{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- T <= 32.50\n",
      "|   |--- TiO2 <= 0.18\n",
      "|   |   |--- Al2O3 <= 0.18\n",
      "|   |   |   |--- T <= 22.50\n",
      "|   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [3.71]\n",
      "|   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [4.66]\n",
      "|   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |--- value: [4.88]\n",
      "|   |   |   |--- T >  22.50\n",
      "|   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [3.18]\n",
      "|   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [3.38]\n",
      "|   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |--- value: [4.24]\n",
      "|   |   |--- Al2O3 >  0.18\n",
      "|   |   |   |--- T <= 22.50\n",
      "|   |   |   |   |--- value: [6.67]\n",
      "|   |   |   |--- T >  22.50\n",
      "|   |   |   |   |--- T <= 27.50\n",
      "|   |   |   |   |   |--- value: [5.59]\n",
      "|   |   |   |   |--- T >  27.50\n",
      "|   |   |   |   |   |--- value: [4.73]\n",
      "|   |--- TiO2 >  0.18\n",
      "|   |   |--- T <= 22.50\n",
      "|   |   |   |--- value: [7.13]\n",
      "|   |   |--- T >  22.50\n",
      "|   |   |   |--- T <= 27.50\n",
      "|   |   |   |   |--- value: [5.87]\n",
      "|   |   |   |--- T >  27.50\n",
      "|   |   |   |   |--- value: [4.94]\n",
      "|--- T >  32.50\n",
      "|   |--- T <= 47.50\n",
      "|   |   |--- TiO2 <= 0.18\n",
      "|   |   |   |--- Al2O3 <= 0.18\n",
      "|   |   |   |   |--- T <= 42.50\n",
      "|   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |   |--- value: [2.36]\n",
      "|   |   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |   |--- value: [2.68]\n",
      "|   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |--- T <= 37.50\n",
      "|   |   |   |   |   |   |   |--- value: [3.12]\n",
      "|   |   |   |   |   |   |--- T >  37.50\n",
      "|   |   |   |   |   |   |   |--- value: [2.65]\n",
      "|   |   |   |   |--- T >  42.50\n",
      "|   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |--- value: [1.83]\n",
      "|   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [2.40]\n",
      "|   |   |   |--- Al2O3 >  0.18\n",
      "|   |   |   |   |--- T <= 37.50\n",
      "|   |   |   |   |   |--- value: [4.12]\n",
      "|   |   |   |   |--- T >  37.50\n",
      "|   |   |   |   |   |--- value: [3.56]\n",
      "|   |   |--- TiO2 >  0.18\n",
      "|   |   |   |--- T <= 40.00\n",
      "|   |   |   |   |--- value: [4.35]\n",
      "|   |   |   |--- T >  40.00\n",
      "|   |   |   |   |--- value: [3.56]\n",
      "|   |--- T >  47.50\n",
      "|   |   |--- TiO2 <= 0.18\n",
      "|   |   |   |--- Al2O3 <= 0.18\n",
      "|   |   |   |   |--- T <= 52.50\n",
      "|   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |--- Al2O3 <= 0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.63]\n",
      "|   |   |   |   |   |   |--- Al2O3 >  0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.90]\n",
      "|   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |--- value: [2.11]\n",
      "|   |   |   |   |--- T >  52.50\n",
      "|   |   |   |   |   |--- T <= 65.00\n",
      "|   |   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.55]\n",
      "|   |   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.66]\n",
      "|   |   |   |   |   |--- T >  65.00\n",
      "|   |   |   |   |   |   |--- TiO2 <= 0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.19]\n",
      "|   |   |   |   |   |   |--- TiO2 >  0.03\n",
      "|   |   |   |   |   |   |   |--- value: [1.29]\n",
      "|   |   |   |--- Al2O3 >  0.18\n",
      "|   |   |   |   |--- T <= 65.00\n",
      "|   |   |   |   |   |--- T <= 57.50\n",
      "|   |   |   |   |   |   |--- value: [2.43]\n",
      "|   |   |   |   |   |--- T >  57.50\n",
      "|   |   |   |   |   |   |--- value: [2.16]\n",
      "|   |   |   |   |--- T >  65.00\n",
      "|   |   |   |   |   |--- value: [1.73]\n",
      "|   |   |--- TiO2 >  0.18\n",
      "|   |   |   |--- T <= 65.00\n",
      "|   |   |   |   |--- T <= 57.50\n",
      "|   |   |   |   |   |--- value: [2.84]\n",
      "|   |   |   |   |--- T >  57.50\n",
      "|   |   |   |   |   |--- value: [2.54]\n",
      "|   |   |   |--- T >  65.00\n",
      "|   |   |   |   |--- value: [1.91]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "\n",
    "model = pickle.load(open(\"data/vtree.model.sav\", \"rb\"))\n",
    "features = (\n",
    "    pd.read_csv(\"data/viscosity_train.csv\", sep=\";\", decimal=\",\")\n",
    "    .drop([\"Viscosity\"], 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": [
       "35"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T > 52.5) and (T > 65.0) and (TiO2 <= 0.025) -> 1.194,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T > 52.5) and (T > 65.0) and (TiO2 > 0.025) -> 1.289,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T > 52.5) and (T <= 65.0) and (TiO2 <= 0.025) -> 1.548,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 52.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) -> 1.629,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T > 52.5) and (T <= 65.0) and (TiO2 > 0.025) -> 1.662,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) and (T > 65.0) -> 1.728,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T > 42.5) and (TiO2 <= 0.025) -> 1.832,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 52.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) -> 1.897,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 > 0.175) and (T > 65.0) -> 1.91,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 52.5) and (TiO2 > 0.025) -> 2.109,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) and (T <= 65.0) and (T > 57.5) -> 2.16,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 42.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) -> 2.361,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T > 42.5) and (TiO2 > 0.025) -> 2.402,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) and (T <= 65.0) and (T <= 57.5) -> 2.426,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 > 0.175) and (T <= 65.0) and (T > 57.5) -> 2.538,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 42.5) and (TiO2 > 0.025) and (T > 37.5) -> 2.655,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 42.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) -> 2.682,\n",
       " if (T > 32.5) and (T > 47.5) and (TiO2 > 0.175) and (T <= 65.0) and (T <= 57.5) -> 2.838,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 42.5) and (TiO2 > 0.025) and (T <= 37.5) -> 3.121,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T > 22.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) -> 3.18,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T > 22.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) -> 3.38,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 > 0.175) and (T > 40.0) -> 3.561,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) and (T > 37.5) -> 3.565,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 22.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) -> 3.707,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) and (T <= 37.5) -> 4.118,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T > 22.5) and (TiO2 > 0.025) -> 4.236,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 > 0.175) and (T <= 40.0) -> 4.354,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 22.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) -> 4.66,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) and (T > 22.5) and (T > 27.5) -> 4.731,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (T <= 22.5) and (TiO2 > 0.025) -> 4.885,\n",
       " if (T <= 32.5) and (TiO2 > 0.175) and (T > 22.5) and (T > 27.5) -> 4.944,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) and (T > 22.5) and (T <= 27.5) -> 5.594,\n",
       " if (T <= 32.5) and (TiO2 > 0.175) and (T > 22.5) and (T <= 27.5) -> 5.865,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) and (T <= 22.5) -> 6.67,\n",
       " if (T <= 32.5) and (TiO2 > 0.175) and (T <= 22.5) -> 7.132]"
      ]
     },
     "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": [
       "35"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.194,\n",
       " if (T > 32.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 1.289,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.548,\n",
       " if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.629,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 1.662,\n",
       " if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 1.728,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.832,\n",
       " if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 1.897,\n",
       " if (T > 32.5) and (TiO2 > 0.175) -> 1.91,\n",
       " if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 2.109,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 2.16,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 2.361,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 2.402,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 2.426,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 > 0.175) -> 2.538,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 2.655,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 2.682,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 > 0.175) -> 2.838,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 3.121,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 3.18,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 3.38,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 > 0.175) -> 3.561,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 3.565,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 3.707,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 4.118,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 4.236,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 > 0.175) -> 4.354,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 4.66,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 4.731,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 4.885,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 > 0.175) -> 4.944,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 5.594,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 > 0.175) -> 5.865,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 6.67,\n",
       " if (T <= 32.5) and (TiO2 > 0.175) -> 7.132]"
      ]
     },
     "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": [
       "26"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.194,\n",
       " if (T > 32.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 1.289,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.548,\n",
       " if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.629,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 1.662,\n",
       " if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 1.728,\n",
       " if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 1.897,\n",
       " if (T > 32.5) and (TiO2 > 0.175) -> 1.91,\n",
       " if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 2.109,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 2.097,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 2.293,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 2.682,\n",
       " if (T > 32.5) and (T <= 65.0) and (TiO2 > 0.175) -> 2.688,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 2.726,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 3.18,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 3.38,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 3.707,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 3.842,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 4.236,\n",
       " if (T > 32.5) and (T <= 47.5) and (TiO2 > 0.175) -> 3.958,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 4.66,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 4.885,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 5.162,\n",
       " if (T <= 32.5) and (T > 22.5) and (TiO2 > 0.175) -> 5.405,\n",
       " if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 6.67,\n",
       " if (T <= 32.5) and (TiO2 > 0.175) -> 7.132]"
      ]
     },
     "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['(Al2O3 <= 0.175)', '(Al2O3 > 0.025)', '(Al2O3 > 0.175)', '(TiO2 <= 0.175)', '(TiO2 > 0.025)', '(TiO2 > 0.175)']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>(Al2O3 &lt;= 0.175)</th>\n",
       "      <th>(Al2O3 &gt; 0.025)</th>\n",
       "      <th>(Al2O3 &gt; 0.175)</th>\n",
       "      <th>(TiO2 &lt;= 0.175)</th>\n",
       "      <th>(TiO2 &gt; 0.025)</th>\n",
       "      <th>(TiO2 &gt; 0.175)</th>\n",
       "      <th>consequent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rule</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>if (T &gt; 32.5) and (TiO2 &lt;= 0.175) and (Al2O3 &lt;= 0.175) -&gt; 1.194</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>if (T &gt; 32.5) and (TiO2 &lt;= 0.175) and (TiO2 &gt; 0.025) and (Al2O3 &lt;= 0.175) -&gt; 1.289</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>if (T &gt; 32.5) and (T &lt;= 65.0) and (TiO2 &lt;= 0.175) and (Al2O3 &lt;= 0.175) -&gt; 1.548</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>if (T &gt; 32.5) and (T &lt;= 52.5) and (TiO2 &lt;= 0.175) and (Al2O3 &lt;= 0.175) -&gt; 1.629</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>if (T &gt; 32.5) and (T &lt;= 65.0) and (TiO2 &lt;= 0.175) and (TiO2 &gt; 0.025) and (Al2O3 &lt;= 0.175) -&gt; 1.662</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   (Al2O3 <= 0.175)  \\\n",
       "rule                                                                  \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 <=...                1   \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (TiO2 > 0...                1   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...                1   \n",
       "if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.17...                1   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...                1   \n",
       "\n",
       "                                                   (Al2O3 > 0.025)  \\\n",
       "rule                                                                 \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 <=...               0   \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (TiO2 > 0...               0   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...               0   \n",
       "if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.17...               0   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...               0   \n",
       "\n",
       "                                                   (Al2O3 > 0.175)  \\\n",
       "rule                                                                 \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 <=...               0   \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (TiO2 > 0...               0   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...               0   \n",
       "if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.17...               0   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...               0   \n",
       "\n",
       "                                                   (TiO2 <= 0.175)  \\\n",
       "rule                                                                 \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 <=...               1   \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (TiO2 > 0...               1   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...               1   \n",
       "if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.17...               1   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...               1   \n",
       "\n",
       "                                                   (TiO2 > 0.025)  \\\n",
       "rule                                                                \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 <=...              0   \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (TiO2 > 0...              1   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...              0   \n",
       "if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.17...              0   \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...              1   \n",
       "\n",
       "                                                   (TiO2 > 0.175) consequent  \n",
       "rule                                                                          \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 <=...              0      1.194  \n",
       "if (T > 32.5) and (TiO2 <= 0.175) and (TiO2 > 0...              0      1.289  \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...              0      1.548  \n",
       "if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.17...              0      1.629  \n",
       "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.17...              0      1.662  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import get_features, vectorize_rules\n",
    "\n",
    "features = get_features(rules, [\"T\"])\n",
    "print(features)\n",
    "\n",
    "df_rules = vectorize_rules(rules, features)\n",
    "df_rules.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\site-packages\\sklearn\\base.py:1473: ConvergenceWarning: Number of distinct clusters (5) found smaller than n_clusters (6). Possibly due to duplicate points in X.\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{2: 0.42601604060235754,\n",
       " 3: 0.7018993361356377,\n",
       " 4: 0.8249121250065079,\n",
       " 5: 1.0,\n",
       " 6: 1.0}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'The best clusters count is 5'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.cluster_helper import draw_best_clusters_plot, get_best_clusters_num\n",
    "\n",
    "random_state = 9\n",
    "\n",
    "X = df_rules.copy()\n",
    "X = X.drop([\"consequent\"], axis=1)\n",
    "\n",
    "clusters_score = get_best_clusters_num(X, random_state)\n",
    "display(clusters_score)\n",
    "\n",
    "draw_best_clusters_plot(clusters_score)\n",
    "\n",
    "clusters_num = sorted(clusters_score.items(), key=lambda x: x[1], reverse=True)[0][0]\n",
    "display(f\"The best clusters count is {clusters_num}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Кластер 1 (6):\n",
      "if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.194;\n",
      "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.548;\n",
      "if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 1.629;\n",
      "if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 2.097;\n",
      "if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 3.18;\n",
      "if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) -> 3.707\n",
      "--------\n",
      "Кластер 2 (5):\n",
      "if (T > 32.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 1.728;\n",
      "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 2.293;\n",
      "if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 3.842;\n",
      "if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 5.162;\n",
      "if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 > 0.175) -> 6.67\n",
      "--------\n",
      "Кластер 3 (5):\n",
      "if (T > 32.5) and (TiO2 > 0.175) -> 1.91;\n",
      "if (T > 32.5) and (T <= 65.0) and (TiO2 > 0.175) -> 2.688;\n",
      "if (T > 32.5) and (T <= 47.5) and (TiO2 > 0.175) -> 3.958;\n",
      "if (T <= 32.5) and (T > 22.5) and (TiO2 > 0.175) -> 5.405;\n",
      "if (T <= 32.5) and (TiO2 > 0.175) -> 7.132\n",
      "--------\n",
      "Кластер 4 (6):\n",
      "if (T > 32.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 1.289;\n",
      "if (T > 32.5) and (T <= 65.0) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 1.662;\n",
      "if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 2.109;\n",
      "if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 2.726;\n",
      "if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 4.236;\n",
      "if (T <= 32.5) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (Al2O3 <= 0.175) -> 4.885\n",
      "--------\n",
      "Кластер 5 (4):\n",
      "if (T > 32.5) and (T <= 52.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 1.897;\n",
      "if (T > 32.5) and (T <= 47.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 2.682;\n",
      "if (T <= 32.5) and (T > 22.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 3.38;\n",
      "if (T <= 32.5) and (TiO2 <= 0.175) and (Al2O3 <= 0.175) and (Al2O3 > 0.025) -> 4.66\n",
      "--------\n"
     ]
    }
   ],
   "source": [
    "from sklearn import cluster\n",
    "\n",
    "from src.cluster_helper import print_cluster_result\n",
    "\n",
    "kmeans = cluster.KMeans(n_clusters=clusters_num, random_state=random_state)\n",
    "kmeans.fit(X)\n",
    "\n",
    "print_cluster_result(X, clusters_num, kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Viscosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Viscosity\n",
       "0  20    0.0   0.0      3.707\n",
       "1  25    0.0   0.0      3.180\n",
       "2  35    0.0   0.0      2.361"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Viscosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    T  Al2O3  TiO2  Viscosity\n",
       "0  30    0.0   0.0      2.716\n",
       "1  40    0.0   0.0      2.073\n",
       "2  60    0.0   0.0      1.329"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viscosity_train = pd.read_csv(\"data/viscosity_train.csv\", sep=\";\", decimal=\",\")\n",
    "viscosity_test = pd.read_csv(\"data/viscosity_test.csv\", sep=\";\", decimal=\",\")\n",
    "\n",
    "display(viscosity_train.head(3))\n",
    "display(viscosity_test.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[if (T = 70) and (TiO2 = 0.0) and (Al2O3 = 0.0) -> 1.194,\n",
       "  if (T = 48.75) and (TiO2 = 0.0) and (Al2O3 = 0.0) -> 1.548,\n",
       "  if (T = 42.5) and (TiO2 = 0.0) and (Al2O3 = 0.0) -> 1.629,\n",
       "  if (T = 40.0) and (TiO2 = 0.0) and (Al2O3 = 0.0) -> 2.097,\n",
       "  if (T = 27.5) and (TiO2 = 0.0) and (Al2O3 = 0.0) -> 3.18,\n",
       "  if (T = 20) and (TiO2 = 0.0) and (Al2O3 = 0.0) -> 3.707],\n",
       " [if (T = 70) and (TiO2 = 0.0) and (Al2O3 = 0.3) -> 1.728,\n",
       "  if (T = 48.75) and (TiO2 = 0.0) and (Al2O3 = 0.3) -> 2.293,\n",
       "  if (T = 40.0) and (TiO2 = 0.0) and (Al2O3 = 0.3) -> 3.842,\n",
       "  if (T = 27.5) and (TiO2 = 0.0) and (Al2O3 = 0.3) -> 5.162,\n",
       "  if (T = 20) and (TiO2 = 0.0) and (Al2O3 = 0.3) -> 6.67],\n",
       " [if (T = 70) and (TiO2 = 0.3) -> 1.91,\n",
       "  if (T = 48.75) and (TiO2 = 0.3) -> 2.688,\n",
       "  if (T = 40.0) and (TiO2 = 0.3) -> 3.958,\n",
       "  if (T = 27.5) and (TiO2 = 0.3) -> 5.405,\n",
       "  if (T = 20) and (TiO2 = 0.3) -> 7.132],\n",
       " [if (T = 70) and (TiO2 = 0.1) and (Al2O3 = 0.0) -> 1.289,\n",
       "  if (T = 48.75) and (TiO2 = 0.1) and (Al2O3 = 0.0) -> 1.662,\n",
       "  if (T = 42.5) and (TiO2 = 0.1) and (Al2O3 = 0.0) -> 2.109,\n",
       "  if (T = 40.0) and (TiO2 = 0.1) and (Al2O3 = 0.0) -> 2.726,\n",
       "  if (T = 27.5) and (TiO2 = 0.1) and (Al2O3 = 0.0) -> 4.236,\n",
       "  if (T = 20) and (TiO2 = 0.1) and (Al2O3 = 0.0) -> 4.885],\n",
       " [if (T = 42.5) and (TiO2 = 0.0) and (Al2O3 = 0.1) -> 1.897,\n",
       "  if (T = 40.0) and (TiO2 = 0.0) and (Al2O3 = 0.1) -> 2.682,\n",
       "  if (T = 27.5) and (TiO2 = 0.0) and (Al2O3 = 0.1) -> 3.38,\n",
       "  if (T = 20) and (TiO2 = 0.0) and (Al2O3 = 0.1) -> 4.66]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import simplify_and_group_rules\n",
    "\n",
    "clustered_rules = simplify_and_group_rules(\n",
    "    viscosity_train, rules, clusters_num, kmeans.labels_\n",
    ")\n",
    "clustered_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\Projects\\python\\fuzzy\\.venv\\Lib\\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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EV/ZBxCJ4YSKUqK46TcFqOwWKeGkXUeszVacRwuiOxB3hu9Pf8UHdD6jkXkl1nOeS4qYAFXK0Y15vf47E3OfrvZdUxxHCuFIfav+w+zSCJh+oTlPwHAtrrSWuHoSIharTCGFUyenJBIUH4V/cnwE1BqiO84+kuClgDX09eLtZeeZtOcf5uAeq4whhPNtC4EGc9g+8ja3qNGqUawpNhsCf0+HWGdVphDCaTw5/wu3k24Q2C8XWDI5vKW4UGNW+Kj7uzoxaFUl6ptxdISzAxR3axbTtpkKxiqrTqPVCELiX0y6qzkxXnUaIPNt/cz8rzq5geP3hlHMtpzpOtkhxo4CTvS3z+9Th1I1EFu+Uiw+FmUtJgLUfgG9z7aJaa2fvDD2WaLMz7/1EdRoh8uRB2gOCw4Np5N2IftX6qY6TbVLcKFLHpyiDW1bks+3nOXUjQXUcIXJv8wStwOm+CGzkIwWAMvWh2QhtIsObx1WnESLX5h6aS0JqAlMDp2KjM5/j23ySWqAP21SmUonCjFoZSWqG3F0hzNDZzXDsB+gwXRuKEf/VciwUrwZrBkFGquo0QuTYrqu7WHNhDWMajqF04dKq4+SIFDcKOdjZ8HGfOly8/ZDPtp9XHUeInEm+B+s/hErtoJ7p3z1R4OwctLl+7pzTzuAIYUbiU+KZHDGZZqWb8VLll1THyTEpbhSrUcqVYW0qs3jnRY7G3FcdR4js2/QRZKRAt89Bp1OdxjR514ZWY7Vrb679pTqNENk24+AMUjNTmdxkMjozPL6luDEBg1pWpHZpN0atiiQlXYanhBk4FQYnV0OneeBaUnUa0xY4AkrV1Yan0h+pTiPEP9pyeQu/R//O+Ebj8SrkpTpOrkhxYwLsbG2Y38efa/cfMfePs6rjCPF8D2/BxpFQrQvU7q06jemztdPm/omPge3TVKcR4rnuPrpL6P5Q2pRtQ5cKXVTHyTUpbkxEpRJFGNOhKt+ER3Pg0l3VcYR4OoMBNozQ/rvLAhmOyq7iVaFNMOz/Ai6Hq04jxFMZDAamRkwFYFLjSWY5HPUfUtyYkDcCy9OgnDujV0eSlJqhOo4QTzq+Es5sgC6fQOHiqtOYl8aDoWxjrUVF6kPVaYR4woZLG/jz6p8ENQ6imHMx1XHyRIobE2Jro2Neb3/uPEhjxqYo1XGEeFziDe0i4lq9oEZ31WnMj40t9PgCkm5r3cOFMCFxSXHMPDCTF8u/SHvf9qrj5JkUNyamXLFCTOhUjR8PxLD73G3VcYTQGAywbqg2+26nuarTmC+PClqLir++hgvbVacRAtCGo0IiQnC0c2RiwETVcYxCihsT1D+gHM0qeTL21+MkPJLeNMIEHPkWLmyDbp+Bi4fqNOatwVtQoZVWLD6KV51GCH49/yvh18OZ0nQKbo5uquMYhRQ3JsjGRsfsXn48TMlg2obTquMIa3f/CvwxEeq+ClU6qE5j/mxsoNtCSH0Am8erTiOs3PWH15l7aC49K/WkRZkWquMYjRQ3Jqp0UWcmda3B6sPX2Ho6TnUcYa30elg7BJzdocNM1WksR1Ef6DgTIn+CM5tUpxFWSm/QMyl8Eq6OrnzU8CPVcYxKihsT1rt+GdpUK8H4305wPylNdRxhjQ4tg8t7oPtCcHJVncay1OkPVTrC+mFaKwshCtjPZ37mUOwhpjadShGHIqrjGJUUNyZMp9Mx86XapGfqmbT2pOo4wtrcvQhbQ6Dh29o1IsK4dDro+ilkpsHGUarTCCtzOeEyCw4v4F9V/0WTUk1UxzE6KW5MXAlXJ6Z2r8mG4zfZePym6jjCWugztXYBRbyh7RTVaSxXEW/oPB9O/Qan1qhOI6xEpj6ToPAgirsUZ0T9Earj5AspbsxAN/9SvFjLm6CwE9x+kKo6jrAGEQvh2iGtbYBjYdVpLFutl6F6N9gwUmttIUQ+++70dxy/fZzQwFBc7F1Ux8kXUtyYAZ1OR2iPWtjodExYcwKDwaA6krBkt6Lgz1BoMgTKWd7papOj02kzPutsYP1wbU4hIfLJhfsX+Pzo5wyoMYB6XvVUx8k3youbRYsW4evri5OTEwEBARw8ePC5yy9YsICqVavi7OyMj48PI0aMICUlpYDSqlOssCPTe9Zm6+k41hy9rjqOsFSZ6dpwlHt5eGGS6jTWo5AndF0AZzfC8V9UpxEWKl2fzsTwifgU8WFovaGq4+QrpcXNL7/8wsiRIwkJCeHIkSP4+/vToUMHbt16+qnZn376iXHjxhESEkJUVBRff/01v/zyCxMmTCjg5Gp0rOVNz7qlCVl3ipsJj1THEZZoz8cQewJ6LgZ7J9VprEv1ruDXFzaNgQT5AiOM76sTX3H23lmmN5uOo62j6jj5Smlx8/HHH/POO+/wxhtvUKNGDZYsWYKLiwvffPPNU5fft28fgYGBvPLKK/j6+tK+fXv69ev3j2d7LMnkrjVxcbBl7K8yPCWM7GYk7J4DzUdC6fqq01inF2eDg4s2e7Ec38KIou5GsTRyKW/VfotanrVUx8l3yoqbtLQ0Dh8+TNu2bf8bxsaGtm3bEhER8dTXNG3alMOHD2cVM5cuXWLTpk106tTpmdtJTU0lMTHxsYc5c3OxZ9bLfuw+d5ufD15VHUdYioxUbTiqeHVoMUZ1Guvl7A7dPoeL2+HwctVphIVIy0xjwt4JVCxakUF+g1THKRDKips7d+6QmZmJl5fXY897eXkRGxv71Ne88sorTJ06lWbNmmFvb0/FihVp1arVc4elZs6ciZubW9bDx8fHqO9DhdZVS/Cvhj5M33iaq/eSVccRlmDnLLhzXhuOsnNQnca6VW4H9QbAliC4f1l1GmEBFkcu5nLiZaY3m469rb3qOAVC+QXFObFz505mzJjBF198wZEjR/jtt9/YuHEj06ZNe+Zrxo8fT0JCQtbj6lXLONsxsXN1iro4MHpVJHq9nL4WeXD1EIQvgFbjwLu26jQCoP10cPaAsCFaCwwhcinydiTfnPyGwf6DqepRVXWcAqOsuPH09MTW1pa4uMf7JsXFxeHt7f3U10yaNInXXnuNt99+m9q1a9OzZ09mzJjBzJkz0T/jA8DR0RFXV9fHHpagiJM9c3v7cSD6Ht9GXFYdR5irtGQIGwSl6kLgcNVpxH84uUKPRXBlLxxcqjqNMFOPMh4RtDeImsVq8matN1XHKVDKihsHBwfq16/P9u3bs57T6/Vs376dJk2ePrdGcnIyNjaPR7a1tQWwyotrm1b05PWmvszefIZLtx+qjiPM0Z/TIOEa9FgCtnaq04j/Vb4FNHoPtk2GOxdUpxFm6LMjn3Ez6SahzUKxs7Gu41vpsNTIkSNZtmwZ3377LVFRUQwePJikpCTeeOMNAAYMGMD48eOzlu/atSuLFy9mxYoVREdHs3XrViZNmkTXrl2zihxrM6ZjVbxdnRi1KpJMGZ4SOXF5L+xfrM1nU7yK6jTiadqGgGtJ7eyaPlN1GmFGDsUe4seoHxladygV3CqojlPglJZyffv25fbt2wQHBxMbG0udOnXYvHlz1kXGMTExj52pCQoKQqfTERQUxPXr1ylevDhdu3Zl+vTpqt6Cci4Odszv40/vJREs3X2Jwa0qqo4kzEHqQwh7H8o2gcaDVacRz+JQSGuB8U1H2PcZNLPMPkDCuJLTk5kUPom6JeryavVXVcdRQmewsvGcxMRE3NzcSEhIsJjrbwBmbori3+GXWT+0GVW9Lat1vcgHG0ZA5AoYHA4e1vetzuxsmQQHlsC7u8Crhuo0wsRNi5jG+kvr+bXrr/i4mv8dwrlhVndLiWcb0a4K5Yq5MGrVMdIz5e4K8RwXtsNf30C7qVLYmIvWE7W/q7BBWosMIZ5h3/V9rDy3klH1R1ltYQNS3FgMJ3tbPu5Th6ibD1i0Qy4+FM/wKF6b/bZCa2j4tuo0IrvsnbThqdiTsGe+6jTCRCWmJRK8L5jGJRvTp2of1XGUkuLGgtQu48aQ1pVY+OcFTl5PUB1HmKI/JkDqA+i+UOtGLcxH6XrQfBTsngs3jqlOI0zQnINzSEpPYmrTqeis/PiW4sbCfNC6ElW8ijBy5TFSM+TuCvE/zmyCYz9Cx5ngVkZ1GpEbLT6CEtW1VhkZqarTCBOyI2YHay+uZUzDMZQsXFJ1HOWkuLEwDnY2fNzXn+g7SSzYdl51HGEqku/B+mFQpSPU6a86jcgtOwfo+SXcvQA7Z6pOI0xEfEo8UyKm0LJMS3pU6qE6jkmQ4sYCVfN2ZXjbKny56yJHYu6rjiNMwcZRkJkGXT+V4Shz51UTWo+H8E+11hnC6k0/MJ10fTohTUKsfjjqP6S4sVDvtaiAX5mijFoZyaM0GZ6yaid/g1O/Qef5UOTprU2EmWk6TGuZETZIa6EhrNYfl/9g8+XNBDUOorhLcdVxTIYUNxbKztaG+X38uRH/iDl/nFEdR6jy8JZ21qZGd6j1suo0wlhs7bSWGQnXYPtU1WmEInce3SF0fyjtyrWjo29H1XFMihQ3Fqxi8cKM6ViNf4dfJuLiXdVxREEzGLTrbGxsofPHMhxlaYpXgTbBcGAxRO9RnUYUMIPBwJSIKdjobAhqHCTDUX8jxY2Fe6OpL43Ke/DR6kgepmaojiMKUuQKOLsJuiyAQp6q04j8EDAYygXC2ve1W/yF1Vh/aT07r+4kuEkwHk4equOYHCluLJyNjY55vfy5l5TGjE1RquOIgpJwHX4fC37/gupdVKcR+cXGBrovgqS7WosGYRVik2KZdWAWXSt0pU3ZNqrjmCQpbqxA2WIuTOhUnZ8OxLDr3G3VcUR+Mxhg3Qfg4AIvzlKdRuQ3j/LQfhoc/jdc2KY6jchnBoOBkH0hONs5M7bRWNVxTJYUN1aif0BZmlf2ZOzq4yQ8kt40Fu3wcrj4J3RbCM7uqtOIgtDgTa2lxtqhWosNYbFWn1/Nvhv7mBI4BTdHN9VxTJYUN1ZCp9Mx+2U/klIzmLL+lOo4Ir/cvwxbgqDeQKjcVnUaUVB0Oq2lRtpD2DxedRqRT649uMbcQ3N5ufLLNCvdTHUckybFjRUpVdSZkG41+e3IdbacilUdRxibXg9hQ8DFAzpMV51GFDS3MvDibIj8SWu1ISyK3qBnUvgkPJw8+KjhR6rjmDwpbqzMy/VK07Z6CSasOcG9pDTVcYQxHVwKV/ZqF5g6FlGdRqjg3w+qvKhNAZAk0z9Ykp+ifuKvuL+Y2nQqhewLqY5j8qS4sTI6nY4ZL9UmQ29g0tqTquMIY7lzHraFQKP3oHwL1WmEKjqd1mJDnw6bRqlOI4wkOiGaBUcW8Eq1V2hUspHqOGZBihsrVKKIE9O612Lj8Zusj7yhOo7IK30mhA0G19LQdrLqNEK1Il5aq41Ta+Dkr6rTiDzK1GcSFB6EdyFvhtcfrjqO2ZDixkp19S9F59olmbT2JLcSU1THEXmx7zO4fhh6LNZu/xai1stQo4fWeuNBnOo0Ig+Wn1rOyTsnCQ0MxdnOWXUcsyHFjRWb1qMWdjY6Jqw5gcFgUB1H5EbcadgxA5oOhbIBqtMIU9L5Y7Cx066/kePbLJ2/f55FxxYxsMZA6pSoozqOWZHixop5FHJgRs/abIu6xa9HrquOI3IqMx3WvAceFaDVBNVphKkpVEy7/ubc7xD5s+o0IofS9elM3DuRskXKMqTuENVxzI4UN1aufU1vXqpXminrTnEj/pHqOCInds+DuFPQcwnYO6lOI0xRtc7aHVS/j9U6iAuzsez4Ms7dP8f05tNxtHVUHcfsSHEjCOlak0KOdoz99bgMT5mLG0dhzzxoMRpK1VWdRpiyjrPAoTCs/UCGp8zEqbunWHZ8Ge/4vUPNYjVVxzFLUtwI3Jztmd3Ljz3n7/DjgRjVccQ/yUiFNYOhRA1oPlp1GmHqnItC98/h0g746xvVacQ/SMtMI2hvEJXdK/Nu7XdVxzFbUtwIAFpWKU6/RmWZsSmKmLvJquOI59kxA+5e0Iaj7BxUpxHmoFJbqP+61jn8XrTqNOI5Fh1bxOXEy4Q2C8Xe1l51HLMlxY3IMrFzdTwKOTB6dSR6vZy+NklXD2q3frceD15yulrkQPtQ7SLjtUO0Vh3C5By7dYzlp5YzpM4QqrhXUR3HrElxI7IUdrRjbi9/Dkbf49/7LquOI/4uLRnWDIJS9aDpMNVphLlxLALdv4Ar4XBgieo04m8eZTwiKDyIWsVq8XrN11XHMXtS3IjHNKlYjDcCfZmz+QwXbz9UHUf8r+1TIPG6Nhxla6c6jTBH5ZtDwGDtd+nOedVpxP/49MinxCbFEtosFDsbOb7zSoob8YQxHapRqqgzo1ZGkpEpp69NQvQe7dt2mxDwrKw6jTBnbYK1Vh1rBkFmhuo0AjgUe4gfo35kWL1hlHcrrzqORZDiRjzB2cGWeb39OX4tnqV7LqmOI1IfwNr3oVwzCBikOo0wdw4u2tm/G0dg36eq01i9pPQkJoVPooFXA/pX7686jsWQ4kY8Vf1y7rzXsiKfbD3HmdhE1XGs25YgSLoL3ReCjRyywgh8GkHTD2HHTG0iSKHMvL/mcS/lHtMCp2Gjk+PbWGRPimca3rYyFTwLM/KXSNIyZHhKiQvb4PBy6BAKHnK6WhhR6wlQrJLWwiMjTXUaqxR+PZzV51YzusFoyhQpozqORZHiRjyTo50t8/v4cy7uAQt3XFAdx/o8ioe1Q6HiC1D/DdVphKWxc9SGp25FabNdiwKVmJZI8L5gmpZqSu8qvVXHsThS3IjnqlXajQ9eqMSiHRc4cS1BdRzrsnkcpCVBt89Bp1OdRliiUnWgxUdan7LrR1SnsSqzD87mUfojpjSdgk6Ob6OT4kb8oyGtK1G9ZBFGrjxGSnqm6jjW4cxGrZPzi7PATU5Xi3zUfBR419LunkpPUZ3GKvwZ8yfrLq5jbKOxeBfyVh3HIklxI/6Rva0N83vX4crdZD7Zek51HMuXdBfWD4OqnbSOzkLkJ1t76LEE7kfDjumq01i8+yn3mRIxhVY+rehWsZvqOBZLihuRLVW9izCiXRWW7rnE4Sv3VMexbBtHgj4DuiyQ4ShRMLxqaBcY7/scYvarTmPRQveHkmnIJKRJiAxH5SMpbkS2vduiAnV8ijJqZSTJaTL5V744+SucDoPOH0MRL9VphDVp+iGUaQBhg7VrvYTRbY7ezJYrWwhqHISns6fqOBZNihuRbbY2Oub39ic2MYU5m8+qjmN5HsTBxlFQsyfUekl1GmFtbGy14anEm7Btsuo0FufOozuEHgilg28HOvp2VB3H4klxI3KkQvHCjO1YjeX7LrPvwh3VcSyHwQDrPwQbe+g0X3UaYa08K0HbyXBwKVzapTqNxTAYDEzeNxk7nR0TAyaqjmMVpLgROTawiS+NK3jw0erjPEhJVx3HMhz7Cc5thq6fQqFiqtMIa9boXfBtDmuHQIrMTm4May+uZde1XYQ0CcHdyV11HKsgxY3IMRsbHXN7+ROfnMb0jVGq45i/hGvanDb+r0C1TqrTCGtnY6O1+nh0H7bIWYa8ik2KZfbB2XSr2I3WZVurjmM1pLgRueLj4cLEzjVYcegqO87eUh3HfBkMsPYDcCgMHWeqTiOExt0X2ofCke/g3BbVacyWwWAgODwYF3sXxjYaqzqOVZHiRuRav0Y+tKhSnLGrj5OQLMNTufLXN3Bph/ZN2bmo6jRC/Ff916FiG1g3FJJl+ofcWHVuFRE3I5jWdBquDq6q41gVKW5Erul0Oma/XJtH6ZlMXi+dhXPsXjRsmaT1jarURnUaIR6n02mtP9Ifwe9y1iGnrj64yry/5tG7Sm+alm6qOo7VkeJG5ElJN2cmd63JmqPX2XwyVnUc86HXQ9j72sXD7aepTiPE07mVhhdnw4mVcHqd6jRmQ2/QE7Q3CA8nD0Y1GKU6jlWS4kbk2Uv1StOuhhcT15zg7sNU1XHMw4HFELMPeiwGxyKq0wjxbP7/gqqdYcMISJLpH7Ljh9M/cOTWEaYFTqOQfSHVcaySFDciz3Q6HTN61kZvMBAUdhKDwaA6kmm7fQ62T4WAweDbTHUaIZ5Pp4OuC8Cghw3DtYvgxTNdSrjEZ0c/49Xqr9LQu6HqOFZLihthFMWLOBLaoza/n4xlXeQN1XFMV2YGhA3SOn23CVadRojsKVwCunwMUevhxGrVaUxWhj6DoL1BlCxUkg/rfag6jlWT4kYYTWe/knTxK0nw2lPEJaaojmOa9n0KN45q09w7uKhOI0T21ewJNV+CTaO1Fg3iCctPLefU3VOENgvF2c5ZdRyrJsWNMKpp3Wthb2vDuF+Py/DU38WehB0ztQaFPnK6WpihzvPBzlFrFSLH92PO3jvLomOLeKPmG/gX91cdx+pJcSOMyr2QA7Neqs2Os7dZ9dc11XFMR0aaNhzlWRlaT1CdRojccfHQWoSc3wJHf1CdxmSkZ6Yzce9EfF19eb/O+6rjCKS4EfmgbQ0vetUvw9QNp7l2P1l1HNOwey7citLujrJzVJ1GiNyr+iLU6Q+bx0N8jOo0JuHL419yMf4i05tNx8HWQXUcgRQ3Ip8Ed61BESc7xqw+jl5v5aevrx+BPfOhxUdQqo7qNELkXceZ4OSmNdfU61WnUerknZN8deIr3vV7lxrFaqiOI/6fFDciX7g62TP7ZT/2XbzLDweuqI6jTnoKrBkE3rWguUzmJSyEkxt0/xyid8NfX6tOo0xqZioT906kinsV3vZ7W3Uc8T+kuBH5pkWV4vQPKMvMTWe4fCdJdRw1doTC/Wjo+SXY2qtOI4TxVHwBGrwFW4Ph7kXVaZRYeHQhVx9cZUazGdjbyPFtSqS4EflqQqfqeBZxYPSqSDKtbXgqZj/sWwitJ0KJ6qrTCGF87aZqc+CEvQ/6TNVpCtSRuCN8e+pbhtQZQiX3SqrjiL+R4kbkq0KOdszr5c/hmPt8szdadZyCk5akDUeVaQhNh6pOI0T+cCwM3b+Aqwdg/xeq0xSY5PRkgsKD8Cvux+s1X1cdRzyFFDci3wVUKMabgeWZu+Us5+MeqI5TMLZNhgex2t1RNraq0wiRf3wDockQ2D4Nbp1RnaZAfHL4E24n3yY0MBRbOb5NkhQ3okB81KEqZdydGb0qkoxMC7+74tJOOLgU2k4GTzldLazAC0HgXg7CBmstRizY/pv7WXF2BcPrD8fXzVd1HPEMUtyIAuFkb8v83v6cuJ7Akl0WfPFhSiKs/QB8m0Ojd1WnEaJg2DtrLUVuHoPwT1SnyTcP0x4SHB5MQ++G9KvWT3Uc8RxS3IgCU7esO4NbVeTT7ec5fSNRdZz88ccEeHQfui8CGzm8hBUpUx+ajYCdsyH2hOo0+WLuX3NJSE1gWuA0bHRyfJsy5X87ixYtwtfXFycnJwICAjh48OBzl4+Pj2fIkCGULFkSR0dHqlSpwqZNmwoorcirD9tUpmLxwoxceYy0DAsbnjq3BY5+D+1DtVP0QliblmOheFXtYvqMNNVpjGr3td38dv43Pmr4EaULl1YdR/yDXBc327dvp0uXLlSsWJGKFSvSpUsXtm3blqN1/PLLL4wcOZKQkBCOHDmCv78/HTp04NatW09dPi0tjXbt2nH58mVWr17N2bNnWbZsGaVLyy+auXC0s2V+H38u3HrIZ9vPq45jPMn3YN1QqNQW6r+uOo0Qatg5ahfR3z4Du2arTmM0CakJTN43mcDSgbxc+WXVcUQ25Kq4+eKLL+jYsSNFihRh2LBhDBs2DFdXVzp16sSiRYuyvZ6PP/6Yd955hzfeeIMaNWqwZMkSXFxc+Oabb566/DfffMO9e/cICwsjMDAQX19fWrZsib+/dGA1JzVLufFhm8os3nWRY1fjVccxjt/HQsYj6PY56HSq0wihTkk/aDkO9n4C1w6rTmMUMw/OJCUzhSlNpqCT49ss6AyGnPetL1OmDOPGjeODDz547PlFixYxY8YMrl+//o/rSEtLw8XFhdWrV9OjR4+s5wcOHEh8fDxr16594jWdOnXCw8MDFxcX1q5dS/HixXnllVcYO3YstrZPvx0vNTWV1NTUrD8nJibi4+NDQkICrq6u2XzHwtjSM/W8vHgfSakZbPywOU72Znw75el1sPI1bRZi/3+pTiOEepkZ8HVbbb6n93ZrFxybqW1XtjFi5whmNJtB14pdVccR2WSXmxfFx8fTsWPHJ55v3749Y8eOzdY67ty5Q2ZmJl5eXo897+XlxZkzT58r4dKlS/z555/079+fTZs2ceHCBd5//33S09MJCQl56mtmzpzJlClTspVJFBx7Wxvm9/an8+d7mb/lLBM7m2nDuYe3YcMIqNYF/PqqTpMrBoOBjAzLvn3XnNna2mJjbhen29ppd0992QL+DIUO01UnypW7j+4ybf80XvB5gS4VuqiOI3IgV8VNt27dWLNmDR999NFjz69du5YuXfLvF0Cv11OiRAmWLl2Kra0t9evX5/r168ydO/eZxc348eMZOXJk1p//c+ZGqFfZqwij21dh5u9naFfDm0blPVRHyhmDATaOAIMeunxilsNRGRkZ3L59m1ycwBUFyMXFBTc3N/MaEilRTZv/ZmswVOsM5ZqqTpQjBoOB0P2h6A16JjWZZF77XuSuuKlRowbTp09n586dNGnSBID9+/cTHh7OqFGj+Oyzz7KW/fDDD5+6Dk9PT2xtbYmLi3vs+bi4OLy9vZ/6mpIlS2Jvb//YEFT16tWJjY0lLS0NBweHJ17j6OiIo6Njjt+jKBhvNavAllNxjF4Vye/DmlPIMVe/kmqcWA1R66H3t1p/HTNjMBiIj4/HxsYGd3d3+fA2QQaDgbS0NBITtakTihYtqjZQTjUZAmc2apP7DQrX2jWYiU3Rm9gWs435Lefj6eypOo7IoVxdc1O+fPnsrVyn49KlS8/8eUBAAI0aNeLzzz8HtDMzZcuW5YMPPmDcuHFPLD9hwgR++uknLl26lHWa9tNPP2X27NncuHEjW5kSExNxc3OTa25MyOU7Sbz46R561S/DtB61VMfJnsSb8EWAdndUr6dfAG/qMjMziYuLw93dHWdn870mwho8fPiQxMREvL29zW+I6u5FWNIM6rwCneerTpMtt5Jv0WNtD5qVasaclnNUxxG5kKuvydHRxmmAOHLkSAYOHEiDBg1o1KgRCxYsICkpiTfeeAOAAQMGULp0aWbOnAnA4MGDWbhwIcOGDWPo0KGcP3+eGTNmPPPskDAPvp6FGN+pGsFrT9GhpjfNKpv4tySDAdZ/CHZO0Gme6jS5ptdr8ww962J8YTr+c1Y6MzPT/IqbYhW17uGbRmvXplVsrTrRcxkMBibvm4yjrSMTAiaojiNySekYQN++fbl9+zbBwcHExsZSp04dNm/enHWRcUxMzGMHso+PD3/88QcjRozAz8+P0qVLM2zYsGxfxCxM16sB5dh8MpYxqyPZPKIFrk72qiM929Hv4fwW6PcLuJjZdUJPIcNRps/s/44avAVR67TWJO/vAyc31Ymeac2FNey5voeFLyykqFNR1XFELmV7WGrkyJFMmzaNQoUKPXaB7tN8/PHHRgmXH2RYynRdu59MxwV76FTbmzm9THTuovgY+KIp1OgOPbI/p5MpSk9P5/bt2xQvXhx7exMuJoVl/F3959ip2V1rT2KCbjy8wUvrXqJduXZMC5ymOo7Ig2yfuTl69Cjp6elZ//0sZv8NQyhTxt2FSV2qM/bXE3So6U2b6l7//KKCpNfD2iHat86OM1SnEcK8FC2rHTfrhkL1blClg+pEj9Eb9ASHB1PEoQhjGo5RHUfkUbaLmx07djz1v4Uwpj4NfNh8MpZxv51gy3B33As9eQecMn99DdG74bUwkz6tbulatWpFnTp1WLBggeooIqfqvqbdYbhuKLy/36SGdX85+wsHYg+wtN1SijgUUR1H5JGZXZkmLJ1Op2PWy36kZegJWXdKdZz/untRm6+jwVsmf0GkECZLp4Oun0FGKvxuOmdHYhJj+OTwJ/St2pcmpZqojiOMIFfFTVJSEpMmTaJp06ZUqlSJChUqPPYQIi+8XJ2Y0q0m6yJvsOnETdVxQJ8JYe9rc9m0m6o6jRDmzbUkdJoLJ1bB6Sfb7BS0TH0mQeFBFHMqxsj6z7+eVJiPXN0t9fbbb7Nr1y5ee+01SpYsKdfZCKPrXqcUm0/GEhR2kkblPfAsrHAixv1fwNUD8MYms5qEzBrcv3+fYcOGsX79elJTU2nZsiWfffYZlStXxmAwUKJECRYvXkyvXr0AqFOnDnFxcdy8qRXNe/fupU2bNty/fx8XFxeVb8W61O6t3T21YQSUbQqFiyuL8v3p7zl26xjfdPgGF3v5HbAUuSpufv/9dzZu3EhgYKCx8wgBaMNToT1r0f6T3Uz47QRfvlZfTRF96wxsn6bNtGpm08fnxqO0TC7efljg261YvDDODjmfb+f111/n/PnzrFu3DldXV8aOHUunTp04ffo09vb2tGjRgp07d9KrVy/u379PVFQUzs7OnDlzhmrVqrFr1y4aNmwohU1B0+mg8yfaRJgbhkPfH5S0L7kYf5HPj37OqzVepYF3gwLfvsg/uSpu3N3d8fAwnQvBhGXyLOzIjJ61GPTDEcKOXadn3TIFGyAzA8IGgXs5rUeOFbh4+yFdPt9b4NvdMLQZtUrn7CLt/xQ14eHhNG2qFZ4//vgjPj4+hIWF0bt3b1q1asWXX34JwO7du6lbty7e3t7s3LmTatWqsXPnTlq2bGn09yOyoXBx6LIAVr6mDVH59SnQzWfoM5i4dyKlCpfiw7oyEaylyVVxM23aNIKDg/n222/lG4/IVx1rlaR7nVKErD1FkwqeeLs5FdzG934CNyPhrW1gbx3tCSoWL8yGoc2UbDenoqKisLOzIyAgIOu5YsWKUbVqVaKiogBo2bIlw4YN4/bt2+zatYtWrVplFTdvvfUW+/btY8wY07mw1erU6KYNUW0aDb7NwLVUgW366xNfE3Uviu9f/B4nuwL8XBEFItvFTd26dR8bFrhw4QJeXl74+vo+ManUkSNHjJdQWL0p3WoScfEuY389zvI3GhbM8NTN47BrNjQbAWXq5//2TISzg22Oz6CYstq1a+Ph4cGuXbvYtWsX06dPx9vbm9mzZ3Po0CHS09OzzvoIRV6cA9F7YN2H0H9VgQxPnbl3hiWRS3iz1pv4FffL9+2Jgpft4qZHjx75GEOIZyvq4sDsl/14Y/khfjl0lX81Kpu/G8xI1boYF68KLaW1h6mqXr06GRkZHDhwIKtAuXv3LmfPnqVGjRqAdu1W8+bNWbt2LadOnaJZs2a4uLiQmprKl19+SYMGDShUqJDKtyFcPKDb5/BTbzjyHdQfmK+bS8tMY+LeiZQvWp7B/oPzdVtCnWwXNyEhIfmZQ4jnal2tBH0b+DBtw2kCK3ni45GPw6G7ZsPtM/DODrBTeJeWeK7KlSvTvXt33nnnHb788kuKFCnCuHHjKF26NN27d89arlWrVowaNYoGDRpQuLA2/NWiRQt+/PFHPvroI1Xxxf+q0l6b4O+PCVChlXadWz5ZErmES/GX+LnLzzjYmtAkocKocjXPzdWrV7l27VrWnw8ePMjw4cNZunSp0YIJ8XdBXapT1MWBMauPo9dnqyVazl37S7vWpuU4KCmnq03dv//9b+rXr0+XLl1o0qQJBoOBTZs2PTZU3rJlSzIzM2nVqlXWc61atXriOaFYhxng7K61OPn/jvXGdvz2cb4++TXv+b9HNY9q+bINYRqy3TjzfzVv3px3332X1157jdjYWKpUqUKtWrU4f/48Q4cOJTg4OD+yGoU0zjRv4Rfu0P+rA0zuWoPXA8sbd+Xpj2BJc20um7e2gW2urrc3GxbRjNFKWM3f1aWd8F137TqcgPeMuuqUjBR6r+9NIftCfN/pe+xtLHg/ityduTl58iSNGjUCYOXKldSuXZt9+/bx448/snz5cmPmE+IxgZU8GdCkHLM2nyH6TpJxV/5nqNa5uMcSiy9shDBJFVpBw3dga4jW8sSIPj/6OTce3mB6s+lS2FiBXBU36enpODpq1yJs27aNbt26AVCtWrWsmT+FyC/jXqyGl6sTo1YeI9NYw1NX9kHEIm0+mxJyuloIZdpNgSLesGaQ1vrECA7HHeb7098ztO5QKhataJR1CtOWq+KmZs2aLFmyhD179rB161Y6duwIwI0bNyhWrJhRAwrxdy4Odszv7c/Rq/F8tedS3leY+lC7O8onQJuJWAihjkMh6LkErh2CiIV5Xl1yejJBe4OoU6IOr9V4zQgBhTnIVXEze/ZsvvzyS1q1akW/fv3w9/cHYN26dVnDVULkpwa+HrzdrDzzt5zjXNyDvK1sWwg8vAU9vgCbnLcAEEIYWdnG0PQDbaj4VlSeVvXx4Y+5m3KX0MBQbOX4tho5vrDAYDBQoUIFYmJiyMjIwN3dPetn7777rsxYLArMqPZV2XH2NqNWRvLb+02xt81FrX5xBxz6CjrNg2JyuloIk9E6CM5t0Yan3t4Gtjm/TibiRgS/nP2FCQETKOuaz/NjCZOS438NDAYDlSpVIjY29rHCBsDX15cSJUoYLZwQz+Nkb8v83v6cvpnI4p25uPgwJQHWfgDlW0KDt4wfUAiRe/ZO0HMxxJ7QpmfIoQdpDwjeF0yAdwB9q/bNh4DClOW4uLGxsaFy5crcvXs3P/IIkSP+PkV5v1VFPtt+npPXE3L24s0TtAKn+yKwydUIrRAiP5WuD81HahNr3ozM0UvnHJrDg7QHTA2cio1Ojm9rk6u/8VmzZvHRRx9x8uRJY+cRIseGvlCZyl5FGLUyktSMbN5dcXYzHPsBOs6Eoj75G1AIkXstxkDx6rBmsNYaJRt2Xd1F2IUwxjQcQ6nCBdeMU5iOXBU3AwYM4ODBg/j7++Ps7IyHh8djDyEKkoOdDfN7+3PpzkM+3Xb+n1+QfA/WfwiV20PdV/M/oBAi9+wctLun7pyDnbP+cfH4lHgmR0ymeenm9KzUswACClOUq5nKFixYYOQYQuRNjVKuDGtTmY+3nqNdDS/qlnV/9sKbPtK+AXb9rEA6EAsh8si7FrQaBzumQ7XOUKbBMxedcXAGaZlpTG46GZ0c31YrV8XNwIH527VViNwY1LIiW0/HMWpVJJs+bI6T/VNu+zwVBidXw0tfgWvJAs8ohMilwOFwdpN299SgPWDv/MQiWy5v4ffo35nVfBYlXOTmFmuW66usLl68SFBQEP369ePWrVsA/P7775w6dcpo4YTICTtbG+b38efa/UfM/ePskws8vAUbR0L1blC7V8EHFGYrMzMTfT41cxTZZGuntUZJuArbpz3x4zuP7hC6P5S2ZdvSqXwnBQGFKclVcbNr1y5q167NgQMH+O2333j48CEAkZGRhISEGDWgEDlRqUQRxnSoyjfh0Ry49D939BkMsGEEoIMun8hwlJnbvHkzzZo1o2jRohQrVowuXbpw8aI2HUDTpk0ZO3bsY8vfvn0be3t7du/eDUBqaiqjR4+mdOnSFCpUiICAAHbu3Jm1/PLlyylatCjr1q2jRo0aODo6EhMTw6FDh2jXrh2enp64ubnRsmVLjhw58ti2zpw5Q7NmzXBycqJGjRps27YNnU5HWFhY1jJXr16lT58+FC1aFA8PD7p3787ly5fzZV9ZlOJV4IVJsP8LuBye9bTBYGBaxDR0Oh1BjYNkOErkrrgZN24coaGhbN26FQcHh6znX3jhBfbv32+0cELkxhuB5WlQzp3RqyNJSs3Qnjy+Es5s0AqbQp5qA5qytGS4cazgH2nJOYqZlJTEyJEj+euvv9i+fTs2Njb07NkTvV5P//79WbFiBQbDf/uO/fLLL5QqVYrmzZsD8MEHHxAREcGKFSs4fvw4vXv3pmPHjpw//98L0pOTk5k9ezZfffUVp06dokSJEjx48ICBAweyd+9e9u/fT+XKlenUqRMPHmizZGdmZtKjRw9cXFw4cOAAS5cuZeLEiY9lT09Pp0OHDhQpUoQ9e/YQHh5O4cKF6dixI2lpaTnaD1ap8WAo20RrmZKqfbHecGkDf179k0mNJ1HMWVoACdAZ/vcTIJsKFy7MiRMnKF++PEWKFCEyMpIKFSpw+fJlqlWrRkpKSn5kNYrExETc3NxISEjA1dVVdRyRT67cTaLjgj28VK8009sUg0WNoUoHeHmZ6mgmIz09ndu3b1O8eHHs7f9/9tcbx2Bpy4IP8+4uKFUn1y+/c+cOxYsX58SJE3h5eVGqVCn+/PPPrGKmadOmtGjRglmzZhETE5M1y3qpUv+9Tbht27Y0atSIGTNmsHz5ct544w2OHTuW1V7mafR6PUWLFuWnn36iS5cubN68ma5du3L16lW8vb0Brblwu3btWLNmDT169OCHH34gNDSUqKiorDMMaWlpFC1alLCwMNq3b//Edp76d2XN7l2CxYHg/y/iWo+j59qetPBpwazm/3w3lbAOubqguGjRoty8eZPy5cs/9vzRo0cpXbq0UYIJkRflihViQqdqTFp7klG3JuDh4AKd5qiOZfo8q2iFhort5sD58+cJDg7mwIED3LlzJ+t6mJiYGGrVqkX79u358ccfad68OdHR0URERPDll18CcOLECTIzM6lS5fFtpqamPtb418HBAT8/v8eWiYuLIygoiJ07d3Lr1i0yMzNJTk4mJiYGgLNnz+Lj45NV2ABP9NuLjIzkwoULFClS5LHnU1JSsobWxD/wqADtp2HYOIqQzGs42TkxvtF41amECclVcfOvf/2LsWPHsmrVKnQ6HXq9nvDwcEaPHs2AAQOMnVGIXOkfUI7UA//G4+ZuknuvwMX5ObeHC42DS57OoBSUrl27Uq5cOZYtW0apUqXQ6/XUqlUra1inf//+fPjhh3z++ef89NNP1K5dm9q1awPw8OFDbG1tOXz4MLa2j99RV7hw4az/dnZ2fuLajYEDB3L37l0+/fRTypUrh6OjI02aNMnRcNLDhw+pX78+P/744xM/K168eLbXY/UavMVvp38kPP4Mi5rPwc3RTXUiYUJyVdzMmDGDIUOG4OPjQ2ZmJjVq1CAzM5NXXnmFoKAgY2cUIldsEmJ4M2kZqw0vsP90KebVVJ1IGMPdu3c5e/Ysy5Ytyxp22rt372PLdO/enXfffZfNmzfz008/Pfalq27dumRmZnLr1q2s12dXeHg4X3zxBZ06aXfjXL16lTt37mT9vGrVqly9epW4uDi8vLwAOHTo0GPrqFevHr/88gslSpSQofE8uJ50gzm2SbyUmEqL4+uhwouqIwkTkqsLih0cHFi2bBkXL15kw4YN/PDDD5w5c4bvv//+iW9CQiih18PaIdi4eGDTcQarD19j2+k41amEEbi7u1OsWDGWLl3KhQsX+PPPPxk5cuRjyxQqVIgePXowadIkoqKi6NevX9bPqlSpQv/+/RkwYAC//fYb0dHRHDx4kJkzZ7Jx48bnbrty5cp8//33REVFceDAAfr374+z83/nW2nXrh0VK1Zk4MCBHD9+nPDw8KwvfP85C9S/f388PT3p3r07e/bsITo6mp07d/Lhhx9y7do1Y+0mi6Y36AkOD8bNqSgfNRoLx36EM5tUxxImJE/dxMqWLcuLL75I7969qVy5srEyCZF3h5bB5T3QfRE9G1ejTbUSjPvtBPeT5G4Uc2djY8OKFSs4fPgwtWrVYsSIEcydO/eJ5fr3709kZCTNmzenbNmyj/3s3//+NwMGDGDUqFFUrVqVHj16cOjQoSeW+7uvv/6a+/fvU69ePV577TU+/PBDSpT472Rxtra2hIWF8fDhQxo2bMjbb7+ddbeUk5MTAC4uLuzevZuyZcvy0ksvUb16dd566y1SUlLkTE42/XzmZw7GHmRa4DQK138TqnSE9cO01ipCkMu7pUA7yD/55JOsWycrV67M8OHDefvtt40a0NjkbikrcOcCLGkG9V6DTto/ercSU2j3yW5aVCnO5/3qKg5oGuQOnIIRHh5Os2bNuHDhAhUrVszVOuTv6r+uJF6h17pe9KjUg4mN//82+wexsCgAKr4Avf+tNqAwCbm65iY4OJiPP/6YoUOH0qRJEwAiIiIYMWIEMTExTJ061aghhcg2faY2/4VrSWg7OevpEq5OTO1ek2ErjtGxpjed/aT1gsgfa9asoXDhwlSuXJkLFy4wbNgwAgMDc13YiP/K1Gcyce9ESriUYET9Ef/9QRFv6Dwffn0LqneFWi+pCylMQq6Km8WLF7Ns2bLHxrG7deuGn58fQ4cOleJGqLPvc7h2CN7cDA6FHvtRN/9S/HEqlqCwEzQq70HxIo6KQgpL9uDBA8aOHUtMTAyenp60bduW+fPnq45lEb47/R3Hbx/n2xe/xcXe5fEf1noZotbBxlHg2wwKS28pa5ara27S09Np0ODJrqz169cnIyMjz6GEyJVbUVrX4KYfQNnGT/xYp9MxrXstbHQ6Jqw5QS5HZIV4rgEDBnDu3DlSUlK4du0ay5cvf2z+HJE7F+5f4POjnzOw5kDqlnjK0LJOB50/Bp2Ndv2NHN9WLVfFzWuvvcbixYufeH7p0qX0798/z6GEyLHMdFjzHriXh9bPno6gWGFHZrxUm62n41hz9HoBBhRC5Fa6Pp2J4RPxKeLDB3U/ePaChTyh66da9/DIFQUXUJicbA9L/e+tljqdjq+++ootW7bQuLH2DfnAgQPExMTIJH5CjT0fQ+xJeHsb2Ds9d9EONb3pWbc0IetO0aRiMUq6OT93eSGEWl+d+Iqz987yY6cfcbT9h+Hk6l3Ary/8PhbKtwA3mTXfGmW7uDl69Ohjf65fvz5A1nThnp6eeHp6curUKSPGEyIbbhyD3XOg+UgoXS9bL5nctSb7Lt5h7K8n+PaNhtJFWAgTFXU3iqWRS3m79tvU9MzmTJwvzobo3bDuA3j1N23ISliVXN8Kbq7kVnALk5EKS1uBjS28/SfYOfzjS/5j59lbvP7vQ8zoWZtXAp4/v4klktuLzYe1/l2lZabRd0Nf7Gzs+KnTT9jb5uC9n98GP74MXRZAgzfyLaMwTXmaxE8I5XbOhDvnoceSHBU2AK2qlqBfIx+mbzzN1XvJ+RRQCJFbiyMXcznxMqGBoTkrbAAqt4V6A+GPiXD/cr7kE6YrV8VNSkoKc+fOpVOnTjRo0IB69eo99hCiQFw9BOGfQqtx4F0rV6uY2LkGRV0cGL0qEr3eqk5iCmHSIm9H8s3Jb3jf/32qelTN3Uo6TAeXYhA2RGvJIqxGroqbt956izlz5lCuXDm6dOlC9+7dH3sIke/SkiFsEJSqC4HDc72awo52zO3tx4Hoeyzfd9lo8UT+adWqFcOHD3/mz3U6HWFhYdle386dO9HpdMTHx+c5mzCORxmPCNobRM1iNXmjVh6GlByLQI9FcGUvHPzSeAGFycvVJH4bNmxg06ZNBAYGGjuPENnz5zRIuAb/+hlsc/VrnKVpRU9eb+rL7M1naFm1OBWLFzZSSKHCzZs3cXd3Vx1D5MFnRz7jZtJNPn3hU+xs8nZ8U74FNHoPtk2GSm3BU/ogWoNcnbkpXbo0RYoUMXYWIbLn8l7Y/wW8MAmKVzHKKsd2rEapos6MXhVJpgxPmTVvb28cHWX2aXN1KPYQP0T9wNC6Q6ngVsE4K20bAq6ltNYsmTLRrDXIVXEzf/58xo4dy5UrV4ydR4jnS30IYe9D2abQeLDRVuvsYMu83n5EXo1n6e5LRluvyB96vZ4xY8bg4eGBt7c3kydPzvrZ34el9u3bR506dXBycqJBgwaEhYWh0+k4duzYY+s8fPgwDRo0wMXFhaZNm3L27NmCeTMiS3J6MpPCJ1GvRD1eq/Ga8VbsUEi76eD6Ydj3mfHWK0xWrs73NWjQgJSUFCpUqICLi8sTtybeuydt50U+2ToJkm7DgDDt9m8jql/Og3daVOCTred4oVoJqnpb39nJRxmPiE6ILvDtlncrj7Nd9idT/Pbbbxk5ciQHDhwgIiKC119/ncDAQNq1a/fYcomJiXTt2pVOnTrx008/ceXKlWderzNx4kTmz59P8eLFGTRoEG+++Sbh4eF5eVsih+b/NZ97KfdY1m4ZNjoj38xbNgCafKDdYVmlA3hlc84cYZZyVdz069eP69evM2PGDLy8vGQCNFEwLmyHv77Ruv96GOl09d+MaFuFP6NuMXLlMcKGBGJva12zJUQnRNN3Q98C3+4vXX6hRrEa2V7ez8+PkJAQACpXrszChQvZvn37E8XNTz/9hE6nY9myZTg5OVGjRg2uX7/OO++888Q6p0+fTsuWLQEYN24cnTt3JiUlBSen5894LYxj3/V9rDy3kqCAIHxcffJnI60nwvktsGYQvPMn5PT2cmE2clXc7Nu3j4iICPz9/Y2dR4inexQP64ZChVbQ4K1824yTvS0f96lDjy/CWfjnBUa0M841PeaivFt5funyi5Lt5oSfn99jfy5ZsiS3bt16YrmzZ8/i5+f3WIHSqFGjf1xnyZIlAbh16xZly1rfBI8FLTEtkeB9wTQp2YQ+Vfvk34bsnaDnEljWBnbPg9bj829bQqlcFTfVqlXj0aNHxs4ixLNtHg+pD6DbwnyfSr12GTeGtK7Eoh0XaFvdi9pl3PJ1e6bE2c45R2dQVPn7ULhOp0Ofx3lM/ned/zkbndd1iuyZfXA2SelJTA2cmv8jAaXqQovRsHsuVO2o/VlYnFydc581axajRo1i586d3L17l8TExMceQhjVmU0Q+RN0nAlF8+l09d980LoSVb2LMGrVMVIzMgtkm8L4qlatyokTJ0hNTc167tChQwoTib/bEbODdRfXMbbRWLwLeRfMRpuP1q65WTMI0lMKZpuiQOWquOnYsSMRERG0adOGEiVK4O7ujru7O0WLFpX5JYRxJd+D9cOgSkeo07/ANutgZ8P8Pv5E30nik63nC2y7wrheeeUV9Ho97777LlFRUfzxxx/MmzcPQK4VNAHxKfFMiZhCyzIt6V6xACeAtXPQhqfuXoSdMwpuu6LA5GpYaseOHcbOIcTTbRwFmWnQ9dMC7+xbzduVEe2qMO+Ps7Sr4UX9clK4mxtXV1fWr1/P4MGDqVOnDrVr1yY4OJhXXnlFLhQ2AdMPTCfDkEFIk5CCLza9amrX3PwZClU7a3dTCYshXcGF6Tr5G6x+A17+Gmr3UhIhI1NPryURJDxKZ9OHzXF2MO7t5ypZa6fpH3/8kTfeeIOEhAScnbN/+7lKlvh3tfnyZj7a9RFzWszhxfIvqgmRmQHfdIBH92HQXnBwUZNDGF2u73Pds2cPr776Kk2bNuX69esAfP/99+zdu9do4YQVexCnnbWp0R1qvawshp2tNjx1I/4RszefUZZD5N53333H3r17iY6OJiwsjLFjx9KnTx+zKWws0Z1Hd5i+fzrty7Wno29HdUFs7bThqcTrsH2KuhzC6HJV3Pz666906NABZ2dnjhw5knWxXkJCAjNmyPilyCODATYM1ybp6/xxgQ9H/V3F4oUZ07Eay/ddZt/FO0qziJyLjY3l1VdfpXr16owYMYLevXuzdOlS1bGslsFgYErEFGx0NgQ1DlJ/7ZNnZWgTAgeWQPRutVmE0eSquAkNDWXJkiUsW7bssVOkgYGBHDlyxGjhhJWKXAFnN0GXBVDIU3UaAN5o6ktAeQ8+WnWch6nSm8acjBkzhsuXL5OSkkJ0dDSffPIJLi4y/KDK+kvr2Xl1J8FNgnF3MpHr2AIGQblACBuiTTkhzF6uipuzZ8/SokWLJ553c3MjPj4+r5mENUu4Dr+PBb9/QfUuqtNksbHRMa+3P/eT05i+8bTqOEKYpdikWGYdmEXXCl1pU7aN6jj/ZWMD3RdB8l34Y6LqNMIIclXceHt7c+HChSee37t3LxUq5M+0+MIKGAyw7gPtor4XZ6lO8wQfDxcmdq7OzwevsvPsk7Phmisru6fALFnC35HBYCBkXwjO9s6MbTRWdZwneZSH9tPgyLdwfpvqNCKPcnUr+DvvvMOwYcP45ptv0Ol03Lhxg4iICEaPHs2kSZOMnVFYi8PL4eKf0P9XcDaR09V/80qjsmw+Gcu4X0/wx/AWuLmY750rtra26HQ6Hjx4QJEiRdRf+yCeYDAYyMzMJDExEZ1Oh51drj6yTcLq86vZd2Mfi9suxs3RRGf9bvAmRK3XWr28v89kP4fEP8vVreAGg4EZM2Ywc+ZMkpOTAXB0dGT06NFMmzbN6CGNSW4FN1H3L8MXTbVbvrt9pjrNc92If0SHBbtpV92Lj/vWUR0nT1JTU7l3755FnBmwZA4ODhQtWtRsi5trD67x0rqX6FyhMyFNQlTHeb6Ea/BFE6jaCV76UnUakUt5mucmLS2NCxcu8PDhQ2rUqEHhwoWNmS1fSHFjgvR6+LYrJMTA4H3gWER1on+0+vA1Rq+K5MvX6tOhZgFNGZ9P9Ho9mZnSYsJU2djYYGNjY7Zn1vQGPW/98RY3k27ya7dfKWRfSHWkf3bsJwgbDH1/NKlr/0T25ehrwJtvvpmt5b755pschVi0aBFz584lNjYWf39/Pv/882d27v1fK1asoF+/fnTv3p2wsLAcbVOYkINfwpW9MHC9WRQ2AC/XK83mkzeZuOYEDX098CjkoDpSrv3nH08h8sNPUT/xV9xffN3+a/MobAD8+8HpddqUFGUbm8xdmyL7cvSJtnz5cnbs2EF8fDz3799/5iMnfvnlF0aOHElISAhHjhzB39+fDh06cOvW8y/YvHz5MqNHj6Z58+Y52p4wMXfOw7bJ0Og9KP/kHXimSqfTMeOl2mToDQSFnZBhHSGeIjohmgVHFtC/en8alfznL6wmQ6fTWr7oM2DjSO1mB2FWcjQsNWTIEH7++WfKlSvHG2+8wauvvoqHh0eeAgQEBNCwYUMWLlwIaKfIfXx8GDp0KOPGjXvqazIzM2nRogVvvvkme/bsIT4+/plnblJTUx/rCJyYmIiPj48MS5mCzAz4d0etOaaZTn2+PvIGQ38+ymf96tLNv5TqOEKYjEx9JgM2DyAhNYFVXVfhbGeGM0Kf/BVWv6m0BYzInRyduVm0aBE3b95kzJgxrF+/Hh8fH/r06cMff/yRq2+uaWlpHD58mLZt2/43kI0Nbdu2JSIi4pmvmzp1KiVKlOCtt976x23MnDkTNze3rIePj0+Oc4p8su8zuH4Yeiw2y8IGoKt/KTr7lSR47UluJaaojiOEyVh+ajkn75wkNDDUPAsb0Fq/1OwJm0bDg1jVaUQO5Hig3dHRkX79+rF161ZOnz5NzZo1ef/99/H19eXhw4c5WtedO3fIzMzEy8vrsee9vLyIjX36L9LevXv5+uuvWbZsWba2MX78eBISErIeV69ezVFGkU/iTsHOmdB0qNl3453WvRZ2NjrG/ybDU0IAnL9/nkXHFjGw5kDqlKijOk7edJoPNnawfpgMT5mRPF1F+J8r+P8zF0N+e/DgAa+99hrLli3D0zN7F3g5Ojri6ur62EMolpkOawaBR0VoNUF1mjzzKOTAzJf82H7mFqsPX1MdRwil0vXpTNw7kXKu5RhSZ4jqOHlXqBh0/QzObdbuohJmIcfFTWpqKj///DPt2rWjSpUqnDhxgoULFxITE5PjW8E9PT2xtbUlLi7usefj4uLw9n7y9tqLFy9y+fJlunbtip2dHXZ2dnz33XesW7cOOzs7Ll68mNO3I1TYPU87c9NzMdg7qU5jFO1qePFSvdJMXX+a6/GPVMcRQpllx5dx7v45QpuF4mjrqDqOcVTrpN1BtXmcNg+OMHk5Km7ef/99SpYsyaxZs+jSpQtXr15l1apVdOrUKVe3kjo4OFC/fn22b9+e9Zxer2f79u00adLkieWrVavGiRMnOHbsWNajW7dutG7dmmPHjsn1NObgxlHYPRdafASl6qpOY1QhXWtSyNGOsauPy/CUsEqn7p5i2fFlvOP3DjWL1VQdx7g6zgKHwrB2iAxPmYEc3S1lY2ND2bJlqVu37nMnlPrtt9+yHeCXX35h4MCBfPnllzRq1IgFCxawcuVKzpw5g5eXFwMGDKB06dLMnDnzqa9//fXXn3u31N/JJH4KpafA0pZg6wDv/Am25tu64Fl2nbvNwG8OMq1HLV5rXE51HCEKTFpmGn039MXexp4fO/+IvY3lHd9c2AY/vAyd50PDt1WnEc+Ro0n8BgwYYPRZMvv27cvt27cJDg4mNjaWOnXqsHnz5qyLjGNiYmSCMUuxcwbcuwTv7rTIwgagZZXivBJQlpmbomhR2ZNyxcxk0jIh8mjRsUVcSbzCii4rLLOwAajUFuq/AVuCoeIL4CGNok1VntovmCM5c6NIzAH4pgO0CYbmI1WnyVcPUzPouGA3pdycWfFuY2xszHPafCGy69itYwzcPJChdYfydm0LP6OR+gAWNwXXMvD6RpAv3yZJ/lZE/ktLgrBBUKYBNP1QdZp8V9jRjnm9/Tl4+R7fhEerjiNEvnqU8Yig8CBqedbi9Zqvq46T/xyLaHNzxeyDA4tVpxHPIMWNyH/bpkDiDe0DwdY8uxrnVOMKxXgj0Jc5f5zlwq2czf8khDn59MinxCbFEhoYip2NdRzf+DaDgMHaZ9vts6rTiKeQ4kbkr0u7tMaYbULAs7LqNAVqTIdqlCnqzKhVkWRk6lXHEcLoDt48yI9RPzKs3jDKu5VXHadgtQmGoj7anF2ZGarTiL+R4kbkn5REWPsBlGsGAYNUpylwzg62zOvjz4lr8Xy5+5LqOEIYVVJ6EpPCJ9HAqwH9q/dXHafgObhoZ6NvHoPwBarTiL+R4kbkny1BkHwXui+02ovu6pV1572WFVmw7RxRNxNVxxHCaOYemsv91PtMDZyKjc46j298GkHgMNg5C2JPqk4j/oeV/kaKfHd+Kxz5FjqEgoeVna7+m+FtK1PBszAjV0aSliHDU8L87b2+l1/P/8roBqPxKWLlk6e2Gq8Nua8ZBBlpqtOI/yfFjTC+R/dh3VBtHoj6b6hOo5yjnS3z+/hzPu4BC/88rzqOEHmSkJpASHgITUs1pXeV3qrjqGfnqA1P3Y7SZl8XJkGKG2F8v4+FtGTothCMPOmjuapV2o2hL1Rm0c6LRF6NVx1HiFybdXAWjzIeMaXpFKNP6mq2StXRWsrsmQ/XD6tOI5DiRhhb1Ho4/gu8OBvcSqtOY1Leb12RGiVdGbUqkpT0TNVxhMix7Ve2s+HSBsY2Got3oSebG1u15qPAuxasGay1mhFKSXEjjCfpDqwfDlU7gf+/VKcxOfa2Nszv40/M3WQ+3npOdRwhcuReyj2m7p9KK59WdKvYTXUc02NrDz2/hPvRsCNUdRqrJ8WNMA6DATaMAIMeuiyQ4ahnqOJVhJHtq7BszyX+unxPdRwhssVgMBC6P5RMQyYhTUJkOOpZSlSH1hNh30KI2a86jVWT4kYYx8lfIWqd1i23iJfqNCbtneYVqOtTlFGrIklOk8m/hOn7Pfp3tl7ZSlDjIDydPVXHMW1Nh0KZhtrdU2lJqtNYLSluRN49iIWNo6DmS1DrJdVpTJ6tjY75feoQl5jC7N/PqI4jxHPdTr7N9APT6eDbgY6+HVXHMX02ttBzifa5uG2y6jRWS4obkTcGA6z7EGwdtLM2IlvKexZiXMdqfBtxhfALd1THEeKpDAYDkyMmY29jz8SAiarjmI9iFaHdFDi4FC7tVJ3GKklxI/Lm2I9w/g/o+im4eKhOY1YGNPGlSYVijFl9nAcp6arjCPGEsAth7L62m5AmIbg7uauOY14avgO+zbUWNCkyO3lBk+JG5F78Vdg8HvxfgWqdVKcxOzY2Oub08iM+OY3pG6NUxxHiMTcf3mTOoTl0q9iN1mVbq45jfmxsoPsibVLTLXLWq6BJcSNyx2CAdR+AYxHoOFN1GrPl4+FCUJcarDh0lR1nbqmOIwSgDUcF7wvGxd6FsY3Gqo5jvtzLQYcZcOQ7OLdFdRqrIsWNyJ2/vtbGkrt9Ds5FVacxa/9q6EPLKsUZ++tx4pOlN41Qb+XZley/uZ9pTafh6uCqOo55qzcAKrXTWtIky/QPBUWKG5Fz9y7BlknQ4E2o1EZ1GrOn0+mY/bIfKemZTF53SnUcYeWuJl5l/uH59K7Sm6alm6qOY/50Ouj2GWQ80lrTiAIhxY3IGX0mhA2BQsWh3TTVaSyGt5sTU7rXJOzYDTafvKk6jrBSmfpMgsKD8HDyYFSDUarjWA7XUvDiXDixEk6vU53GKkhxI3Jm/2KIiYAeX4BjYdVpLEqPOqVpX8OLiWtOcudhquo4wgr9EPUDR24dYVrgNArZF1Idx7L49YFqXbSZ3JNk+of8JsWNyL7bZ2H7VGg8GHybqU5jcXQ6HTNeqo0BCFpzEoPBoDqSsCKX4i/x2ZHPeLX6qzT0bqg6juXR6aDLJ4ABNgzXbsoQ+UaKG5E9mRnadOJFy0KbYNVpLJZnYUdCe9Ri86lY1kXeUB1HWIkMfQYT906kVOFSDKs3THUcy1W4BHT+GKLWw4nVqtNYNCluRPaEL4Cbx7Rpxe2dVaexaJ1ql6SbfymC154iLjFFdRxhBf598t+cvnea0GahONk5qY5j2Wr2gFq9YNNoSJTr6/KLFDfin8WegJ2zIHA4lGmgOo1VmNq9Jg52Noz79bgMT4l8dfbeWb6I/II3a72Jf3F/1XGsQ6e5YOcI6z+U4al8IsWNeL6MNFgzGDyrQKtxqtNYjaIuDsx6qTY7zt5m1V/XVMcRFio9M52JeydS3q08g/0Hq45jPVw8oOtncH4LHP1BdRqLJMWNeL7dc+B2lDYcZeeoOo1VaVPdi971yzB1w2mu3U9WHUdYoCXHl3Ax/iLTA6fjYOugOo51qdoR6ryqtbCJj1GdxuJIcSOe7fph2PMxtBgDJf1Up7FKk7rWwNXJjjGrj6PXy+lrYTwn75zk6xNf867/u1QvVl11HOvUcQY4ucHaIaDXq05jUaS4EU+XnqINR3nXhuYjVaexWq5O9szp5c++i3f54cAV1XGEhUjNTGXi3olU9ajK27XfVh3Hejm5QfeFEL1ba2kjjEaKG/F0O0Lh/mVtOMrWXnUaq9assievNS7HzE1nuHwnSXUcYQEWHl3I1QdXmR44HXsbOb6VqtgaGr4NW4Ph7kXVaSyGFDfiSVciYN9CeGEilJDT1aZg3IvVKF7EkdGrIsmU4SmRB0fijvDtqW8ZWncoldwrqY4jANpO0ebACXtfa3Ej8kyKG/G4tCQIGww+jaDJB6rTiP9XyNGOeb39ORxzn2/2RquOI8xUcnoyQeFB+Bf3Z0CNAarjiP9wLAw9FsPVA7D/C9VpLIIUN+JxW0PgQax2oNnYqk4j/kej8h68FVieuVvOcj7ugeo4wgx9cvgTbiffJrRZKLZyfJuWck2hyRDYPg1unVGdxuxJcSP+69JOOLQM2k2BYhVVpxFPMbpDVXzcnRm1KpKMTLm7QmTf/pv7WXF2BcPrD6ecaznVccTTvBAE7uUgbJDW8kbkmhQ3QpOSCGs/AN/m0PAd1WnEMzjZ2zK/Tx1OXk9g8U65+FBkz8O0hwSHB9PIuxH9qvVTHUc8i70z9FgCNyNh7yeq05g1KW6E5o8J8Og+dF8ENvJrYcrq+BRlcKuKfPbneU7dSFAdR5iBuX/NJSE1gamBU7HRyfFt0srUh2YjYNdsuHlcdRqzJb/lAs79AUe/hw4ztFOiwuR92KYyFYsXZtTKSNIyZHhKPNvua7v57fxvjGk4htKFS6uOI7Kj5VgoXlW7uSMjTXUasyTFjbVLvgfrPoRK7aCe3D1hLhztbJnfx58Ltx7y2fbzquMIE5WQmsDkfZNpVroZL1V+SXUckV12jtpNHbfPaGdwRI5JcWPtfh8DGY+g22eg06lOI3KgZik3hrWpzBc7L3DsarzqOMIEzTgwg5TMFCY3mYxOjm/zUtIPWo6DvR/DtcOq05gdKW6s2el1cGIVvDgXXEupTiNyYXCritQu7caolcdISZfJv8R/bb2ylU3Rm5gQMAGvQl6q44jcaDYCSvprd0+lP1KdxqxIcWOtHt6GDcOhWhfw66M6jcglO1sb5vfx5+r9R8z746zqOMJE3H10l2kR02hTtg2dy3dWHUfklq2ddvfU/SvwZ6jqNGZFihtrZDDAxhHaf3dZIMNRZq5SiSJ81L4qX4dHczD6nuo4QjGDwUDofu0fwkmNJ8lwlLkrUU2b/yZiEVzZpzqN2ZDixhqdWAVR66Hzx1C4uOo0wgjebFae+mXdGb0qkqRUmfzLmm2M3si2mG1MajKJYs7FVMcRxtBkCPgEaHdPpT5UncYsSHFjbRJvwqbRUKsX1OyhOo0wElsbHfN6+3P7QSqzfpep263VreRbzDgwg07lO9GuXDvVcYSx2NhCjy/g4S3YFqI6jVmQ4saaGAywbijYOUGnuarTCCPz9SzE+E7V+H7/Ffaev6M6jihgBoOBkH0hONk6MSFgguo4wtiKVYR2U+HQV3Bxh+o0Jk+KG2ty9Hu4sBW6fQ4uHqrTiHzwakA5AisVY8zqSBJT0lXHEQVozYU17L2+l8lNJ+Pm6KY6jsgPDd6C8i20VjkpMjv580hxYy3iY2DzBKj7KlTpoDqNyCc2Njrm9PInMSWD0A2nVccRBeTGwxvMOTSHnpV60qJMC9VxRH6xsdFa5KQkaC1zxDNJcWMN9HpYOwSc3LQWC8KilS7qTHCXGqz86xrbo+JUxxH5TG/QExwejKuDK2MajlEdR+S3omWh4ww4+gOc3aw6jcmS4sYa/PU1RO+G7gu1AkdYvN4NytC6anHG/XaC+0nSm8aS/XL2Fw7EHmBq4FQKOxRWHUcUhLqvQeX2sP5DrYWOeIIUN5bu7kXYGgwN34aKrVWnEQVEp9Mx62U/0jL0hKw7pTqOyCcxiTF8cvgT+lbtS+OSjVXHEQVFp4Oun0FGKmz6SHUakyTFjSXTZ0LY+1DYC9pOUZ1GFDAvVyemdq/JusgbbDpxU3UcYWSZ+kyCwoPwdPZkZP2RquOIguZaEjrNg5Or4VSY6jQmR4obSxaxCK4e0LrLOsrpamvUzb8UHWt6ExR2kjsPU1XHEUb0/envOXbrGKGBobjYu6iOI1So3Quqd4WNI7U5cEQWKW4s1a0zWi+SJkOgXBPVaYQiOp2O0J610AETfjuBwWBQHUkYwcX4i3x+9HMG1BhAPa96quMIVXQ6rYUOOtgwQpvLTABS3FimzAyti6x7Oa0nibBqnoUdmd6zFltOxxF27LrqOCKPMvQZTNw7kdJFSvNB3Q9UxxGqFfKELp/AmQ1wfKXqNCZDihtLtPcTuHlc6yZr76w6jTABHWuVpEedUoSsPUVsQorqOCIPvj7xNWfunWF64HSc7JxUxxGmoEY3qN0Hfv8IEm+oTmMSpLixNDePw65Z0GwElKmvOo0wIVO61cLJ3paxvx6X4SkzdebeGZZELuHNWm9Su3ht1XGEKek0B+yctRY7cnxLcWNRMlK1rrHFq0HLsarTCBPj5mLP7Jf92HXuNr8cuqo6jsihtMw0Ju6dSIWiFRjsP1h1HGFqnN211joXtsGR71SnUU6KG0uyazbcPgs9l4Cdg+o0wgS1rlaCvg18mLbhNFfvJauOI3JgSeQSLiVcYkazGdjb2quOI0xRlfbaBH9/TID7V1SnUUqKG0tx7S/tWptWY8FbTleLZwvqUp2iLg6MWX0cvV5OX5uD47eP8/XJrxnkN4iqHlVVxxGmrMMM7SzO2iFa6x0rJcWNJUh/BGsGQck6EDhCdRph4oo42TO3lx8Rl+7yXcRl1XHEP0jJSGHi3onU8KjBW7XfUh1HmDonV6255uU9cGiZ6jTKSHFjCf4M1bp+91wCtnaq0wgz0LSSJwOblGPW5jNE30lSHUc8x+dHP+fGwxtMbzYdOxs5vkU2VGgJjd6FrSFaCx4rJMWNubuyT5uJuM0kKC6nq0X2jX2xGt6uToxaeYxMGZ4ySYfjDvP96e/5sN6HVChaQXUcYU7aTtZaNKwZpLXisTImUdwsWrQIX19fnJycCAgI4ODBg89cdtmyZTRv3hx3d3fc3d1p27btc5e3aKkPtbujfAKg8fuq0wgz4+Jgx7ze/hy9Gs9Xey6pjiP+Jjk9maC9QdQpUYdXq7+qOo4wNw6FtNY71w5BxELVaQqc8uLml19+YeTIkYSEhHDkyBH8/f3p0KEDt249vU/Gzp076devHzt27CAiIgIfHx/at2/P9etWOPPq1mCtn0iPL8DGVnUaYYYa+HrwTvMKzN9yjnNxD1THEf/j48MfczflLqGBodjK8S1yo2xjaPqBdunCrSjVaQqUzqB4Nq+AgAAaNmzIwoVaZanX6/Hx8WHo0KGMGzfuH1+fmZmJu7s7CxcuZMCAAf+4fGJiIm5ubiQkJODq6prn/Mpc/BO+76l1hW30juo0woylpGfS5fO9ONnbsOb9QOxtlX/nsXr7buzjva3vMSFgAv2q9VMdR5iz9BT4soU2W/3b28BKphFQ+imWlpbG4cOHadu2bdZzNjY2tG3bloiIiGytIzk5mfT0dDw8PJ7689TUVBITEx97mL2UBFj7AZRvCQ3k7gmRN072tszv7U/UzQd8scM6Lz40JQ/SHhAcHkxAyQD6Vu2rOo4wd/ZO0HMxxJ6APR+rTlNglBY3d+7cITMzEy8vr8ee9/LyIjY2NlvrGDt2LKVKlXqsQPpfM2fOxM3NLevh4+OT59zKbZ4AKYna7X428i1b5J2/T1Heb1WRz/88z8nrCarjWLU5h+bwMP0h05pOw0Ynx7cwgtL1oflI2D0HbhxTnaZAmPWRM2vWLFasWMGaNWtwcnp6A7nx48eTkJCQ9bh61cynnT+7GY79AB1nQlELKNSEyRj6QmUqexVh1MpIUjOs7+4KU7Dr6i7CLoQxtuFYShYuqTqOsCQtxkDx6tpNKBmpqtPkO6XFjaenJ7a2tsTFxT32fFxcHN7e3s997bx585g1axZbtmzBz8/vmcs5Ojri6ur62MNsJd+D9R9C5fZQV+6eEMblYGfDx338uXTnIQu2nVcdx+rEp8QzOWIyzUs3p0elHqrjCEtj56DNhXbnPOycqTpNvlNa3Dg4OFC/fn22b9+e9Zxer2f79u00adLkma+bM2cO06ZNY/PmzTRo0KAgopqGTaO1irvrZ6DTqU4jLFD1kq4Mb1uFL3dd5EjMfdVxrMqMAzNIy0xjctPJ6OT4FvnBuxa0Ggfhn8LVQ6rT5Cvlw1IjR45k2bJlfPvtt0RFRTF48GCSkpJ44403ABgwYADjx4/PWn727NlMmjSJb775Bl9fX2JjY4mNjeXhw4eq3kLBOLUGTv6q3R3lKqerRf55r0UFapcpyqiVkTxKk+GpgrDl8hZ+v/w7EwImUMKlhOo4wpIFDodSdSFsEKRZbvNc5cVN3759mTdvHsHBwdSpU4djx46xefPmrIuMY2JiuHnzZtbyixcvJi0tjV69elGyZMmsx7x581S9hfz38BZsGAnVu0HtXqrTCAtnZ2vD/N7+3Ih/xJw/zqiOY/HuPLrDtP3TaFeuHZ3Kd1IdR1g6WzvosQQSrsGf01SnyTfK57kpaGY3z43BAL+8CjH7YcgBKOSpOpGwEl/tuUToxih+fqcxTSoWUx3HIhkMBobvGM6x28dY030NHk5Pn9JCCKPbtxC2TITXN4JvM9VpjE75mRvxD47/Amc2QJdPpLARBeqNwPI08vXgo9WRPEzNUB3HIm24tIE/r/7JpMaTpLARBavxYCjbFMLeh1TLm51cihtTlnAdNo2B2n2gRjfVaYSVsbXRMbe3H3cfpjFjk3VN3V4QYpNimXlgJp0rdKZtuafP0yVEvrGxhR6LIOk2bJmkOo3RSXFjqgwGWDdUmzK70xzVaYSVKlesEBM6V+enAzHsOndbdRyLYTAYmLxvMs52zoxvNP6fXyBEfvCoAO2mwuF/w4VtqtMYlRQ3purIt3BxO3T7HJzdVacRVuzVgLI0q+TJ2NXHSXiUrjqORfj1/K+E3whnctPJuDm6qY4jrFmDt6BCK1g7FB7Fq05jNFLcmKL7V+CPiVD3NajSXnUaYeV0Oh2ze/mRlJrB1PWnVccxe9cfXmfuobm8XPllmpdprjqOsHY2NtBtIaQ9hM2WcxZRihtTo9fD2iHa2ZoOM1SnEQKA0kWdmdS1Br8eucbW03H//ALxVHqDnknhk3BzdGN0g9Gq4wihKeqjtfSJ/AnObFKdxiikuDE1B5fC5T1aU0wnM7hVXViN3vXL0KZaCcb/doJ7SWmq45iln8/8zKHYQ0wLnEZhh8Kq4wjxX3X6Q5WOsH4YJN1VnSbPpLgxJXcuwLbJ0OhdqNBSdRohHqPT6Zj5Um3SM/VMWntSdRyzcznhMgsOL6BftX4ElAxQHUeIx+l00PVTyEyDTaNUp8kzKW5MhT5T69bqWhLaTladRoinKuHqxLQetdh4/CbrI2+ojmM2MvWZBIUHUcKlBMPrDVcdR4inK+INnef/t92PGZPixlTs+xyuHYIei8GhkOo0QjxTV7+SdKrtzaS1J7n1IEV1HLPw7elvOX77OKHNQnGxd1EdR4hnq/Uy1OgOG0fBA/O9vk6KG1MQdxp2TIemQ6FsY9VphHgunU7HtO61sLPRMeG3k1hZB5ccu3D/AguPLmRgzYHULVFXdRwhnk+ng84fg40dbBiuzblmhqS4US0zXevO6lEBWk9UnUaIbClW2JHpPWuzLSqOX49cVx3HZKXr05mwdwJli5Tlg7ofqI4jRPYU8oQuC+DsJoj8WXWaXJHiRrU98yH2pDYcZe+kOo0Q2dahpjcv1S3NlPWnuBH/SHUck/TVia84d/8c05tNx9HWUXUcIbKvehfw+xf8Pk7rIG5mpLhR6cYx2D0Xmo+C0vVUpxEix0K61sTFwZaxvx6X4am/OX33NEsjl/J27bep6VlTdRwhcu7FWeDgorUCMrPjW4obVTJSYc0gKFEdWnykOo0QueLmYs/sl/3Yc/4OPx2MUR3HZKRlpjFx70QquVfiPb/3VMcRInec3bXZiy/+qfWfMiNS3KiycybcvQA9vwQ7B9VphMi1VlVL0K+RD9M3RhFzN1l1HJPwxbEvuJx4menNpmNva686jhC5V7kt1BsIfwTBvWjVabJNihsVrh6C8E+h1TjwktPVwvxN7FwDj0IOjF4diV5vXqevjS3ydiT/PvVvhtQZQhX3KqrjCJF3HaZDoWKw9gOtRZAZkOKmoKUla3dHlaoLgcNVpxHCKAo72jGnlx8Ho+/x732XVcdR5lHGI4L2BlGzWE1er/m66jhCGIdjEa0l0JW9cPBL1WmyRYqbgrZ9qnbleY8lYGunOo0QRtO0oievN/VlzuYzXLz9UHUcJT478hk3k24S2iwUOxs5voUFKd8CAgZpLYLunFed5h9JcVOQovfAgcXQJhiKy+lqYXnGdqxGqaLOjFoZSUameZy+NpZDsYf4IeoHPqz7IRXcKqiOI4TxtQkB19LazTCZGarTPJcUNwUl9QGsfR/KNoWAwarTCJEvnB1smdfbj+PX4lm655LqOAUmKT2JSeGTqFeiHq/WeFV1HCHyh4OLNifbjSOw7zPVaZ5LipuCsiVIayPfYxHYyG4Xlqt+OQ/eaVGBT7ae40xsouo4BWLeX/O4l3KP0MBQbHRyfAsLVjZAaxW0YwbEnVKd5pnkKCwIF7bB4eXQfqrWZkEICzeibRXKexZi1MpI0jIse3gq/Ho4q8+tZlT9Ufi4+qiOI0T+azUBilXShqcy0lSneSopbvLbo3hYOxQqtIYGb6lOI0SBcLK3ZX7vOpyJfcDCHRdUx8k3iWmJBO8LpknJJvSp2kd1HCEKhr0T9FysnbnZM091mqeS4ia/bR4HaQ+h+0Kt26oQVqJ2GTeGtK7Eoh0XOHEtQXWcfDH74GyS05OZGjgVnRzfwpqUqqvNrr97Htw4qjrNE6S4yU9nNmodVTvOArcyqtMIUeA+aF2Jat5FGLnyGCnpmarjGNWfMX+y7uI6xjYai3chb9VxhCh4LUZrE9GuGQTpKarTPEaKm/ySdBfWD4MqHaHOK6rTCKGEg50N8/v4c+VuMp9sO6c6jtHcT7nPlIgptCzTku4Vu6uOI4QatvZaC6F7l2DnDNVpHiPFTX7ZOBL0GdD1UxmOElatmrcrw9tVZunuSxy+ck91HKMI3R9KpiGTkCYhMhwlrJtXDWg9AcI/g5gDqtNkkeImP5z8FU6HQad5UEROVwvxbvMK1PEpyqiVkSSnmfbkX/9kc/RmtlzZwsSAiRR3Ka46jhDqNf0QyjTQWgulJalOA0hxY3wP4mDjKKjRA2q9rDqNECbBztaGeb39uZmQwpzNZ1XHybU7j+4QeiCU9uXa09G3o+o4QpgGG1utpVDiTdg2RXUaQIob4zIYYP2HYGMHnT+W4Sgh/kfF4oUZ27Eay/ddZt/FO6rj5JjBYGDyvsnY6mwJahwkw1FC/C/PStA2RGusGb1bdRopbozq2E9wbjN0WaC1hxdCPOb1pr4ElPfgo1XHeZCSrjpOjqy9uJZd13YR3CQYdyd31XGEMD2N3oNyzSBsCKSonZ1cihtjSbimzWnj9y+o3kV1GiFMko2Njnm9/bmfnMaMTVGq42RbbFIssw/OpmuFrrQp20Z1HCFMk42N1mLo0T2t5ZDKKEq3bikMBlj7ATgUhhdnqU4jhEnz8XBhYufq/HzwKjvP3lId5x8ZDAaCw4NxsXNhbKOxquMIYdrcfaF9KBz5Fs5vUxZDihtj+OsbuLQDun0OznK6Woh/8kqjsjSv7MnYX4+TkGzaw1Orzq0i4mYEUwKn4ObopjqOEKav/utQsQ2s+wAe3VcSQYqbvLoXDVsmaX+ZlduqTiOEWdDpdMzp5UdyWiZT1ptuZ+GrD64y7695vFz5ZZqVbqY6jhDmQafTvuynJcPv45REkOImL/R6WDtEu3i4fajqNEKYlZJuzkzuWpPfjl7nj1OxquM8QW/QMyl8Eh5OHnzU8CPVcYQwL26l4cXZcHwFRG0o8M1LcZMXB5bAlXDo/gU4FlGdRgiz81K90rSt7sXENSe4+zBVdZzH/Bj1I4fjDjMtcBqF7AupjiOE+fH/F1TtDBuGQ1LBTv8gxU1u3TkP26dAwCAo31x1GiHMkk6nY8ZLtcjUG5i09iQGg0F1JACiE6L59Min9K/en4beDVXHEcI86XTQdQHoM7WWRAV4fEtxkxuZGVoXVNfS0CZEdRohzFqJIk5M61GLTSdiWX/8puo4ZOgzCNobhHchb4bVG6Y6jhDmrXAJ6PIxnF6rtSYqIFLc5Ma+T+HGEei5BBxcVKcRwux18StFF7+STAo7ya3EFKVZlp9azsm7JwkNDMXZzllpFiEsQs2eUPMlrTXRg4K5vk6Km5yKOwU7ZkLToeDTSHUaISzGtO61sLe1YfxvJ5QNT527f45Fxxbxes3XqVOijpIMQlikzvPBzhHWDyuQ4SkpbnIiIw3WvAfFKkHriarTCGFR3As5MPOl2mw/c4tVh68V+PbTM9OZuHcivq6+DKkzpMC3L4RFc/GArp9qLYqO/Zjvm5PiJif2zINbUdBzsVaBCiGMql0NL3rVL8O09ae5Hv+oQLe99MRSLty/wPRm03GwdSjQbQthFaq+CHX6w+bxEH81XzclxU12XT8Cu+dB89FQqq7qNEJYrOCuNSjsZMfY1ccLbHjq1J1TLDu+jHf93qVGsRoFsk0hrFLHmdrUKes+yNfhKSlusiM9BcIGg1dNaDFadRohLJqrkz2zX/Zj74U7/HAgJt+3l5qZysS9E6niXoW3/d7O9+0JYdWc3LTZiy/thL++zrfNSHGTHTtnwL1L0PNLsLVXnUYIi9eiSnH6B5Rl5qYortxNytdtLTq2iJgHMUxvNh17Gzm+hch3ldpAgzdhS7D2b2s+kOLmn8QcgPDPoPUE8JLT1UIUlAmdqlOssAMfrTqOXp8/p6+P3TrG8pPLGVJnCJXdK+fLNoQQT9FuGhTyhLAhWisjI5Pi5nnSkiBsEJRpAE0/VJ1GCKtSyNGOeb38OXTlHt+ERxt9/cnpyUzcOxG/4n68XvN1o69fCPEcjoWhx2KIiYADi42+eilunmfbFEi8CT2WgI2t6jRCWJ2ACsV4o2l55vxxlgu3Hhp13Z8e+ZRbybcIDQzFVo5vIQqebyA0Hqz9W3v7nFFXLcXNs0TvhoNfQtvJ4FlJdRohrNaYjlUp4+7MqFWRZGQa5/T1wZsH+enMTwyvPxxfN1+jrFMIkQttgqFoWW2UJDPDaKuV4uZpUhK1cUDf5tDoXdVphLBqTva2zO/tz4lr8Xy5O+8XHz5Me8ik8Ek09G5Iv2r9jJBQCJFr9s5aK6MbRyF8gdFWK8XN02wJgkf3oPtCsJFdJIRqdcu6M6hlRRZsO8fpG4l5Wte8v+YRnxrP1KZTsdHJ8S2EcmUaQOBw2DkLYk8aZZVyZP/d+a1w5FtoHwruvqrTCCH+37C2lalYvDCjVkWSlpG74ak91/bw6/lfGd1wNGWKlDFyQiFErrUaB55VYM0grdVRHklx878e3Yd1Q6FiG6j/uuo0Qoj/4Whny7ze/pyPe8Dnf57P8esTUhOYvG8ygaUC6VW5Vz4kFELkmp2j1trodhTsnpvn1Ulx879+HwtpydrsiTqd6jRCiL+pVdqNoS9U5oudF4m8Gp+j1846OItHGY+Y3HQyOjm+hTA9Jf2hxRjYMx+uH87TqqS4+Y+o9XD8F+g0B9xKq04jhHiG91tXpEZJV0atiiQlPTNbr9l+ZTsbLm1gfMB4vAt553NCIUSuNR8J3rVhzWCt9VEuSXEDkHQH1g+Hqp3Br6/qNEKI57C3tWF+H39i7ibz8dZ/nhvjXso9pu6fSmuf1nSp0KUAEgohcs3WXrt76n407AjN9WqkuDEYYMMIMOih6wIZjhLCDFTxKsKo9lVYtucShy7fe+ZyBoOB0P2h6A16gpsEy3CUEOagRHV4IQj2LYSY/blahRQ3J3+FqHXQ5WMoXEJ1GiFENr3dvAL1yrrzznd/se103BM/T05PJig8iK1XthLUOAhPZ08FKYUQudLkA/BppN09lZbz5rnWXdw8iIWNo6DmS1Czp+o0QogcsLXR8dWABtQv687b3/3F1PWns24RP3vvLH039GXrla1MbzadDr4dFKcVQuSIja3We+pBLGybnOOX6wwGQ/602zVRiYmJuLm5kRAfj+uGd7RZEYccABcP1dGEELlgMBj4Jvwys36Poqp3ETo3jear05/i6+bLvJbzKO9WXnVEIURuHfgSfh8DA9ZChVbZfplJnLlZtGgRvr6+ODk5ERAQwMGDB5+7/KpVq6hWrRpOTk7Url2bTZs25Xyjx1fC+T+g22dS2AhhxnQ6HW81K893b9fmhsOXfHFyLnU9OvBT55+ksBHC3DV8R2uFtPYDrTVSNikvbn755RdGjhxJSEgIR44cwd/fnw4dOnDr1q2nLr9v3z769evHW2+9xdGjR+nRowc9evTg5MkcTtm8NQTq9IeqLxrhXQghVDp++zhTjryDY5GL1LQbyrY9gYSEneVRWvZuFRdCmCgbG+i+SJtk948J2X6Z8mGpgIAAGjZsyMKFCwHQ6/X4+PgwdOhQxo0b98Tyffv2JSkpiQ0bNmQ917hxY+rUqcOSJUv+cXtZw1LTq+A68iA4uRnvzQghCpTeoOfbU9/y2ZHPqOFZgzkt5lCqUCl+OXSVyetPUdbDhYWv1KOKVxHVUYUQeXH4W1j/IUxOyNbidvkc57nS0tI4fPgw48ePz3rOxsaGtm3bEhER8dTXREREMHLkyMee69ChA2FhYU9dPjU1ldTU1Kw/JyRoO6apiy2237XI4zsQQqhkwECGPoPXqr/Gu/7vYq+358GDB3SqVpQqHn6MXhVJu9mbsZMGuEKYuUJ8bluLZomJFClS5B+ndVBa3Ny5c4fMzEy8vLwee97Ly4szZ8489TWxsbFPXT42Nvapy8+cOZMpU6Y88fypEVG5TC2EMDUT/v9/QgjL1RlgjhsJCQm4uro+d1mlxU1BGD9+/GNneuLj4ylXrhwxMTG4ucmQVF4lJibi4+PD1atX//GXTfwz2Z/GJfvT+GSfGpfsz5wrUuSfh5mVFjeenp7Y2toSF/f4BFxxcXF4ez+9/4u3t3eOlnd0dMTR0fGJ593c3OQXyYhcXV1lfxqR7E/jkv1pfLJPjUv2p3EpHYh2cHCgfv36bN++Pes5vV7P9u3badKkyVNf06RJk8eWB9i6deszlxdCCCGEdVE+LDVy5EgGDhxIgwYNaNSoEQsWLCApKYk33ngDgAEDBlC6dGlmzpwJwLBhw2jZsiXz58+nc+fOrFixgr/++oulS5eqfBtCCCGEMBHKi5u+ffty+/ZtgoODiY2NpU6dOmzevDnrouGYmBhs/udOh6ZNm/LTTz8RFBTEhAkTqFy5MmFhYdSqVStb23N0dCQkJOSpQ1Ui52R/GpfsT+OS/Wl8sk+NS/Zn/lA+z40QQgghhDHJ5A9CCCGEsChS3AghhBDCokhxI4QQQgiLIsWNEEIIISyKRRQ3ixYtwtfXFycnJwICAjh48OBzl1+1ahXVqlXDycmJ2rVrs2nTpsd+bjAYCA4OpmTJkjg7O9O2bVvOnz+fn2/BpBh7f77++uvodLrHHh07dszPt2BScrI/T506xcsvv4yvry86nY4FCxbkeZ2Wxtj7c/LkyU/8flarVi0f34Fpycn+XLZsGc2bN8fd3R13d3fatm37xPLy+Wnc/Wntn5+5ZjBzK1asMDg4OBi++eYbw6lTpwzvvPOOoWjRooa4uLinLh8eHm6wtbU1zJkzx3D69GlDUFCQwd7e3nDixImsZWbNmmVwc3MzhIWFGSIjIw3dunUzlC9f3vDo0aOCelvK5Mf+HDhwoKFjx46GmzdvZj3u3btXUG9JqZzuz4MHDxpGjx5t+Pnnnw3e3t6GTz75JM/rtCT5sT9DQkIMNWvWfOz38/bt2/n8TkxDTvfnK6+8Yli0aJHh6NGjhqioKMPrr79ucHNzM1y7di1rGfn8NO7+tObPz7ww++KmUaNGhiFDhmT9OTMz01CqVCnDzJkzn7p8nz59DJ07d37suYCAAMN7771nMBgMBr1eb/D29jbMnTs36+fx8fEGR0dHw88//5wP78C0GHt/Ggzawdm9e/d8yWvqcro//1e5cuWe+o9xXtZp7vJjf4aEhBj8/f2NmNJ85PV3KSMjw1CkSBHDt99+azAY5PPT2PvTYLDuz8+8MOthqbS0NA4fPkzbtm2znrOxsaFt27ZEREQ89TURERGPLQ/QoUOHrOWjo6OJjY19bBk3NzcCAgKeuU5LkR/78z927txJiRIlqFq1KoMHD+bu3bvGfwMmJjf7U8U6zUV+vvfz589TqlQpKlSoQP/+/YmJiclrXJNnjP2ZnJxMeno6Hh4egHx+Gnt//oc1fn7mlVkXN3fu3CEzMzNrNuP/8PLyIjY29qmviY2Nfe7y//n/nKzTUuTH/gTo2LEj3333Hdu3b2f27Nns2rWLF198kczMTOO/CROSm/2pYp3mIr/ee0BAAMuXL2fz5s0sXryY6OhomjdvzoMHD/Ia2aQZY3+OHTuWUqVKZf2DLp+fxt2fYL2fn3mlvP2CsHz/+te/sv67du3a+Pn5UbFiRXbu3EmbNm0UJhMCXnzxxaz/9vPzIyAggHLlyrFy5UreeusthclM26xZs1ixYgU7d+7EyclJdRyz96z9KZ+fuWPWZ248PT2xtbUlLi7usefj4uLw9vZ+6mu8vb2fu/x//j8n67QU+bE/n6ZChQp4enpy4cKFvIc2YbnZnyrWaS4K6r0XLVqUKlWqyO/nc8ybN49Zs2axZcsW/Pz8sp6Xz0/j7s+nsZbPz7wy6+LGwcGB+vXrs3379qzn9Ho927dvp0mTJk99TZMmTR5bHmDr1q1Zy5cvXx5vb+/HlklMTOTAgQPPXKelyI/9+TTXrl3j7t27lCxZ0jjBTVRu9qeKdZqLgnrvDx8+5OLFi/L7+Qxz5sxh2rRpbN68mQYNGjz2M/n8NO7+fBpr+fzMM9VXNOfVihUrDI6Ojobly5cbTp8+bXj33XcNRYsWNcTGxhoMBoPhtddeM4wbNy5r+fDwcIOdnZ1h3rx5hqioKENISMhTbwUvWrSoYe3atYbjx48bunfvblW3Mhpzfz548MAwevRoQ0REhCE6Otqwbds2Q7169QyVK1c2pKSkKHmPBSmn+zM1NdVw9OhRw9GjRw0lS5Y0jB492nD06FHD+fPns71OS5Yf+3PUqFGGnTt3GqKjow3h4eGGtm3bGjw9PQ23bt0q8PdX0HK6P2fNmmVwcHAwrF69+rFbkx88ePDYMvL5aZz9ae2fn3lh9sWNwWAwfP7554ayZcsaHBwcDI0aNTLs378/62ctW7Y0DBw48LHlV65caahSpYrBwcHBULNmTcPGjRsf+7lerzdMmjTJ4OXlZXB0dDS0adPGcPbs2YJ4KybBmPszOTnZ0L59e0Px4sUN9vb2hnLlyhneeecdq/iH+D9ysj+jo6MNwBOPli1bZnudls7Y+7Nv376GkiVLGhwcHAylS5c29O3b13DhwoUCfEdq5WR/litX7qn7MyQkJGsZ+fw03v6Uz8/c0xkMBkPBnisSQgghhMg/Zn3NjRBCCCHE30lxI4QQQgiLIsWNEEIIISyKFDdCCCGEsChS3AghhBDCokhxI4QQQgiLIsWNEEIIISyKFDdCCCGEsChS3AghLNbly5fR6XQcO3ZMdRQhRAGS4kYIIYQQFkWKGyGEEEJYFCluhBBmbfPmzTRr1oyiRYtSrFgxunTpwsWLF1XHEkIoJMWNEMKsJSUlMXLkSP766y+2b9+OjY0NPXv2RK/Xq44mhFBEuoILISzKnTt3KF68OCdOnKBw4cKUL1+eo0ePUqdOHdXRhBAFRM7cCCHM2vnz5+nXrx8VKlTA1dUVX19fAGJiYtQGE0IoY6c6gBBC5EXXrl0pV64cy5Yto1SpUuj1emrVqkVaWprqaEIIRaS4EUKYrbt373L27FmWLVtG8+bNAdi7d6/iVEII1aS4EUKYLXd3d4oVK8bSpUspWbIkMTExjBs3TnUsIYRics2NEMJs2djYsGLFCg4fPkytWrUYMWIEc+fOVR1LCKGY3C0lhBBCCIsiZ26EEEIIYVGkuBFCCCGERZHiRgghxP+1WwcyAAAAAIP8re/xFUWwIjcAwIrcAAArcgMArMgNALAiNwDAitwAACtyAwCsyA0AsBLP7pxPGSRyJAAAAABJRU5ErkJggg==",
      "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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",
      "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",
    "temp = ctrl.Antecedent(viscosity_train[\"T\"].sort_values().unique(), \"temp\")\n",
    "al = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"al\")\n",
    "ti = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"ti\")\n",
    "viscosity = ctrl.Consequent(np.arange(1.18, 3.71, 0.00001), \"viscosity\")\n",
    "\n",
    "temp.automf(5, variable_type=\"quant\")\n",
    "temp.view()\n",
    "al.automf(3, variable_type=\"quant\")\n",
    "al.view()\n",
    "ti.automf(3, variable_type=\"quant\")\n",
    "ti.view()\n",
    "viscosity.automf(5, variable_type=\"quant\")\n",
    "viscosity.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[IF (temp[higher] AND ti[low]) AND al[low] THEN viscosity[lower]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[average] AND ti[low]) AND al[low] THEN viscosity[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[low] AND ti[low]) AND al[low] THEN viscosity[high]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[lower] AND ti[low]) AND al[low] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[higher] AND ti[low]) AND al[high] THEN viscosity[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[average] AND ti[low]) AND al[high] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[low] AND ti[low]) AND al[high] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[lower] AND ti[low]) AND al[high] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF temp[higher] AND ti[high] THEN viscosity[low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF temp[average] AND ti[high] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF temp[low] AND ti[high] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF temp[lower] AND ti[high] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[higher] AND ti[average]) AND al[low] THEN viscosity[lower]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[average] AND ti[average]) AND al[low] THEN viscosity[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[low] AND ti[average]) AND al[low] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[lower] AND ti[average]) AND al[low] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[average] AND ti[low]) AND al[average] THEN viscosity[average]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[low] AND ti[low]) AND al[average] THEN viscosity[high]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (temp[lower] AND ti[low]) AND al[average] THEN viscosity[higher]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.rules import  get_fuzzy_rules\n",
    "\n",
    "fuzzy_variables = {\"Al2O3\": al, \"TiO2\": ti, \"T\": temp, \"consequent\": viscosity}\n",
    "fuzzy_rules = get_fuzzy_rules(clustered_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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============\n",
      " Antecedents \n",
      "=============\n",
      "Antecedent: temp                    = 20\n",
      "  - lower                           : 1.0\n",
      "  - low                             : 0.0\n",
      "  - average                         : 0.0\n",
      "  - high                            : 0.0\n",
      "  - higher                          : 0.0\n",
      "Antecedent: ti                      = 0.0\n",
      "  - low                             : 1.0\n",
      "  - average                         : 0.0\n",
      "  - high                            : 0.0\n",
      "Antecedent: al                      = 0.0\n",
      "  - low                             : 1.0\n",
      "  - average                         : 0.0\n",
      "  - high                            : 0.0\n",
      "\n",
      "=======\n",
      " Rules \n",
      "=======\n",
      "RULE #0:\n",
      "  IF (temp[higher] AND ti[low]) AND al[low] THEN viscosity[lower]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[higher]                                           : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[low]                                                : 1.0\n",
      "                    (temp[higher] AND ti[low]) AND al[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                          viscosity[lower] : 0.0\n",
      "\n",
      "RULE #1:\n",
      "  IF (temp[average] AND ti[low]) AND al[low] THEN viscosity[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[average]                                          : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[low]                                                : 1.0\n",
      "                   (temp[average] AND ti[low]) AND al[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            viscosity[low] : 0.0\n",
      "\n",
      "RULE #2:\n",
      "  IF (temp[low] AND ti[low]) AND al[low] THEN viscosity[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[low]                                                : 1.0\n",
      "                       (temp[low] AND ti[low]) AND al[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                           viscosity[high] : 0.0\n",
      "\n",
      "RULE #3:\n",
      "  IF (temp[lower] AND ti[low]) AND al[low] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[lower]                                            : 1.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[low]                                                : 1.0\n",
      "                     (temp[lower] AND ti[low]) AND al[low] = 1.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 1.0\n",
      "\n",
      "RULE #4:\n",
      "  IF (temp[higher] AND ti[low]) AND al[high] THEN viscosity[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[higher]                                           : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[high]                                               : 0.0\n",
      "                   (temp[higher] AND ti[low]) AND al[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            viscosity[low] : 0.0\n",
      "\n",
      "RULE #5:\n",
      "  IF (temp[average] AND ti[low]) AND al[high] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[average]                                          : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[high]                                               : 0.0\n",
      "                  (temp[average] AND ti[low]) AND al[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 0.0\n",
      "\n",
      "RULE #6:\n",
      "  IF (temp[low] AND ti[low]) AND al[high] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[high]                                               : 0.0\n",
      "                      (temp[low] AND ti[low]) AND al[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 0.0\n",
      "\n",
      "RULE #7:\n",
      "  IF (temp[lower] AND ti[low]) AND al[high] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[lower]                                            : 1.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[high]                                               : 0.0\n",
      "                    (temp[lower] AND ti[low]) AND al[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 0.0\n",
      "\n",
      "RULE #8:\n",
      "  IF temp[higher] AND ti[high] THEN viscosity[low]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[higher]                                           : 0.0\n",
      "  - ti[high]                                               : 0.0\n",
      "                                 temp[higher] AND ti[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                            viscosity[low] : 0.0\n",
      "\n",
      "RULE #9:\n",
      "  IF temp[average] AND ti[high] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[average]                                          : 0.0\n",
      "  - ti[high]                                               : 0.0\n",
      "                                temp[average] AND ti[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 0.0\n",
      "\n",
      "RULE #10:\n",
      "  IF temp[low] AND ti[high] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 0.0\n",
      "  - ti[high]                                               : 0.0\n",
      "                                    temp[low] AND ti[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 0.0\n",
      "\n",
      "RULE #11:\n",
      "  IF temp[lower] AND ti[high] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[lower]                                            : 1.0\n",
      "  - ti[high]                                               : 0.0\n",
      "                                  temp[lower] AND ti[high] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 0.0\n",
      "\n",
      "RULE #12:\n",
      "  IF (temp[higher] AND ti[average]) AND al[low] THEN viscosity[lower]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[higher]                                           : 0.0\n",
      "  - ti[average]                                            : 0.0\n",
      "  - al[low]                                                : 1.0\n",
      "                (temp[higher] AND ti[average]) AND al[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                          viscosity[lower] : 0.0\n",
      "\n",
      "RULE #13:\n",
      "  IF (temp[average] AND ti[average]) AND al[low] THEN viscosity[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[average]                                          : 0.0\n",
      "  - ti[average]                                            : 0.0\n",
      "  - al[low]                                                : 1.0\n",
      "               (temp[average] AND ti[average]) AND al[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                        viscosity[average] : 0.0\n",
      "\n",
      "RULE #14:\n",
      "  IF (temp[low] AND ti[average]) AND al[low] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 0.0\n",
      "  - ti[average]                                            : 0.0\n",
      "  - al[low]                                                : 1.0\n",
      "                   (temp[low] AND ti[average]) AND al[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 0.0\n",
      "\n",
      "RULE #15:\n",
      "  IF (temp[lower] AND ti[average]) AND al[low] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[lower]                                            : 1.0\n",
      "  - ti[average]                                            : 0.0\n",
      "  - al[low]                                                : 1.0\n",
      "                 (temp[lower] AND ti[average]) AND al[low] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 0.0\n",
      "\n",
      "RULE #16:\n",
      "  IF (temp[average] AND ti[low]) AND al[average] THEN viscosity[average]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[average]                                          : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[average]                                            : 0.0\n",
      "               (temp[average] AND ti[low]) AND al[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                        viscosity[average] : 0.0\n",
      "\n",
      "RULE #17:\n",
      "  IF (temp[low] AND ti[low]) AND al[average] THEN viscosity[high]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[low]                                              : 0.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[average]                                            : 0.0\n",
      "                   (temp[low] AND ti[low]) AND al[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                           viscosity[high] : 0.0\n",
      "\n",
      "RULE #18:\n",
      "  IF (temp[lower] AND ti[low]) AND al[average] THEN viscosity[higher]\n",
      "\tAND aggregation function : fmin\n",
      "\tOR aggregation function  : fmax\n",
      "\n",
      "  Aggregation (IF-clause):\n",
      "  - temp[lower]                                            : 1.0\n",
      "  - ti[low]                                                : 1.0\n",
      "  - al[average]                                            : 0.0\n",
      "                 (temp[lower] AND ti[low]) AND al[average] = 0.0\n",
      "  Activation (THEN-clause):\n",
      "                                         viscosity[higher] : 0.0\n",
      "\n",
      "\n",
      "==============================\n",
      " Intermediaries and Conquests \n",
      "==============================\n",
      "Consequent: viscosity                = 3.499157499995422\n",
      "  lower:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  low:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  average:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  high:\n",
      "    Accumulate using accumulation_max : 0.0\n",
      "  higher:\n",
      "    Accumulate using accumulation_max : 1.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(3.499157499995422)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.input[\"temp\"] = 20\n",
    "sim.input[\"al\"] = 0.0\n",
    "sim.input[\"ti\"] = 0.0\n",
    "sim.compute()\n",
    "sim.print_state()\n",
    "display(sim.output[\"viscosity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Viscosity</th>\n",
       "      <th>ViscosityPred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.707</td>\n",
       "      <td>3.499157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.180</td>\n",
       "      <td>3.188565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.361</td>\n",
       "      <td>2.732494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.832</td>\n",
       "      <td>1.812498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.629</td>\n",
       "      <td>1.812498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.465</td>\n",
       "      <td>1.812498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.194</td>\n",
       "      <td>1.390833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.660</td>\n",
       "      <td>3.481064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.380</td>\n",
       "      <td>3.090537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.874</td>\n",
       "      <td>2.703435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>40</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.489</td>\n",
       "      <td>2.365680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.897</td>\n",
       "      <td>2.054459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>55</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.709</td>\n",
       "      <td>2.128746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.470</td>\n",
       "      <td>1.465795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.670</td>\n",
       "      <td>3.499157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     T  Al2O3  TiO2  Viscosity  ViscosityPred\n",
       "0   20   0.00   0.0      3.707       3.499157\n",
       "1   25   0.00   0.0      3.180       3.188565\n",
       "2   35   0.00   0.0      2.361       2.732494\n",
       "3   45   0.00   0.0      1.832       1.812498\n",
       "4   50   0.00   0.0      1.629       1.812498\n",
       "5   55   0.00   0.0      1.465       1.812498\n",
       "6   70   0.00   0.0      1.194       1.390833\n",
       "7   20   0.05   0.0      4.660       3.481064\n",
       "8   30   0.05   0.0      3.380       3.090537\n",
       "9   35   0.05   0.0      2.874       2.703435\n",
       "10  40   0.05   0.0      2.489       2.365680\n",
       "11  50   0.05   0.0      1.897       2.054459\n",
       "12  55   0.05   0.0      1.709       2.128746\n",
       "13  60   0.05   0.0      1.470       1.465795\n",
       "14  20   0.30   0.0      6.670       3.499157"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fuzzy_pred(row):\n",
    "    sim.input[\"temp\"] = row[\"T\"]\n",
    "    sim.input[\"al\"] = row[\"Al2O3\"]\n",
    "    sim.input[\"ti\"] = row[\"TiO2\"]\n",
    "    sim.compute()\n",
    "    return sim.output[\"viscosity\"]\n",
    "\n",
    "result_train = viscosity_train.copy()\n",
    "result_train[\"ViscosityPred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
    "result_train.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>T</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Viscosity</th>\n",
       "      <th>ViscosityPred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.716</td>\n",
       "      <td>3.089540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.073</td>\n",
       "      <td>2.359522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.329</td>\n",
       "      <td>1.465795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.211</td>\n",
       "      <td>1.414928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.120</td>\n",
       "      <td>3.188565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.217</td>\n",
       "      <td>2.045546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>65</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.315</td>\n",
       "      <td>1.414928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.105</td>\n",
       "      <td>1.408926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.111</td>\n",
       "      <td>3.499157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.735</td>\n",
       "      <td>3.475062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>65</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.936</td>\n",
       "      <td>1.812498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3.587</td>\n",
       "      <td>3.111691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.953</td>\n",
       "      <td>2.128746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.443</td>\n",
       "      <td>1.414928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.990</td>\n",
       "      <td>3.475062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.189</td>\n",
       "      <td>3.475062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.287</td>\n",
       "      <td>1.812498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     T  Al2O3  TiO2  Viscosity  ViscosityPred\n",
       "0   30   0.00  0.00      2.716       3.089540\n",
       "1   40   0.00  0.00      2.073       2.359522\n",
       "2   60   0.00  0.00      1.329       1.465795\n",
       "3   65   0.00  0.00      1.211       1.414928\n",
       "4   25   0.05  0.00      4.120       3.188565\n",
       "5   45   0.05  0.00      2.217       2.045546\n",
       "6   65   0.05  0.00      1.315       1.414928\n",
       "7   70   0.05  0.00      1.105       1.408926\n",
       "8   45   0.30  0.00      3.111       3.499157\n",
       "9   50   0.30  0.00      2.735       3.475062\n",
       "10  65   0.30  0.00      1.936       1.812498\n",
       "11  30   0.00  0.05      3.587       3.111691\n",
       "12  55   0.00  0.05      1.953       2.128746\n",
       "13  65   0.00  0.05      1.443       1.414928\n",
       "14  40   0.00  0.30      3.990       3.475062\n",
       "15  50   0.00  0.30      3.189       3.475062\n",
       "16  65   0.00  0.30      2.287       1.812498"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test = viscosity_test.copy()\n",
    "result_test[\"ViscosityPred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
    "result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_train': 1.0977710360150494,\n",
       " 'RMSE_test': 0.4076186194536602,\n",
       " 'RMAE_test': 0.5797504263400755,\n",
       " 'R2_test': 0.813200460937507}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "rmetrics = {}\n",
    "rmetrics[\"RMSE_train\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_train[\"Viscosity\"], result_train[\"ViscosityPred\"])\n",
    ")\n",
    "rmetrics[\"RMSE_test\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(result_test[\"Viscosity\"], result_test[\"ViscosityPred\"])\n",
    ")\n",
    "rmetrics[\"RMAE_test\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(result_test[\"Viscosity\"], result_test[\"ViscosityPred\"])\n",
    ")\n",
    "rmetrics[\"R2_test\"] = metrics.r2_score(\n",
    "    result_test[\"Viscosity\"], result_test[\"ViscosityPred\"]\n",
    ")\n",
    "\n",
    "rmetrics"
   ]
  }
 ],
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