLo6KFSuyfft2bGxsgLx/zF+172Caxxxev+9Dhw7Fx8eHLl26vPDzefm4v27fwTSP++v2+91336VkyZIULVqUEydOMGrUKM6fP8+ff/4JGPiYZ+q6kMhVDx8+1Ds5Oem/++67Z95/1fDWP33//fd6KysrfUJCQg6lzBkv2/eYmBh9/fr19W3bttUnJSWlv//ZZ5/pW7du/cyycXFxekC/adOmXMlsKJnd9xcZP368vnjx4jkZ0+BetN+PHj3SX7hwQb9nzx59p06d9LVr19Y/ffpUr9fn/WP+qn1/EVM85nr9s/u+du1afbly5fSPHz9O/zr/GM7Jq8c9I/v+IqZ43F/2M+5vO3fufGY415DHXIa3jFiBAgWoUKECly5dyvI6vL29SUlJ4dq1a4YLlgtetO+PHz+mbdu2ODo6smbNGqytrdO/5uHhQVRU1DPr+PvPr7pMbIwyu+8v4u3tzc2bN0lMTMzpuAbzov12dnamfPnyNGnShFWrVnHu3DnWrFkD5P1j/qp9fxFTPObw7L7/9ddfXL58mQIFCmBlZZU+hPvWW2/RrFkzIO8e94zs+4uY4nF/3b9t3t7eAOlfN+Qxl6LHiD158oTLly9TpEiRLK8jLCwMCwsLChcubMBkOe+f+x4bG0vr1q2xsbFh3bp12NnZPbN8w4YNOXnyJNHR0envbd++HScnJ6pUqZKr2bMrs/v+ImFhYbi4uJhUs8LXfb/r9Xr0en36D/e8fMz/6Z/7/iKmeMzh2X0fPXo0J06cICwsLP0F8NVXX/Hjjz8Cefe4Z2TfX8QUj/vrvt//3ve/v27QY575C1MipwwfPly/e/du/dWrV/XBwcH6li1b6t3c3PTR0dF6vV6vv3Pnjv748eP6ZcuW6QH93r179cePH9ffv39fr9fr9SEhIfqvvvpKHxYWpr98+bL+l19+0RcqVEjfp08flbuVIa/a95iYGL23t7e+evXq+kuXLunv3LmT/kpJSdHr9Xp9SkqKvlq1avrWrVvrw8LC9Fu2bNEXKlRIP2bMGMV79nrZ3fd169bply1bpj958qT+4sWL+kWLFukdHBz0/v7+ivfs1V6135cvX9ZPnTpVf+TIEf3169f1wcHB+k6dOuldXV31UVFRer0+7x7zjOy7qR5zvf71P+f+iX8M8eTV4/4i/9x3Uz3ur9rvS5cu6SdNmqQ/cuSI/urVq/q1a9fqy5Qpo2/SpEn65w15zKXoMSK9evXSFylSRG9jY6MvVqyYvlevXs88ohoQEKAHnnv9+OOPer1erz969Kje29tb7+zsrLezs9NXrlxZP3XqVJO4n+dV+/73PUz/1979u7QOhWEcf4q1pRgQioiDo0KLoo4OWueOblo7uhcU7Sb+GS5xcal0c3Et6GA3HRSdFEFwUagFQUXeOxmIV/SCF5N4vh840KYnh/NyoH3Ij+ajdnl5GYxxdXVl5XLZcrmcDQwM2Orqaui27rj6bu37+/s2NTVlnudZX1+fTU5O2tbWlr2+vkZY1dc+q/vm5sbK5bINDg5ab2+vDQ8PW6VSsfPz89AYv3HN/6X2pK652dffc++9/+E3+53r/pH3tSd13T+r+/r62kqlkuXzectmszYyMmJra2vW6XRCY/yvNU+ZJeBeNwAAgG/imh4AAOAEQg8AAHACoQcAADiB0AMAAJxA6AEAAE4g9AAAACcQegAAgBMIPQAAwAmEHgAA4ARCD4BYS6VSn7bNzc2gb6FQUDab1e3trSSp1Wp9uX+r1YqmMAA/jsdQAIi1twAjSbu7u9rY2NDFxUWwzfM8eZ6nw8NDLS0taWZmRhMTE6rX63p+ftb9/X3Qt1ar6eHhIfTU6nw+r0wm8zPFAIhUOuoJAMBnhoaGgtf9/f1KpVKhbW9831elUtHc3JxqtZrq9boymUyoby6X09PT04f7A/j9CD0AEq/b7arZbKrdbqtQKKjT6ejg4ECzs7NRTw1AjHBND4DEazQaGh0d1djYmHp6erSwsCDf96OeFoCYIfQASLzt7W1Vq9XgfbVaVbPZVLfbjXBWAOKG0AMg0c7OznR0dKT19XWl02ml02lNT0/r8fFRjUYj6ukBiBFCD4BE831fpVJJJycnOj4+DtrKygqnuACEEHoAJNbLy4t2dna0uLio8fHxUFteXla73dbp6WnU0wQQE4QeAIm1t7enu7s7zc/P//VZsVhUsVjkaA+AAH9OCAAAnMCRHgAA4ARCDwAAcAKhBwAAOIHQAwAAnEDoAQAATiD0AAAAJxB6AACAEwg9AADACYQeAADgBEIPAABwAqEHAAA44Q+cv2jkL+cFGgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from skfuzzy import control as ctrl\n",
    "import skfuzzy as fuzz\n",
    "\n",
    "# AT = ctrl.Antecedent(np.arange(-6.2348, 37.103, 0.0001), \"AT\")\n",
    "# AP = ctrl.Antecedent(np.arange(985.85, 1036.6, 0.01), \"AP\")\n",
    "AH = ctrl.Antecedent(np.arange(24.085, 100.2, 0.1), \"AH\")\n",
    "# AFDP = ctrl.Antecedent(np.arange(2.0874, 7.6106, 0.0001), \"AFDP\")\n",
    "TIT = ctrl.Antecedent(np.arange(1000.8, 1100.9, 0.1), \"TIT\")\n",
    "TAT = ctrl.Antecedent(np.arange(511.04, 550.61, 0.1), \"TAT\")\n",
    "CO = ctrl.Consequent(np.arange(0.000388, 44.103, 0.1), \"CO\")\n",
    "\n",
    "# AT.automf(3, variable_type=\"quant\")\n",
    "# AP.automf(5, variable_type=\"quant\")\n",
    "AH.automf(3, variable_type=\"quant\")\n",
    "AH.view()\n",
    "# AFDP.automf(3, variable_type=\"quant\")\n",
    "TIT.automf(3, variable_type=\"quant\")\n",
    "# TIT[\"low\"] = fuzz.zmf(TIT.universe, 1025, 1035)\n",
    "# TIT[\"average\"] = fuzz.trapmf(TIT.universe, [1025, 1035, 1056, 1075])\n",
    "# TIT[\"high\"] = fuzz.smf(TIT.universe, 1065, 1075)\n",
    "TIT.view()\n",
    "TAT.automf(3, variable_type=\"quant\")\n",
    "# TAT[\"low\"] = fuzz.zmf(TAT.universe, 520, 525)\n",
    "# TAT[\"average\"] = fuzz.trapmf(TAT.universe, [520, 525, 535, 540])\n",
    "# TAT[\"high\"] = fuzz.smf(TAT.universe, 535, 540)\n",
    "TAT.view()\n",
    "CO.automf(names=[str(i) for i in range(1, 13)], variable_type=\"quant\")\n",
    "# CO[\"low\"] = fuzz.zmf(CO.universe, 10, 13)\n",
    "# CO[\"average\"] = fuzz.trapmf(CO.universe, [10, 13, 30, 33])\n",
    "# CO[\"high\"] = fuzz.smf(CO.universe, 30, 33)\n",
    "CO.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[IF TIT[high] AND TAT[low] THEN CO[1]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF TIT[high] AND TAT[high] THEN CO[1]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF TIT[low] AND TAT[average] THEN CO[4]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF TIT[average] AND TAT[average] THEN CO[3]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF TIT[low] AND TAT[high] THEN CO[3]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF TIT[average] AND TAT[low] THEN CO[4]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF TIT[average] AND TAT[high] THEN CO[1]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (TIT[low] AND TAT[low]) AND AH[low] THEN CO[6]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (TIT[low] AND TAT[low]) AND AH[high] THEN CO[11]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import get_fuzzy_rules\n",
    "\n",
    "fuzzy_variables = {\n",
    "    # \"AT\": AT,\n",
    "    # \"AP\": AP,\n",
    "    \"AH\": AH,\n",
    "    # \"AFDP\": AFDP,\n",
    "    \"TIT\": TIT,\n",
    "    \"TAT\": TAT,\n",
    "    \"consequent\": CO,\n",
    "}\n",
    "fuzzy_rules = get_fuzzy_rules(rules, fuzzy_variables)\n",
    "\n",
    "fuzzy_cntrl = ctrl.ControlSystem(fuzzy_rules)\n",
    "\n",
    "sim = ctrl.ControlSystemSimulation(fuzzy_cntrl, lenient=False)\n",
    "\n",
    "display(len(fuzzy_rules))\n",
    "fuzzy_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: TIT                     = 1100.0\n",
      "  - low                             : 0.0\n",
      "  - average                         : 0.017982017982469945\n",
      "  - high                            : 0.9820179820175301\n",
      "Antecedent: TAT                     = 526.21\n",
      "  - low                             : 0.23189873417738902\n",
      "  - average                         : 0.7681012658226116\n",
      "  - high                            : 0.0\n",
      "Antecedent: AH                      = 88.135\n",
      "  - low                             : 0.0\n",
      "  - average                         : 0.3166885676741368\n",
      "  - high                            : 0.6833114323258631\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF TIT[high] AND TAT[low] THEN CO[1]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - TIT[high]                                              : 0.9820179820175301\n",
      "  - TAT[low]                                               : 0.23189873417738902\n",
      "                                    TIT[high] AND TAT[low] = 0.23189873417738902\n",
      "  Activation (THEN-clause):\n",
      "                                                     CO[1] : 0.23189873417738902\n",
      "\n",
      "RULE #1:\n",
      "  IF TIT[high] AND TAT[high] THEN CO[1]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - TIT[high]                                              : 0.9820179820175301\n",
      "  - TAT[high]                                              : 0.0\n",
      "                                   TIT[high] AND TAT[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                     CO[1] : 0.0\n",
      "\n",
      "RULE #2:\n",
      "  IF TIT[low] AND TAT[average] THEN CO[4]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - TIT[low]                                               : 0.0\n",
      "  - TAT[average]                                           : 0.7681012658226116\n",
      "                                 TIT[low] AND TAT[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                     CO[4] : 0.0\n",
      "\n",
      "RULE #3:\n",
      "  IF TIT[average] AND TAT[average] THEN CO[3]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - TIT[average]                                           : 0.017982017982469945\n",
      "  - TAT[average]                                           : 0.7681012658226116\n",
      "                             TIT[average] AND TAT[average] = 0.017982017982469945\n",
      "  Activation (THEN-clause):\n",
      "                                                     CO[3] : 0.017982017982469945\n",
      "\n",
      "RULE #4:\n",
      "  IF TIT[low] AND TAT[high] THEN CO[3]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - TIT[low]                                               : 0.0\n",
      "  - TAT[high]                                              : 0.0\n",
      "                                    TIT[low] AND TAT[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                     CO[3] : 0.0\n",
      "\n",
      "RULE #5:\n",
      "  IF TIT[average] AND TAT[low] THEN CO[4]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - TIT[average]                                           : 0.017982017982469945\n",
      "  - TAT[low]                                               : 0.23189873417738902\n",
      "                                 TIT[average] AND TAT[low] = 0.017982017982469945\n",
      "  Activation (THEN-clause):\n",
      "                                                     CO[4] : 0.017982017982469945\n",
      "\n",
      "RULE #6:\n",
      "  IF TIT[average] AND TAT[high] THEN CO[1]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - TIT[average]                                           : 0.017982017982469945\n",
      "  - TAT[high]                                              : 0.0\n",
      "                                TIT[average] AND TAT[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                     CO[1] : 0.0\n",
      "\n",
      "RULE #7:\n",
      "  IF (TIT[low] AND TAT[low]) AND AH[low] THEN CO[6]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - TIT[low]                                               : 0.0\n",
      "  - TAT[low]                                               : 0.23189873417738902\n",
      "  - AH[low]                                                : 0.0\n",
      "                       (TIT[low] AND TAT[low]) AND AH[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                     CO[6] : 0.0\n",
      "\n",
      "RULE #8:\n",
      "  IF (TIT[low] AND TAT[low]) AND AH[high] THEN CO[11]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - TIT[low]                                               : 0.0\n",
      "  - TAT[low]                                               : 0.23189873417738902\n",
      "  - AH[high]                                               : 0.6833114323258631\n",
      "                      (TIT[low] AND TAT[low]) AND AH[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                                    CO[11] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: CO                       = 3.494757369017915\n",
      "  1:\n",
      "    Accumulate using accumulation_max : 0.23189873417738902\n",
      "  2:\n",
      "    Accumulate using accumulation_max : None\n",
      "  3:\n",
      "    Accumulate using accumulation_max : 0.017982017982469945\n",
      "  4:\n",
      "    Accumulate using accumulation_max : 0.017982017982469945\n",
      "  5:\n",
      "    Accumulate using accumulation_max : None\n",
      "  6:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  7:\n",
      "    Accumulate using accumulation_max : None\n",
      "  8:\n",
      "    Accumulate using accumulation_max : None\n",
      "  9:\n",
      "    Accumulate using accumulation_max : None\n",
      "  10:\n",
      "    Accumulate using accumulation_max : None\n",
      "  11:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  12:\n",
      "    Accumulate using accumulation_max : None\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(3.494757369017915)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/user/Projects/python/fuzzy-rules-generator/.venv/lib/python3.12/site-packages/skfuzzy/control/fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sim.input[\"AT\"] = 4.5878\n",
    "# sim.input[\"AP\"] = 1018.7\n",
    "# sim.input[\"AH\"] = 83.675\n",
    "# sim.input[\"AFDP\"] = 3.5758\n",
    "# sim.input[\"TIT\"] = 1086.2\n",
    "# sim.input[\"TAT\"] = 549.83\n",
    "# sim.input[\"AT\"] = 7.8167\n",
    "# sim.input[\"AP\"] = 1022.2\n",
    "sim.input[\"AH\"] = 88.135\n",
    "# sim.input[\"AFDP\"] = 4.6605\n",
    "sim.input[\"TIT\"] = 1100.0\n",
    "sim.input[\"TAT\"] = 526.21\n",
    "sim.compute()\n",
    "sim.print_state()\n",
    "display(sim.output[\"CO\"])\n",
    "CO.view(sim=sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AH</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "      <th>Real</th>\n",
       "      <th>Inferred</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4480</th>\n",
       "      <td>83.256</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>540.65</td>\n",
       "      <td>0.3527</td>\n",
       "      <td>2.122815</td>\n",
       "      <td>1.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24883</th>\n",
       "      <td>73.583</td>\n",
       "      <td>1099.8</td>\n",
       "      <td>538.53</td>\n",
       "      <td>1.2522</td>\n",
       "      <td>2.418762</td>\n",
       "      <td>1.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21557</th>\n",
       "      <td>81.089</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>534.04</td>\n",
       "      <td>1.4718</td>\n",
       "      <td>3.026952</td>\n",
       "      <td>1.555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>64.757</td>\n",
       "      <td>1086.6</td>\n",
       "      <td>549.76</td>\n",
       "      <td>1.3117</td>\n",
       "      <td>2.374747</td>\n",
       "      <td>1.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21388</th>\n",
       "      <td>75.645</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>534.21</td>\n",
       "      <td>1.7835</td>\n",
       "      <td>2.976760</td>\n",
       "      <td>1.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36422</th>\n",
       "      <td>69.953</td>\n",
       "      <td>1076.7</td>\n",
       "      <td>549.96</td>\n",
       "      <td>2.9304</td>\n",
       "      <td>2.398142</td>\n",
       "      <td>0.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21816</th>\n",
       "      <td>70.858</td>\n",
       "      <td>1087.8</td>\n",
       "      <td>549.43</td>\n",
       "      <td>1.7219</td>\n",
       "      <td>2.666172</td>\n",
       "      <td>0.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34158</th>\n",
       "      <td>47.855</td>\n",
       "      <td>1085.2</td>\n",
       "      <td>549.91</td>\n",
       "      <td>1.5878</td>\n",
       "      <td>2.242754</td>\n",
       "      <td>0.655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12784</th>\n",
       "      <td>69.850</td>\n",
       "      <td>1090.5</td>\n",
       "      <td>550.26</td>\n",
       "      <td>2.0753</td>\n",
       "      <td>1.754647</td>\n",
       "      <td>0.321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30182</th>\n",
       "      <td>70.275</td>\n",
       "      <td>1060.9</td>\n",
       "      <td>550.13</td>\n",
       "      <td>4.1917</td>\n",
       "      <td>1.906511</td>\n",
       "      <td>2.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>61.376</td>\n",
       "      <td>1092.2</td>\n",
       "      <td>549.89</td>\n",
       "      <td>0.3633</td>\n",
       "      <td>2.155418</td>\n",
       "      <td>1.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31492</th>\n",
       "      <td>63.967</td>\n",
       "      <td>1085.9</td>\n",
       "      <td>549.98</td>\n",
       "      <td>2.0315</td>\n",
       "      <td>2.151404</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36021</th>\n",
       "      <td>61.707</td>\n",
       "      <td>1081.1</td>\n",
       "      <td>549.34</td>\n",
       "      <td>2.3146</td>\n",
       "      <td>2.914147</td>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21791</th>\n",
       "      <td>96.150</td>\n",
       "      <td>1087.7</td>\n",
       "      <td>549.79</td>\n",
       "      <td>1.2175</td>\n",
       "      <td>2.323823</td>\n",
       "      <td>1.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>80.911</td>\n",
       "      <td>1086.1</td>\n",
       "      <td>550.32</td>\n",
       "      <td>1.1838</td>\n",
       "      <td>1.733644</td>\n",
       "      <td>0.550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AH     TIT     TAT    Real  Inferred   RMSE\n",
       "4480   83.256  1100.0  540.65  0.3527  2.122815  1.770\n",
       "24883  73.583  1099.8  538.53  1.2522  2.418762  1.167\n",
       "21557  81.089  1100.0  534.04  1.4718  3.026952  1.555\n",
       "1705   64.757  1086.6  549.76  1.3117  2.374747  1.063\n",
       "21388  75.645  1100.0  534.21  1.7835  2.976760  1.193\n",
       "36422  69.953  1076.7  549.96  2.9304  2.398142  0.532\n",
       "21816  70.858  1087.8  549.43  1.7219  2.666172  0.944\n",
       "34158  47.855  1085.2  549.91  1.5878  2.242754  0.655\n",
       "12784  69.850  1090.5  550.26  2.0753  1.754647  0.321\n",
       "30182  70.275  1060.9  550.13  4.1917  1.906511  2.285\n",
       "3996   61.376  1092.2  549.89  0.3633  2.155418  1.792\n",
       "31492  63.967  1085.9  549.98  2.0315  2.151404  0.120\n",
       "36021  61.707  1081.1  549.34  2.3146  2.914147  0.600\n",
       "21791  96.150  1087.7  549.79  1.2175  2.323823  1.106\n",
       "1054   80.911  1086.1  550.32  1.1838  1.733644  0.550"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import math\n",
    "\n",
    "\n",
    "def fuzzy_pred(row):\n",
    "    # sim.input[\"AT\"] = row[\"AT\"]\n",
    "    # sim.input[\"AP\"] = row[\"AP\"]\n",
    "    sim.input[\"AH\"] = row[\"AH\"]\n",
    "    # sim.input[\"AFDP\"] = row[\"AFDP\"]\n",
    "    sim.input[\"TIT\"] = row[\"TIT\"]\n",
    "    sim.input[\"TAT\"] = row[\"TAT\"]\n",
    "    sim.compute()\n",
    "    return sim.output[\"CO\"]\n",
    "\n",
    "\n",
    "def rmse(row):\n",
    "    return math.sqrt(metrics.mean_squared_error([row[\"Real\"]], [row[\"Inferred\"]]))\n",
    "\n",
    "result_train = X_train.copy()\n",
    "result_train[\"Real\"] = y_train\n",
    "result_train = result_train[:10000]\n",
    "\n",
    "result_train[\"Inferred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
    "result_train[\"RMSE\"] = result_train.apply(rmse, axis=1)\n",
    "result_train = result_train.round({\"RMSE\": 3})\n",
    "result_train.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AH</th>\n",
       "      <th>TIT</th>\n",
       "      <th>TAT</th>\n",
       "      <th>Real</th>\n",
       "      <th>Inferred</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>84.837</td>\n",
       "      <td>1088.7</td>\n",
       "      <td>550.39</td>\n",
       "      <td>1.34970</td>\n",
       "      <td>1.608328</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20343</th>\n",
       "      <td>59.574</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>542.01</td>\n",
       "      <td>1.63430</td>\n",
       "      <td>2.036748</td>\n",
       "      <td>0.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>81.262</td>\n",
       "      <td>1092.9</td>\n",
       "      <td>544.91</td>\n",
       "      <td>0.78632</td>\n",
       "      <td>3.997200</td>\n",
       "      <td>3.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>88.135</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>526.21</td>\n",
       "      <td>0.72742</td>\n",
       "      <td>3.494757</td>\n",
       "      <td>2.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5713</th>\n",
       "      <td>86.846</td>\n",
       "      <td>1080.2</td>\n",
       "      <td>550.25</td>\n",
       "      <td>1.35980</td>\n",
       "      <td>1.928498</td>\n",
       "      <td>0.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21917</th>\n",
       "      <td>75.935</td>\n",
       "      <td>1081.1</td>\n",
       "      <td>549.66</td>\n",
       "      <td>1.45140</td>\n",
       "      <td>2.606285</td>\n",
       "      <td>1.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13099</th>\n",
       "      <td>78.314</td>\n",
       "      <td>1089.8</td>\n",
       "      <td>550.37</td>\n",
       "      <td>1.00960</td>\n",
       "      <td>1.622633</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26704</th>\n",
       "      <td>79.478</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>550.19</td>\n",
       "      <td>2.01190</td>\n",
       "      <td>2.042107</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4182</th>\n",
       "      <td>41.623</td>\n",
       "      <td>1100.2</td>\n",
       "      <td>539.10</td>\n",
       "      <td>0.37685</td>\n",
       "      <td>2.116185</td>\n",
       "      <td>1.739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>69.233</td>\n",
       "      <td>1091.6</td>\n",
       "      <td>549.98</td>\n",
       "      <td>1.15990</td>\n",
       "      <td>2.066452</td>\n",
       "      <td>0.907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7347 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           AH     TIT     TAT     Real  Inferred   RMSE\n",
       "18246  84.837  1088.7  550.39  1.34970  1.608328  0.259\n",
       "20343  59.574  1100.0  542.01  1.63430  2.036748  0.402\n",
       "2924   81.262  1092.9  544.91  0.78632  3.997200  3.211\n",
       "117    88.135  1100.0  526.21  0.72742  3.494757  2.767\n",
       "5713   86.846  1080.2  550.25  1.35980  1.928498  0.569\n",
       "...       ...     ...     ...      ...       ...    ...\n",
       "21917  75.935  1081.1  549.66  1.45140  2.606285  1.155\n",
       "13099  78.314  1089.8  550.37  1.00960  1.622633  0.613\n",
       "26704  79.478  1073.0  550.19  2.01190  2.042107  0.030\n",
       "4182   41.623  1100.2  539.10  0.37685  2.116185  1.739\n",
       "2982   69.233  1091.6  549.98  1.15990  2.066452  0.907\n",
       "\n",
       "[7347 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test = X_test.copy()\n",
    "result_test[\"Real\"] = y_test\n",
    "\n",
    "result_test[\"Inferred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
    "result_test[\"RMSE\"] = result_test.apply(rmse, axis=1)\n",
    "result_test = result_test.round({\"RMSE\": 3})\n",
    "result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_train': 2.0980454600317606,\n",
       " 'RMSE_test': 2.05886400827211,\n",
       " 'RMAE_train': 1.1847436158491003,\n",
       " 'RMAE_test': 1.1874008432872027,\n",
       " 'MAE_train': 1.4036174352952007,\n",
       " 'MAE_test': 1.4099207626391603,\n",
       " 'R2_train': 0.1608000420839235,\n",
       " 'R2_test': 0.16319151715106217}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmetrics = {}\n",
    "rmetrics[\"RMSE_train\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_train[\"Real\"], result_train[\"Inferred\"])\n",
    ")\n",
    "rmetrics[\"RMSE_test\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_test[\"Real\"], result_test[\"Inferred\"])\n",
    ")\n",
    "rmetrics[\"RMAE_train\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(result_train[\"Real\"], result_train[\"Inferred\"])\n",
    ")\n",
    "rmetrics[\"RMAE_test\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(result_test[\"Real\"], result_test[\"Inferred\"])\n",
    ")\n",
    "rmetrics[\"MAE_train\"] = float(\n",
    "    metrics.mean_absolute_error(result_train[\"Real\"], result_train[\"Inferred\"])\n",
    ")\n",
    "rmetrics[\"MAE_test\"] = float(\n",
    "    metrics.mean_absolute_error(result_test[\"Real\"], result_test[\"Inferred\"])\n",
    ")\n",
    "rmetrics[\"R2_train\"] = metrics.r2_score(result_train[\"Real\"], result_train[\"Inferred\"])\n",
    "rmetrics[\"R2_test\"] = metrics.r2_score(result_test[\"Real\"], result_test[\"Inferred\"])\n",
    "\n",
    "rmetrics"
   ]
  }
 ],
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