{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import datasets, metrics\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy import stats\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _wine_dataset:\n",
      "\n",
      "Wine recognition dataset\n",
      "------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 178\n",
      "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      " \t\t- Alcohol\n",
      " \t\t- Malic acid\n",
      " \t\t- Ash\n",
      "\t\t- Alcalinity of ash  \n",
      " \t\t- Magnesium\n",
      "\t\t- Total phenols\n",
      " \t\t- Flavanoids\n",
      " \t\t- Nonflavanoid phenols\n",
      " \t\t- Proanthocyanins\n",
      "\t\t- Color intensity\n",
      " \t\t- Hue\n",
      " \t\t- OD280/OD315 of diluted wines\n",
      " \t\t- Proline\n",
      "\n",
      "    - class:\n",
      "            - class_0\n",
      "            - class_1\n",
      "            - class_2\n",
      "\t\t\n",
      "    :Summary Statistics:\n",
      "    \n",
      "    ============================= ==== ===== ======= =====\n",
      "                                   Min   Max   Mean     SD\n",
      "    ============================= ==== ===== ======= =====\n",
      "    Alcohol:                      11.0  14.8    13.0   0.8\n",
      "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
      "    Ash:                          1.36  3.23    2.36  0.27\n",
      "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
      "    Magnesium:                    70.0 162.0    99.7  14.3\n",
      "    Total Phenols:                0.98  3.88    2.29  0.63\n",
      "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
      "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
      "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
      "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
      "    Hue:                          0.48  1.71    0.96  0.23\n",
      "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
      "    Proline:                       278  1680     746   315\n",
      "    ============================= ==== ===== ======= =====\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML Wine recognition datasets.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
      "\n",
      "The data is the results of a chemical analysis of wines grown in the same\n",
      "region in Italy by three different cultivators. There are thirteen different\n",
      "measurements taken for different constituents found in the three types of\n",
      "wine.\n",
      "\n",
      "Original Owners: \n",
      "\n",
      "Forina, M. et al, PARVUS - \n",
      "An Extendible Package for Data Exploration, Classification and Correlation. \n",
      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
      "\n",
      "Citation:\n",
      "\n",
      "Lichman, M. (2013). UCI Machine Learning Repository\n",
      "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
      "School of Information and Computer Science. \n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  Comparison of Classifiers in High Dimensional Settings, \n",
      "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Technometrics). \n",
      "\n",
      "  The data was used with many others for comparing various \n",
      "  classifiers. The classes are separable, though only RDA \n",
      "  has achieved 100% correct classification. \n",
      "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
      "  (All results using the leave-one-out technique) \n",
      "\n",
      "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
      "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Journal of Chemometrics).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vini=datasets.load_wine()\n",
    "print(vini.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\n",
      "['class_0' 'class_1' 'class_2']\n"
     ]
    }
   ],
   "source": [
    "X = vini.data\n",
    "y = vini.target\n",
    "caratt=vini.feature_names\n",
    "qual=vini.target_names\n",
    "print(caratt)\n",
    "print(qual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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ECRP08ccfq7y8XDt37pTFYpEkjRkzRtHR0Vq4cKG8vLyUn58vHx+fO6q5tLRUXbp0uavPbYLQAADAdefPn1dGRoYWLFigpKQkSVL79u3Vv39/HTt2rEpbHx8fzZ071/HaarUqNzdXK1eu1MiRI2Wz2VRaWqohQ4aoffv2klTlh72wsFDTp09X586dJUlRUVF3VPPKlSuVl5end999947eXxucngAA4LpvvvlGZWVlGjhwoFH7zMxMxcTEqGXLlmratKkWLVqkwsJCSd89XGrs2LGKj4/X0KFDlZGRoaKiIsd7U1NTNX78eMXFxWnevHk6evRorevdsmWLxo0bp8WLF6tbt261fn9tERoAALguICDAuO3y5cv1wgsvKDk5WRs2bFB+fr7GjRun8vJyR5usrCzl5uaqb9++WrFihTp27KgdO3ZIkubMmaMDBw5o8ODB2rx5s7p27arVq1cb73/r1q0aOnSo5s+fr8TERPMPeRcIDQAAXBcVFaWAgABt2rTptm2/+OIL9e3bV1OmTFF0dLQ6dOhQ7WhBdHS00tLStH37dnXv3l3Z2dmObR07dtS0adO0YcMGDR8+XFlZWUZ15uTkaPDgwXr99dc1ceJE8w94l5jTAADAdf7+/po5c6ZmzJghX19f9evXT2fOnNGBAwduOmURFRWlDz74QOvXr5fVatWHH36ovLw8Wa1WSVJBQYEWLVqkJ554QuHh4Tp06JAOHz6sxMREXb58WdOnT9fTTz8tq9WqkydPKi8vTyNGjLhtjVu2bNGQIUP03HPPacSIESouLpYk+fr6KiQkxPlfyvfUOjRs27ZNb775pnbv3q2ioiKtXr1aw4YNc2wfO3asli1bVuU98fHxWrdu3S37zczM1Jtvvqni4mL16tVL/+///T/17t27tuUBAOo4Z92h0VVmzZolb29vzZ49W6dOnVJYWJgmT558U7tJkyZp7969GjVqlCwWi0aPHq0pU6Zo7dq1kqTGjRvr4MGDWrZsmc6ePauwsDClpKRo0qRJunbtms6ePavExESVlJSoRYsWGj58eJWJlTVZtmyZLl26pPT0dKWnpzvWP/LII8rJyXHa91Adi91ut9fmDWvXrtUXX3yhmJgYDR8+vNrQUFJSUmWIxc/PT/fdd1+Nfa5YsUKJiYl655131KdPH7399ttatWqVDh06pFatWt22JpvNpuDgYJWWliooKKg2HwdwK1fcPreu/wMM1902uS4f+ytXrqigoEBWq1X+/v6eLgeq+ZjU5je01iMNCQkJSkhIuGUbPz8/hYaGGvf51ltvacKECRo3bpwk6Z133tGf//xnLVmyRL/+9a9val9WVqaysjLHa5vNZrwvAABwZ1wypyEnJ0etWrXSfffdp8cee0y//e1v1bx582rblpeXa/fu3UpLS3Osa9SokeLi4pSbm1vte9LT042GcIC6Zl9BoadL8BhGWQAzCQkJ+q//+q9qt7344ot68cUX3VzR/3F6aHj88cc1fPhwWa1WHT16VC+++KISEhKUm5srLy+vm9p/++23qqioUOvWrausb926dbV34JK+u1tXamqq47XNZlNERIRzPwgAAB7w3nvv6fLly9Vuc/VEx9txemj46U9/6vjfPXr0UM+ePdW+fXvl5OQY3yzjdvz8/OTn5+eUvgAAqEvuv/9+T5dQI5ffp+EHP/iBWrRooSNHjlS7vUWLFvLy8lJJSUmV9SUlJbWaFwEAqHtqOdceLuSMY+Hy0HDy5EnHpSbV8fX1VUxMTJUbaVRWVmrTpk2KjY11dXkAABe48eClS5cuebgS3HDjWNzpQ7GkOzg9ceHChSqjBgUFBcrPz1dISIhCQkI0d+5cjRgxQqGhoTp69KhmzJihDh06KD4+3vGegQMH6qmnntLUqVMlfXf/7aSkJD344IPq3bu33n77bV28eNFxNQUAoH7x8vJSs2bNdPr0aUnf3bPgxtMd4V52u12XLl3S6dOn1axZs2rnF5qqdWjYtWuXBgwY4Hh9Y0JiUlKSFi5cqK+//lrLli3TuXPnFB4erkGDBumVV16pMgfh6NGj+vbbbx2vR40apTNnzmj27NkqLi7WAw88oHXr1t00ORIAUH/cOMV8IzjAs5o1a3bXp/1rHRoeffTRW54XWb9+/W37+OfHi0rS1KlTHSMPAID6z2KxKCwsTK1atdLVq1c9XU6D5uPjc1cjDDfw7AkAgEt5eXk55QcLnsdTLgEAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBHuCAmg3uqxrIenSwAaFEYaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEW9PFwAA97p9BYUu6bfHsh5O73Nf0j6n94l7ByMNAADACKEBAAAYITQAAAAjhAYAAGCk1qFh27ZtGjp0qMLDw2WxWLRmzRrHtqtXr2rmzJnq0aOHmjRpovDwcCUmJurUqVO37HPOnDmyWCxVls6dO9f6wwAAANepdWi4ePGievXqpczMzJu2Xbp0SXv27NGsWbO0Z88effLJJzp06JCeeOKJ2/bbrVs3FRUVOZbPP/+8tqUBAAAXqvUllwkJCUpISKh2W3BwsDZu3Fhl3YIFC9S7d28VFhYqMjKy5kK8vRUaGlrbcgAAgJu4fE5DaWmpLBaLmjVrdst2hw8fVnh4uH7wgx9ozJgxKiys+brmsrIy2Wy2KgsAAHAtl4aGK1euaObMmRo9erSCgoJqbNenTx8tXbpU69at08KFC1VQUKAf/ehHOn/+fLXt09PTFRwc7FgiIiJc9REAAMB1LgsNV69e1ciRI2W327Vw4cJbtk1ISNAzzzyjnj17Kj4+Xn/5y1907tw5rVy5str2aWlpKi0tdSwnTpxwxUcAAADf45LbSN8IDMePH9fmzZtvOcpQnWbNmqljx446cuRItdv9/Pzk5+fnjFIBAIAhp4803AgMhw8f1l//+lc1b9681n1cuHBBR48eVVhYmLPLAwAAd6jWoeHChQvKz89Xfn6+JKmgoED5+fkqLCzU1atX9fTTT2vXrl366KOPVFFRoeLiYhUXF6u8vNzRx8CBA7VgwQLH6xdeeEFbt27VsWPHtH37dj311FPy8vLS6NGj7/4TAgAAp6j16Yldu3ZpwIABjtepqamSpKSkJM2ZM0f/+Z//KUl64IEHqrxvy5YtevTRRyVJR48e1bfffuvYdvLkSY0ePVpnz55Vy5Yt1b9/f+3YsUMtW7asbXkAAMBFah0aHn30Udnt9hq332rbDceOHavyevny5bUtAwAAuBnPngAAAEYIDQAAwIhLLrkE6rsey3q4pN99LujTFbXuS3JFpQ1XuyvZLuk3UL92Sb/Oxv9H7x2MNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABjx9nQBwF2bE+z8Pq2Rzu+zHumxrIfT+9xXUOj0Pns08ONUX5z/Zp6nS4CTMNIAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwEitQ8O2bds0dOhQhYeHy2KxaM2aNVW22+12zZ49W2FhYQoICFBcXJwOHz58234zMzPVrl07+fv7q0+fPtq5c2dtSwMAAC5U69Bw8eJF9erVS5mZmdVuf+ONN/T73/9e77zzjr788ks1adJE8fHxunLlSo19rlixQqmpqXrppZe0Z88e9erVS/Hx8Tp9+nRtywMAAC5S69CQkJCg3/72t3rqqadu2ma32/X222/rN7/5jZ588kn17NlTH3zwgU6dOnXTiMT3vfXWW5owYYLGjRunrl276p133lHjxo21ZMmS2pYHAABcxKlzGgoKClRcXKy4uDjHuuDgYPXp00e5ubnVvqe8vFy7d++u8p5GjRopLi6uxveUlZXJZrNVWQAAgGt5O7Oz4uJiSVLr1q2rrG/durVj2z/79ttvVVFRUe17Dh48WO170tPTNXfuXCdUDAD1176CQk+XgAamXl49kZaWptLSUsdy4sQJT5cEAMA9z6mhITQ0VJJUUlJSZX1JSYlj2z9r0aKFvLy8avUePz8/BQUFVVkAAIBrOTU0WK1WhYaGatOmTY51NptNX375pWJjY6t9j6+vr2JiYqq8p7KyUps2barxPQAAwP1qPafhwoULOnLkiON1QUGB8vPzFRISosjISD3//PP67W9/q6ioKFmtVs2aNUvh4eEaNmyY4z0DBw7UU089palTp0qSUlNTlZSUpAcffFC9e/fW22+/rYsXL2rcuHF3/wkBAIBT1Do07Nq1SwMGDHC8Tk1NlSQlJSVp6dKlmjFjhi5evKiJEyfq3Llz6t+/v9atWyd/f3/He44ePapvv/3W8XrUqFE6c+aMZs+ereLiYj3wwANat27dTZMjAQCA59Q6NDz66KOy2+01brdYLHr55Zf18ssv19jm2LFjN62bOnWqY+QBAADUPfXy6gkAAOB+hAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIzU+tkTQEOwr6DQ0yUAt9XuSrbT+zzm9B5xL2GkAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwIi3pwtAAzMn2NMV3HP2FRQ6vc8e1kin9+kKDfmzu0qPZT1c0Os8F/QJT2CkAQAAGCE0AAAAI4QGAABghNAAAACMOD00tGvXThaL5aYlJSWl2vZLly69qa2/v7+zywIAAHfJ6VdP5OXlqaKiwvF6//79+vGPf6xnnnmmxvcEBQXp0KFDjtcWi8XZZQEAgLvk9NDQsmXLKq/nzZun9u3b65FHHqnxPRaLRaGhoc4uBQAAOJFL5zSUl5frD3/4g37xi1/ccvTgwoULatu2rSIiIvTkk0/qwIEDt+y3rKxMNputygIAAFzLpaFhzZo1OnfunMaOHVtjm06dOmnJkiX69NNP9Yc//EGVlZXq27evTp48WeN70tPTFRwc7FgiIiJcUD0AAPg+l4aG999/XwkJCQoPD6+xTWxsrBITE/XAAw/okUce0SeffKKWLVvq3XffrfE9aWlpKi0tdSwnTpxwRfkAAOB7XHYb6ePHj+uvf/2rPvnkk1q9z8fHR9HR0Tpy5EiNbfz8/OTn53e3JQIAgFpw2UhDVlaWWrVqpcGDB9fqfRUVFdq3b5/CwsJcVBkAALgTLgkNlZWVysrKUlJSkry9qw5mJCYmKi0tzfH65Zdf1oYNG/Q///M/2rNnj/71X/9Vx48f1/jx411RGgAAuEMuOT3x17/+VYWFhfrFL35x07bCwkI1avR/WeUf//iHJkyYoOLiYt13332KiYnR9u3b1bVrV1eUBgAA7pBLQsOgQYNkt9ur3ZaTk1Pl9fz58zV//nxXlAEAAJyIZ08AAAAjhAYAAGDEZZdcAoCrtbuS7fQ+A/Vrp/cJ3CsYaQAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEa8PV0A6rA5wZ6uAPeQdleyPV2CkX0FhU7vs53Te3Sd89/M83QJqMMYaQAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABgxOmhYc6cObJYLFWWzp073/I9q1atUufOneXv768ePXroL3/5i7PLAgAAd8klIw3dunVTUVGRY/n8889rbLt9+3aNHj1aycnJ2rt3r4YNG6Zhw4Zp//79rigNAADcIW+XdOrtrdDQUKO2GRkZevzxxzV9+nRJ0iuvvKKNGzdqwYIFeuedd6p9T1lZmcrKyhyvbTbb3RcNAABuySWh4fDhwwoPD5e/v79iY2OVnp6uyMjIatvm5uYqNTW1yrr4+HitWbOmxv7T09M1d+5cZ5YMAJKkdleyPV0CUGc5/fREnz59tHTpUq1bt04LFy5UQUGBfvSjH+n8+fPVti8uLlbr1q2rrGvdurWKi4tr3EdaWppKS0sdy4kTJ5z6GQAAwM2cPtKQkJDg+N89e/ZUnz591LZtW61cuVLJyclO2Yefn5/8/Pyc0hcAADDj8ksumzVrpo4dO+rIkSPVbg8NDVVJSUmVdSUlJcZzIgAAgHu4PDRcuHBBR48eVVhYWLXbY2NjtWnTpirrNm7cqNjYWFeXBgAAasHpoeGFF17Q1q1bdezYMW3fvl1PPfWUvLy8NHr0aElSYmKi0tLSHO2fe+45rVu3Tr/73e908OBBzZkzR7t27dLUqVOdXRoAALgLTp/TcPLkSY0ePVpnz55Vy5Yt1b9/f+3YsUMtW7aUJBUWFqpRo//LKn379lV2drZ+85vf6MUXX1RUVJTWrFmj7t27O7s0AABwF5weGpYvX37L7Tk5OTete+aZZ/TMM884uxQAAOBEPHsCAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIx4e7oAOMmcYE9XAAC4xzHSAAAAjBAaAACAEUIDAAAwQmgAAABGnB4a0tPT9cMf/lCBgYFq1aqVhg0bpkOHDt3yPUuXLpXFYqmy+Pv7O7s0AABwF5weGrZu3aqUlBTt2LFDGzdu1NWrVzVo0CBdvHjxlu8LCgpSUVGRYzl+/LizSwMAAHfB6Zdcrlu3rsrrpUuXqlWrVtq9e7cefvjhGt9nsVgUGhrq7HIAAICTuHxOQ2lpqSQpJCTklu0uXLigtm3bKiIiQk8++aQOHDhQY9uysjLZbLYqCwAAcC2XhobKyko9//zz6tevn7p3715ju06dOmnJkiX69NNP9Yc//EGVlZXq27evTp48WW379PR0BQcHO5aIiAhXfQQAAHCdxW63213V+S9/+UutXbtWn3/+udq0aWP8vqtXr6pLly4aPXq0XnnllZu2l5WVqayszPHaZrMpIiJCpaWlCgoKckrt9Q53hIQT9bBGOr3P89/Mc3qfaLiOzRvs6RLuGTabTcHBwUa/oS67jfTUqVP12Wefadu2bbUKDJLk4+Oj6OhoHTlypNrtfn5+8vPzc0aZAADAkNNPT9jtdk2dOlWrV6/W5s2bZbVaa91HRUWF9u3bp7CwMGeXBwAA7pDTRxpSUlKUnZ2tTz/9VIGBgSouLpYkBQcHKyAgQJKUmJio+++/X+np6ZKkl19+WQ899JA6dOigc+fO6c0339Tx48c1fvx4Z5cHAADukNNDw8KFCyVJjz76aJX1WVlZGjt2rCSpsLBQjRr93yDHP/7xD02YMEHFxcW67777FBMTo+3bt6tr167OLg8AANwhp4cGk3mVOTk5VV7Pnz9f8+fPd3YpAADAiXj2BAAAMEJoAAAARlx2ySWA+mtfQaHT+2zn9B4BuBsjDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEa8PV1AnTcn2AV9ljq/T8CJ2l3J9nQJgNu1+/WfPV2CkWPzBnts34w0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADDistCQmZmpdu3ayd/fX3369NHOnTtv2X7VqlXq3Lmz/P391aNHD/3lL39xVWkAAOAOuCQ0rFixQqmpqXrppZe0Z88e9erVS/Hx8Tp9+nS17bdv367Ro0crOTlZe/fu1bBhwzRs2DDt37/fFeUBAIA74JLQ8NZbb2nChAkaN26cunbtqnfeeUeNGzfWkiVLqm2fkZGhxx9/XNOnT1eXLl30yiuv6F/+5V+0YMECV5QHAADugLezOywvL9fu3buVlpbmWNeoUSPFxcUpNze32vfk5uYqNTW1yrr4+HitWbOm2vZlZWUqKytzvC4tLZUk2Wy2u6y+up3Znd9nfakTDVZl2SVPlwDckiv+va8v/7939me/0Z/dfvvfEaeHhm+//VYVFRVq3bp1lfWtW7fWwYMHq31PcXFxte2Li4urbZ+enq65c+fetD4iIuIOq3azecGergC4jZGeLgC4peC3PV2B57jqs58/f17Bwbf+fXJ6aHCHtLS0KiMTlZWV+vvf/67mzZvLYrF4sLKqbDabIiIidOLECQUFBXm6HIhjUhdxTOoejknd48pjYrfbdf78eYWHh9+2rdNDQ4sWLeTl5aWSkpIq60tKShQaGlrte0JDQ2vV3s/PT35+flXWNWvW7M6LdrGgoCD+8OoYjkndwzGpezgmdY+rjsntRhhucPpESF9fX8XExGjTpk2OdZWVldq0aZNiY2OrfU9sbGyV9pK0cePGGtsDAAD3c8npidTUVCUlJenBBx9U79699fbbb+vixYsaN26cJCkxMVH333+/0tPTJUnPPfecHnnkEf3ud7/T4MGDtXz5cu3atUuLFi1yRXkAAOAOuCQ0jBo1SmfOnNHs2bNVXFysBx54QOvWrXNMdiwsLFSjRv83yNG3b19lZ2frN7/5jV588UVFRUVpzZo16t69uyvKcxs/Pz+99NJLN51KgedwTOoejkndwzGpe+rKMbHYTa6xAAAADR7PngAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNBwl7Zt26ahQ4cqPDxcFovlpodsffLJJxo0aJDjFtf5+fkeqbOhudVxuXr1qmbOnKkePXqoSZMmCg8PV2Jiok6dOuW5ghuA2/2tzJkzR507d1aTJk103333KS4uTl9++aVnim0gbndMvm/y5MmyWCx6++233VZfQ3S7YzJ27FhZLJYqy+OPP+62+ggNd+nixYvq1auXMjMza9zev39/vf76626urGG71XG5dOmS9uzZo1mzZmnPnj365JNPdOjQIT3xxBMeqLThuN3fSseOHbVgwQLt27dPn3/+udq1a6dBgwbpzJkzbq604bjdMblh9erV2rFjh9GzCXB3TI7J448/rqKiIsfy8ccfu69AO5xGkn316tXVbisoKLBLsu/du9etNeHWx+WGnTt32iXZjx8/7p6iGjiTY1JaWmqXZP/rX//qnqIauJqOycmTJ+3333+/ff/+/fa2bdva58+f7/baGqrqjklSUpL9ySef9Eg9drvdzkgDIKm0tFQWi6VOP/isISkvL9eiRYsUHBysXr16ebqcBquyslI///nPNX36dHXr1s3T5eC6nJwctWrVSp06ddIvf/lLnT171m37rpePxgac6cqVK5o5c6ZGjx7NE/087LPPPtNPf/pTXbp0SWFhYdq4caNatGjh6bIarNdff13e3t761a9+5elScN3jjz+u4cOHy2q16ujRo3rxxReVkJCg3NxceXl5uXz/hAY0aFevXtXIkSNlt9u1cOFCT5fT4A0YMED5+fn69ttvtXjxYo0cOVJffvmlWrVq5enSGpzdu3crIyNDe/bskcVi8XQ5uO6nP/2p43/36NFDPXv2VPv27ZWTk6OBAwe6fP+cnkCDdSMwHD9+XBs3bmSUoQ5o0qSJOnTooIceekjvv/++vL299f7773u6rAbpv/7rv3T69GlFRkbK29tb3t7eOn78uP6//+//U7t27TxdHq77wQ9+oBYtWujIkSNu2R8jDWiQbgSGw4cPa8uWLWrevLmnS0I1KisrVVZW5ukyGqSf//zniouLq7IuPj5eP//5zzVu3DgPVYV/dvLkSZ09e1ZhYWFu2R+h4S5duHChSsIrKChQfn6+QkJCFBkZqb///e8qLCx03APg0KFDkqTQ0FCFhoZ6pOaG4FbHJSwsTE8//bT27Nmjzz77TBUVFSouLpYkhYSEyNfX11Nl39NudUyaN2+uV199VU888YTCwsL07bffKjMzU//7v/+rZ555xoNV39tu9+/XP4dpHx8fhYaGqlOnTu4utcG41TEJCQnR3LlzNWLECIWGhuro0aOaMWOGOnTooPj4ePcU6LHrNu4RW7ZssUu6aUlKSrLb7XZ7VlZWtdtfeuklj9Z9r7vVcblx+Wt1y5YtWzxd+j3rVsfk8uXL9qeeesoeHh5u9/X1tYeFhdmfeOIJ+86dOz1d9j3tdv9+/TMuuXS9Wx2TS5cu2QcNGmRv2bKl3cfHx962bVv7hAkT7MXFxW6rz2K32+2ujSUAAOBewERIAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARb08X4AyVlZU6deqUAgMDZbFYPF0OAAD1ht1u1/nz5xUeHq5GjW49lnBPhIZTp04pIiLC02UAAFBvnThxQm3atLllm3siNAQGBkr67gMHBQV5uBoAAOoPm82miIgIx2/prdwToeHGKYmgoCBCAwAAd8Dk9D4TIQEAgBFCAwAAMEJoAAAARu6JOQ0AgLqroqJCV69e9XQZDZqPj4+8vLzuuh9CAwDAJex2u4qLi3Xu3DlPlwJJzZo1U2ho6F3dz4jQAABwiRuBoVWrVmrcuDE33/MQu92uS5cu6fTp05KksLCwO+6L0AAAcLqKigpHYGjevLmny2nwAgICJEmnT59Wq1at7vhUBRMhAQBOd2MOQ+PGjT1cCW64cSzuZn4JoQEA4DKckqg7nHEsCA0AAMAIoQEAAAPHjh2TxWJRfn6+p0vxGCZCAgDcqt2v/+zW/R2bN9it+3OHr7/+WikpKcrLy1PLli317LPPasaMGS7fLyMNAADUIzabTYMGDVLbtm21e/duvfnmm5ozZ44WLVrk8n0z0gC36rGsh9P73Je0z+l9Ami4Kisr9W//9m9atGiRTpw4odatW2vSpEkaM2ZMlXYVFRWaOHGiNm/erOLiYkVGRmrKlCl67rnnHG1ycnI0Y8YMHThwQD4+PurWrZuys7PVtm1bffXVV3r++ee1a9cuWSwWRUVF6d1339WDDz54y/o++ugjlZeXa8mSJfL19VW3bt2Un5+vt956SxMnTnTJd3IDoQEAgO9JS0vT4sWLNX/+fPXv319FRUU6ePDgTe0qKyvVpk0brVq1Ss2bN9f27ds1ceJEhYWFaeTIkbp27ZqGDRumCRMm6OOPP1Z5ebl27tzpuIphzJgxio6O1sKFC+Xl5aX8/Hz5+Pjctr7c3Fw9/PDD8vX1dayLj4/X66+/rn/84x+67777nPdl/BNCAwAA150/f14ZGRlasGCBkpKSJEnt27dX//79dezYsSptfXx8NHfuXMdrq9Wq3NxcrVy5UiNHjpTNZlNpaamGDBmi9u3bS5K6dOniaF9YWKjp06erc+fOkqSoqCijGouLi2W1Wqusa926tWObK0MDcxoAALjum2++UVlZmQYOHGjUPjMzUzExMWrZsqWaNm2qRYsWqbCwUJIUEhKisWPHKj4+XkOHDlVGRoaKiooc701NTdX48eMVFxenefPm6ejRoy75TM5EaAAA4Lobt1s2sXz5cr3wwgtKTk7Whg0blJ+fr3Hjxqm8vNzRJisrS7m5uerbt69WrFihjh07aseOHZKkOXPm6MCBAxo8eLA2b96srl27avXq1bfdb2hoqEpKSqqsu/E6NDTUuP47QWgAAOC6qKgoBQQEaNOmTbdt+8UXX6hv376aMmWKoqOj1aFDh2pHC6Kjo5WWlqbt27ere/fuys7Odmzr2LGjpk2bpg0bNmj48OHKysq67X5jY2O1bdu2KreD3rhxozp16uTSUxMSoQEAAAd/f3/NnDlTM2bM0AcffKCjR49qx44dev/9929qGxUVpV27dmn9+vX67//+b82aNUt5eXmO7QUFBUpLS1Nubq6OHz+uDRs26PDhw+rSpYsuX76sqVOnKicnR8ePH9cXX3yhvLy8KnMeavKzn/1Mvr6+Sk5O1oEDB7RixQplZGQoNTXVqd9FdZgICQDA98yaNUve3t6aPXu2Tp06pbCwME2ePPmmdpMmTdLevXs1atQoWSwWjR49WlOmTNHatWslffeAqIMHD2rZsmU6e/aswsLClJKSokmTJunatWs6e/asEhMTVVJSohYtWmj48OFVJlbWJDg4WBs2bFBKSopiYmLUokULzZ492+WXW0qSxW63212+Fxez2WwKDg5WaWmpgoKCPF0OboH7NAANw5UrV1RQUCCr1Sp/f39PlwPVfExq8xvK6QkAAGCE0AAAQB2SkJCgpk2bVru89tprHq2NOQ0AANQh7733ni5fvlzttpCQEDdXUxWhAQCAOuT+++/3dAk14vQEAAAwwkgD3GpfQaGnSwAA3CFGGgAAgBFCAwAAMEJoAAAARggNAAAYOHbsmCwWi/Lz8z1discwERIA4F5zgt28v1L37s/Frly5osmTJ2v37t365ptvNGTIEK1Zs8Yt+2akAQCAeqSiokIBAQH61a9+pbi4OLfum9AAAMD3VFZW6o033lCHDh3k5+enyMhIvfrqqze1q6ioUHJysqxWqwICAtSpUydlZGRUaZOTk6PevXurSZMmatasmfr166fjx49Lkr766isNGDBAgYGBCgoKUkxMjHbt2nXb+po0aaKFCxdqwoQJCg0Ndc6HNsTpCQAAvictLU2LFy/W/Pnz1b9/fxUVFengwYM3tausrFSbNm20atUqNW/eXNu3b9fEiRMVFhamkSNH6tq1axo2bJgmTJigjz/+WOXl5dq5c6csFoskacyYMYqOjtbChQvl5eWl/Px8+fj4uPvj1gqhAQCA686fP6+MjAwtWLBASUlJkqT27durf//+OnbsWJW2Pj4+mjt3ruO11WpVbm6uVq5cqZEjR8pms6m0tFRDhgxR+/btJUldunRxtC8sLNT06dPVuXNnSVJUVJSLP93d4/QEAADXffPNNyorK9PAgQON2mdmZiomJkYtW7ZU06ZNtWjRIhUWfnfn25CQEI0dO1bx8fEaOnSoMjIyVFRU5Hhvamqqxo8fr7i4OM2bN09Hjx51yWdyJkIDAADXBQQEGLddvny5XnjhBSUnJ2vDhg3Kz8/XuHHjVF5e7miTlZWl3Nxc9e3bVytWrFDHjh21Y8cOSdKcOXN04MABDR48WJs3b1bXrl21evVqp38mZyI0AABwXVRUlAICArRp06bbtv3iiy/Ut29fTZkyRdHR0erQoUO1owXR0dFKS0vT9u3b1b17d2VnZzu2dezYUdOmTdOGDRs0fPhwZWVlOfXzOBtzGgAAuM7f318zZ87UjBkz5Ovrq379+unMmTM6cODATacsoqKi9MEHH2j9+vWyWq368MMPlZeXJ6vVKkkqKCjQokWL9MQTTyg8PFyHDh3S4cOHlZiYqMuXL2v69Ol6+umnZbVadfLkSeXl5WnEiBFGdf7tb39TeXm5/v73v+v8+fOOG0498MADzvw6bkJoAADge2bNmiVvb2/Nnj1bp06dUlhYmCZPnnxTu0mTJmnv3r0aNWqULBaLRo8erSlTpmjt2rWSpMaNG+vgwYNatmyZzp49q7CwMKWkpGjSpEm6du2azp49q8TERJWUlKhFixYaPnx4lYmVt/KTn/zEcemm9N1ohiTZ7XYnfAM1s9hdvQc3sNlsCg4OVmlpqYKCgjxdDm7FFXeCu8fu9gbcC65cuaKCggJZrVb5+/t7uhyo5mNSm99Q5jQAAAAjhAYAAOqQhIQENW3atNrltdde82htzGkAAKAOee+993T58uVqt4WEhLi5mqoIDQAA1CH333+/p0uoEacnAACAEUIDAAAwQmgAAABGCA0AAMCI20PDvHnzZLFY9PzzzzvWXblyRSkpKWrevLmaNm2qESNGqKSkxN2lAQCAW3BraMjLy9O7776rnj17Vlk/bdo0/elPf9KqVau0detWnTp1SsOHD3dnaQAA4DbcdsnlhQsXNGbMGC1evFi//e1vHetLS0v1/vvvKzs7W4899pik7x4l2qVLF+3YsUMPPfSQu0oEALhBj2U93Lq/fUn7nNLPsWPHZLVatXfvXpc/GKqucttIQ0pKigYPHqy4uLgq63fv3q2rV69WWd+5c2dFRkYqNze32r7Kyspks9mqLAAANAQ5OTl68sknFRYWpiZNmuiBBx7QRx995JZ9u2WkYfny5dqzZ4/y8vJu2lZcXCxfX181a9asyvrWrVuruLi42v7S09ONnwQGAMC9ZPv27erZs6dmzpyp1q1b67PPPlNiYqKCg4M1ZMgQl+7b5SMNJ06c0HPPPaePPvrIaU86S0tLU2lpqWM5ceKEU/oFAKCyslJvvPGGOnToID8/P0VGRurVV1+9qV1FRYWSk5NltVoVEBCgTp06KSMjo0qbnJwc9e7dW02aNFGzZs3Ur18/xyOtv/rqKw0YMECBgYEKCgpSTEyMdu3addv6XnzxRb3yyivq27ev2rdvr+eee06PP/64PvnkE+d8Abfg8pGG3bt36/Tp0/qXf/kXx7qKigpt27ZNCxYs0Pr161VeXq5z585VGW0oKSlRaGhotX36+fnJz8/P1aUDABqgtLQ0LV68WPPnz1f//v1VVFSkgwcP3tSusrJSbdq00apVq9S8eXNt375dEydOVFhYmEaOHKlr165p2LBhmjBhgj7++GOVl5dr586dslgskqQxY8YoOjpaCxculJeXl/Lz8+Xj43NHNZeWlqpLly539blNuDw0DBw4UPv2VZ2EMm7cOHXu3FkzZ85URESEfHx8tGnTJo0YMUKSdOjQIRUWFio2NtbV5QEA4HD+/HllZGRowYIFSkpKkiS1b99e/fv317Fjx6q09fHxqXKq3Gq1Kjc3VytXrtTIkSNls9lUWlqqIUOGqH379pJU5Ye9sLBQ06dPV+fOnSVJUVFRd1TzypUrHVcnuprLQ0NgYKC6d+9eZV2TJk3UvHlzx/rk5GSlpqYqJCREQUFBevbZZxUbG8uVEwAAt/rmm29UVlamgQMHGrXPzMzUkiVLVFhYqMuXL6u8vNxxZUVISIjGjh2r+Ph4/fjHP1ZcXJxGjhypsLAwSVJqaqrGjx+vDz/8UHFxcXrmmWcc4cLUli1bNG7cOC1evFjdunWr1XvvRJ24I+T8+fM1ZMgQjRgxQg8//LBCQ0Pdcm4GAIDvCwgIMG67fPlyvfDCC0pOTtaGDRuUn5+vcePGqby83NEmKytLubm56tu3r1asWKGOHTtqx44dkqQ5c+bowIEDGjx4sDZv3qyuXbtq9erVxvvfunWrhg4dqvnz5ysxMdH8Q94FjzwaOycnp8prf39/ZWZmKjMz0xPlAAAg6btTBAEBAdq0aZPGjx9/y7ZffPGF+vbtqylTpjjWHT169KZ20dHRio6OVlpammJjY5Wdne0YSe/YsaM6duyoadOmafTo0crKytJTTz112zpzcnI0ZMgQvf7665o4cWItP+Wd80hoAACgLvL399fMmTM1Y8YM+fr6ql+/fjpz5owOHDhw0ymLqKgoffDBB1q/fr2sVqs+/PBD5eXlyWq1SpIKCgq0aNEiPfHEEwoPD9ehQ4d0+PBhJSYm6vLly5o+fbqefvppWa1WnTx5Unl5eY65fbeyZcsWDRkyRM8995xGjBjhuD2Br6+vQkJCnP+lfA+hAQDgVs66Q6OrzJo1S97e3po9e7ZOnTqlsLAwTZ48+aZ2kyZN0t69ezVq1ChZLBaNHj1aU6ZM0dq1ayVJjRs31sGDB7Vs2TKdPXtWYWFhSklJ0aRJk3Tt2jWdPXtWiYmJKikpUYsWLTR8+HCjexAtW7ZMly5dUnp6utLT0x3rH3nkkZtG8p3NYrfb7S7dgxvYbDYFBwertLRUQUFBni4HtzIn2AV9ljq/TwB35cqVKyooKJDVanXaPXpwd2o6JrX5Da0TEyEBAEDdR2gAAKAOSUhIUNOmTatdXnvtNY/WxpwGAADqkPfee0+XL1+udpurJzreDqEBAIA65P777/d0CTXi9AQAwGUqKys9XQKuc8axYKQBAOB0vr6+atSokU6dOqWWLVvK19fX8aAmuJfdbld5ebnOnDmjRo0aydfX9477IjQAAJyuUaNGslqtKioq0qlTpzxdDvTdfSMiIyPVqNGdn2QgNAAAXMLX11eRkZG6du2aKioqPF1Og+bl5SVvb++7Hu0hNAAAXMZiscjHx0c+Pj6eLgVOwERIAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARb08XgIal3ZVsp/d5zOk9AgCqw0gDAAAwQmgAAABGXB4aFi5cqJ49eyooKEhBQUGKjY3V2rVrHduvXLmilJQUNW/eXE2bNtWIESNUUlLi6rIAAEAtuTw0tGnTRvPmzdPu3bu1a9cuPfbYY3ryySd14MABSdK0adP0pz/9SatWrdLWrVt16tQpDR8+3NVlAQCAWrLY7Xa7u3caEhKiN998U08//bRatmyp7OxsPf3005KkgwcPqkuXLsrNzdVDDz1k1J/NZlNwcLBKS0sVFBTkytJxl9r9+s9O7/PYvMFO7xMAGora/Ia6dU5DRUWFli9frosXLyo2Nla7d+/W1atXFRcX52jTuXNnRUZGKjc3t8Z+ysrKZLPZqiwAAMC13BIa9u3bp6ZNm8rPz0+TJ0/W6tWr1bVrVxUXF8vX11fNmjWr0r5169YqLi6usb/09HQFBwc7loiICBd/AgAA4JbQ0KlTJ+Xn5+vLL7/UL3/5SyUlJelvf/vbHfeXlpam0tJSx3LixAknVgsAAKrjlps7+fr6qkOHDpKkmJgY5eXlKSMjQ6NGjVJ5ebnOnTtXZbShpKREoaGhNfbn5+cnPz8/V5cNAAC+xyP3aaisrFRZWZliYmLk4+OjTZs2ObYdOnRIhYWFio2N9URpAACgBi4faUhLS1NCQoIiIyN1/vx5ZWdnKycnR+vXr1dwcLCSk5OVmpqqkJAQBQUF6dlnn1VsbKzxlRMAAMA9XB4aTp8+rcTERBUVFSk4OFg9e/bU+vXr9eMf/1iSNH/+fDVq1EgjRoxQWVmZ4uPj9e///u+uLgsAANSSR+7T4Gzcp6H+4D4NAFC31Nn7NAAAgPqL0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIx4e7oA1F09lvVwQa/zXNAnAMAdGGkAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjHDJJWq0r6DQ6X22c3qPAAB3YaQBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADAiMtDQ3p6un74wx8qMDBQrVq10rBhw3To0KEqba5cuaKUlBQ1b95cTZs21YgRI1RSUuLq0gAAQC24PDRs3bpVKSkp2rFjhzZu3KirV69q0KBBunjxoqPNtGnT9Kc//UmrVq3S1q1bderUKQ0fPtzVpQEAgFrwdvUO1q1bV+X10qVL1apVK+3evVsPP/ywSktL9f777ys7O1uPPfaYJCkrK0tdunTRjh079NBDD7m6RAAAYMDtcxpKS0slSSEhIZKk3bt36+rVq4qLi3O06dy5syIjI5Wbm1ttH2VlZbLZbFUWAADgWm4NDZWVlXr++efVr18/de/eXZJUXFwsX19fNWvWrErb1q1bq7i4uNp+0tPTFRwc7FgiIiJcXToAAA2eW0NDSkqK9u/fr+XLl99VP2lpaSotLXUsJ06ccFKFAACgJi6f03DD1KlT9dlnn2nbtm1q06aNY31oaKjKy8t17ty5KqMNJSUlCg0NrbYvPz8/+fn5ubpkAADwPS4fabDb7Zo6dapWr16tzZs3y2q1VtkeExMjHx8fbdq0ybHu0KFDKiwsVGxsrKvLAwAAhlw+0pCSkqLs7Gx9+umnCgwMdMxTCA4OVkBAgIKDg5WcnKzU1FSFhIQoKChIzz77rGJjY7lyAgCAOsTloWHhwoWSpEcffbTK+qysLI0dO1aSNH/+fDVq1EgjRoxQWVmZ4uPj9e///u+uLg0AANSCy0OD3W6/bRt/f39lZmYqMzPT1eUAAIA7xLMnAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACMufzQ24Go9lvVwep/7kvY5vU8AqO8YaQAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMcMkl6r19BYWeLgEAGgRGGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARrw9XQCco8eyHk7vc5/TewQA1GeMNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGuOQSAL7HJZcvJ3EBM+4NjDQAAAAjhAYAAGDELaFh27ZtGjp0qMLDw2WxWLRmzZoq2+12u2bPnq2wsDAFBAQoLi5Ohw8fdkdpAADAkFtCw8WLF9WrVy9lZmZWu/2NN97Q73//e73zzjv68ssv1aRJE8XHx+vKlSvuKA8AABhwy0TIhIQEJSQkVLvNbrfr7bff1m9+8xs9+eSTkqQPPvhArVu31po1a/TTn/70pveUlZWprKzM8dpms7mmcAAA4ODxqycKCgpUXFysuLg4x7rg4GD16dNHubm51YaG9PR0zZ07151l1nn7Cgo9XQIMMDMfQH3m8YmQxcXFkqTWrVtXWd+6dWvHtn+Wlpam0tJSx3LixAmX1wkAQEPn8ZGGO+Hn5yc/Pz9PlwEAQIPi8ZGG0NBQSVJJSUmV9SUlJY5tAADA8zweGqxWq0JDQ7Vp0ybHOpvNpi+//FKxsbEerAwAAHyfW05PXLhwQUeOHHG8LigoUH5+vkJCQhQZGannn39ev/3tbxUVFSWr1apZs2YpPDxcw4YNc0d5AADAgFtCw65duzRgwADH69TUVElSUlKSli5dqhkzZujixYuaOHGizp07p/79+2vdunXy9/d3R3kAAMCAW0LDo48+KrvdXuN2i8Wil19+WS+//LI7yoGhdleyPV2CEVfUeczpPQJA/efxOQ0AAKB+IDQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMFIvnz1R780J9nQFwC3xNE4A1WGkAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADDCJZdANVxxySGcj+MEuBcjDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARLrkE6rn6ctlhfalzX0Ghp0sA6ixGGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjXHIJVMNVl931sEa6pF/Uba663HRf0j6X9AvUhJEGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAiXXAJu5IpLObmM07naXcl2ep+B+rXT+wQ8gZEGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAiXXAKot1xxCWs7p/foOq54eqYrnpxZX+rE7THSAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABjhkkugGq540qEkHfP/mdP7dMVlh/WFq46Ts7nqGPGEU+fi0tDbY6QBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMMIll7czJ9jTFeAe4opLBF1xGWd9qbOhc8WlnK647LBBc8VvyJxS5/dpiJEGAABghNAAAACM1KnQkJmZqXbt2snf3199+vTRzp07PV0SAAC4rs6EhhUrVig1NVUvvfSS9uzZo169eik+Pl6nT5/2dGkAAEB1KDS89dZbmjBhgsaNG6euXbvqnXfeUePGjbVkyRJPlwYAAFRHrp4oLy/X7t27lZaW5ljXqFEjxcXFKTc396b2ZWVlKisrc7wuLf1uJqnNZnN+cWV25/dZT1SWXfJ0CTBgszj//6OuOPb1pU5XcMVnd5WKyxWeLsGIK/69d8Vnt7niN8TJn/3Gd2m3377WOhEavv32W1VUVKh169ZV1rdu3VoHDx68qX16errmzp170/qIiAiX1dgwjfR0ATDgmouCnX/s60udrlC/Ltz+xtMFGAn+Zf34Vl1S5TzXfPbz588rOPjWfdeJ0FBbaWlpSk1NdbyurKzU3//+dzVv3lwWi8WDlbmPzWZTRESETpw4oaCgIE+XU+fxfZnju6odvq/a4fuqHXd8X3a7XefPn1d4ePht29aJ0NCiRQt5eXmppKSkyvqSkhKFhobe1N7Pz09+fn5V1jVr1syVJdZZQUFB/OHVAt+XOb6r2uH7qh2+r9px9fd1uxGGG+rEREhfX1/FxMRo06ZNjnWVlZXatGmTYmNjPVgZAAC4oU6MNEhSamqqkpKS9OCDD6p37956++23dfHiRY0bN87TpQEAANWh0DBq1CidOXNGs2fPVnFxsR544AGtW7fupsmR+I6fn59eeumlm07ToHp8X+b4rmqH76t2+L5qp659Xxa7yTUWAACgwasTcxoAAEDdR2gAAABGCA0AAMAIoQEAABghNNQz27Zt09ChQxUeHi6LxaI1a9Z4uqQ6Kz09XT/84Q8VGBioVq1aadiwYTp06JCny6qzFi5cqJ49ezpuIhMbG6u1a9d6uqx6Y968ebJYLHr++ec9XUqdNGfOHFkslipL586dPV1Wnfa///u/+td//Vc1b95cAQEB6tGjh3bt2uXRmggN9czFixfVq1cvZWZmerqUOm/r1q1KSUnRjh07tHHjRl29elWDBg3SxYsXPV1andSmTRvNmzdPu3fv1q5du/TYY4/pySef1IEDBzxdWp2Xl5end999Vz179vR0KXVat27dVFRU5Fg+//xzT5dUZ/3jH/9Qv3795OPjo7Vr1+pvf/ubfve73+m+++7zaF115j4NMJOQkKCEhARPl1EvrFu3rsrrpUuXqlWrVtq9e7cefvhhD1VVdw0dOrTK61dffVULFy7Ujh071K1bNw9VVfdduHBBY8aM0eLFi/Xb3/7W0+XUad7e3tU+GgA3e/311xUREaGsrCzHOqvV6sGKvsNIAxqMG49QDwkJ8XAldV9FRYWWL1+uixcvciv320hJSdHgwYMVFxfn6VLqvMOHDys8PFw/+MEPNGbMGBUWFnq6pDrrP//zP/Xggw/qmWeeUatWrRQdHa3Fixd7uixGGtAwVFZW6vnnn1e/fv3UvXt3T5dTZ+3bt0+xsbG6cuWKmjZtqtWrV6tr166eLqvOWr58ufbs2aO8vDxPl1Ln9enTR0uXLlWnTp1UVFSkuXPn6kc/+pH279+vwMBAT5dX5/zP//yPFi5cqNTUVL344ovKy8vTr371K/n6+iopKcljdREa0CCkpKRo//79nEO9jU6dOik/P1+lpaX6j//4DyUlJWnr1q0Eh2qcOHFCzz33nDZu3Ch/f39Pl1Pnff+0as+ePdWnTx+1bdtWK1euVHJysgcrq5sqKyv14IMP6rXXXpMkRUdHa//+/XrnnXc8Gho4PYF73tSpU/XZZ59py5YtatOmjafLqdN8fX3VoUMHxcTEKD09Xb169VJGRoany6qTdu/erdOnT+tf/uVf5O3tLW9vb23dulW///3v5e3trYqKCk+XWKc1a9ZMHTt21JEjRzxdSp0UFhZ2U1jv0qWLx0/pMNKAe5bdbtezzz6r1atXKycnp05MIqpvKisrVVZW5uky6qSBAwdq3759VdaNGzdOnTt31syZM+Xl5eWhyuqHCxcu6OjRo/r5z3/u6VLqpH79+t10ifh///d/q23bth6q6DuEhnrmwoULVZJ5QUGB8vPzFRISosjISA9WVvekpKQoOztbn376qQIDA1VcXCxJCg4OVkBAgIerq3vS0tKUkJCgyMhInT9/XtnZ2crJydH69es9XVqdFBgYeNP8mCZNmqh58+bMm6nGCy+8oKFDh6pt27Y6deqUXnrpJXl5eWn06NGeLq1OmjZtmvr27avXXntNI0eO1M6dO7Vo0SItWrTIs4XZUa9s2bLFLummJSkpydOl1TnVfU+S7FlZWZ4urU76xS9+YW/btq3d19fX3rJlS/vAgQPtGzZs8HRZ9cojjzxif+655zxdRp00atQoe1hYmN3X19d+//3320eNGmU/cuSIp8uq0/70pz/Zu3fvbvfz87N37tzZvmjRIk+XZOfR2AAAwAgTIQEAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADAiLenC3CGyspKnTp1SoGBgbJYLJ4uBwCAesNut+v8+fMKDw9Xo0a3Hku4J0LDqVOnFBER4ekyAACot06cOKE2bdrcss09ERoCAwMlffeBg4KCPFwNAAD1h81mU0REhOO39FbuidBw45REUFAQoQEAgDtgcnqfiZAAAMAIoQEAABghNAAAACP3xJwGAEDdZLfbde3aNVVUVHi6lAbNy8tL3t7ed31bAkIDAMAlysvLVVRUpEuXLnm6FEhq3LixwsLC5Ovre8d9EBoAAE5XWVmpgoICeXl5KTw8XL6+vtx8z0PsdrvKy8t15swZFRQUKCoq6rY3caoJoQEA4HTl5eWqrKxURESEGjdu7OlyGryAgAD5+Pjo+PHjKi8vl7+//x31w0RIAIDL3Ol/0cL5nHEsOJoAAMAIoQEAABghNAAAYODYsWOyWCzKz8/3dCkew0RIAIBbtfv1n926v2PzBrt1f+7w9ddfKyUlRXl5eWrZsqWeffZZzZgxw+X7ZaQBAIB6xGazadCgQWrbtq12796tN998U3PmzNGiRYtcvm9GGgC4RY9lPZze576kfU7vE6isrNS//du/adGiRTpx4oRat26tSZMmacyYMVXaVVRUaOLEidq8ebOKi4sVGRmpKVOm6LnnnnO0ycnJ0YwZM3TgwAH5+PioW7duys7OVtu2bfXVV1/p+eef165du2SxWBQVFaV3331XDz744C3r++ijj1ReXq4lS5bI19dX3bp1U35+vt566y1NnDjRJd/JDYQGAAC+Jy0tTYsXL9b8+fPVv39/FRUV6eDBgze1q6ysVJs2bbRq1So1b95c27dv18SJExUWFqaRI0fq2rVrGjZsmCZMmKCPP/5Y5eXl2rlzp+MmV2PGjFF0dLQWLlwoLy8v5efny8fH57b15ebm6uGHH65yZ8f4+Hi9/vrr+sc//qH77rvPeV/GPyE0AABw3fnz55WRkaEFCxYoKSlJktS+fXv1799fx44dq9LWx8dHc+fOdby2Wq3Kzc3VypUrNXLkSNlsNpWWlmrIkCFq3769JKlLly6O9oWFhZo+fbo6d+4sSYqKijKqsbi4WFartcq61q1bO7a5MjQwpwEAgOu++eYblZWVaeDAgUbtMzMzFRMTo5YtW6pp06ZatGiRCgsLJUkhISEaO3as4uPjNXToUGVkZKioqMjx3tTUVI0fP15xcXGaN2+ejh496pLP5EyEBgAArgsICDBuu3z5cr3wwgtKTk7Whg0blJ+fr3Hjxqm8vNzRJisrS7m5uerbt69WrFihjh07aseOHZKkOXPm6MCBAxo8eLA2b96srl27avXq1bfdb2hoqEpKSqqsu/E6NDTUuP47QWgAAOC6qKgoBQQEaNOmTbdt+8UXX6hv376aMmWKoqOj1aFDh2pHC6Kjo5WWlqbt27ere/fuys7Odmzr2LGjpk2bpg0bNmj48OHKysq67X5jY2O1bds2Xb161bFu48aN6tSpk0tPTUiEBgAAHPz9/TVz5kzNmDFDH3zwgY4ePaodO3bo/fffv6ltVFSUdu3apfXr1+u///u/NWvWLOXl5Tm2FxQUKC0tTbm5uTp+/Lg2bNigw4cPq0uXLrp8+bKmTp2qnJwcHT9+XF988YXy8vKqzHmoyc9+9jP5+voqOTlZBw4c0IoVK5SRkaHU1FSnfhfVYSIkAMCt6vrNlmbNmiVvb2/Nnj1bp06dUlhYmCZPnnxTu0mTJmnv3r0aNWqULBaLRo8erSlTpmjt2rWSpMaNG+vgwYNatmyZzp49q7CwMKWkpGjSpEm6du2azp49q8TERJWUlKhFixYaPnx4lYmVNQkODtaGDRuUkpKimJgYtWjRQrNnz3b55ZaSZLHb7XaX78XFbDabgoODVVpaqqCgIE+XA6Aa3KehYbly5YoKCgpktVrv+DHMcK6ajkltfkM5PQEAAIwQGgAAqEMSEhLUtGnTapfXXnvNo7UxpwEAgDrkvffe0+XLl6vdFhIS4uZqqiI0AABQh9x///2eLqFGnJ4AAABGCA0AAMAIoQEAABhxeWhYuHChevbsqaCgIAUFBSk2NtZx4wvpu+tGU1JS1Lx5czVt2lQjRoy46Z7aAADA81weGtq0aaN58+Zp9+7d2rVrlx577DE9+eSTOnDggCRp2rRp+tOf/qRVq1Zp69atOnXqlIYPH+7qsgAAQC25PDQMHTpUP/nJTxQVFaWOHTvq1VdfVdOmTbVjxw6Vlpbq/fff11tvvaXHHntMMTExysrK0vbt2x1PAQMAoC44duyYLBaL8vPzPV2Kx7j1ksuKigqtWrVKFy9eVGxsrHbv3q2rV68qLi7O0aZz586KjIxUbm6uHnrooWr7KSsrU1lZmeO1zWZzee0AACeZE+zm/ZW6d38uduXKFU2ePFm7d+/WN998oyFDhmjNmjVu2bdbQsO+ffsUGxurK1euqGnTplq9erW6du2q/Px8+fr6qlmzZlXat27dWsXFxTX2l56ebvRQDwB3xhXPiQDgHBUVFQoICNCvfvUr/fGPf3Trvt1y9USnTp2Un5+vL7/8Ur/85S+VlJSkv/3tb3fcX1pamkpLSx3LiRMnnFgtAKAhq6ys1BtvvKEOHTrIz89PkZGRevXVV29qV1FRoeTkZFmtVgUEBKhTp07KyMio0iYnJ0e9e/dWkyZN1KxZM/Xr10/Hjx+XJH311VcaMGCAAgMDFRQUpJiYGO3ateu29TVp0kQLFy7UhAkTFBoa6pwPbcgtIw2+vr7q0KGDJCkmJkZ5eXnKyMjQqFGjVF5ernPnzlUZbSgpKbnlF+Hn5yc/Pz9Xlw0AaIDS0tK0ePFizZ8/X/3791dRUZEOHjx4U7vKykq1adNGq1atUvPmzbV9+3ZNnDhRYWFhGjlypK5du6Zhw4ZpwoQJ+vjjj1VeXq6dO3fKYrFIksaMGaPo6GgtXLhQXl5eys/Pl4+Pj7s/bq145DbSlZWVKisrU0xMjHx8fLRp0yaNGDFCknTo0CEVFhYqNjbWE6UBABqw8+fPKyMjQwsWLFBSUpIkqX379urfv7+OHTtWpa2Pj0+VU+VWq1W5ublauXKlRo4cKZvNptLSUg0ZMkTt27eXJHXp0sXRvrCwUNOnT1fnzp0lSVFRUS7+dHfP5aEhLS1NCQkJioyM1Pnz55Wdna2cnBytX79ewcHBSk5OVmpqqkJCQhQUFKRnn31WsbGxNU6CBADAVb755huVlZVp4MCBRu0zMzO1ZMkSFRYW6vLlyyovL9cDDzwg6buHS40dO1bx8fH68Y9/rLi4OI0cOVJhYWGSpNTUVI0fP14ffvih4uLi9MwzzzjCRV3l8jkNp0+fVmJiojp16qSBAwcqLy9P69ev149//GNJ0vz58zVkyBCNGDFCDz/8sEJDQ/XJJ5+4uiwAAG4SEBBg3Hb58uV64YUXlJycrA0bNig/P1/jxo1TeXm5o01WVpZyc3PVt29frVixQh07dnTcUmDOnDk6cOCABg8erM2bN6tr165avXq10z+TM7l8pOH999+/5XZ/f39lZmYqMzPT1aUAAHBLUVFRCggI0KZNmzR+/Phbtv3iiy/Ut29fTZkyxbHu6NGjN7WLjo5WdHS00tLSFBsbq+zsbMdoeseOHdWxY0dNmzZNo0ePVlZWlp566innfign4tHYAABc5+/vr5kzZ2rGjBny9fVVv379dObMGR04cOCmUxZRUVH64IMPtH79elmtVn344YfKy8uT1WqVJBUUFGjRokV64oknFB4erkOHDunw4cNKTEzU5cuXNX36dD399NOyWq06efKk8vLyHPP7budvf/ubysvL9fe//13nz5933HDqxqkRVyE0AADcq47fbGnWrFny9vbW7NmzderUKYWFhWny5Mk3tZs0aZL27t2rUaNGyWKxaPTo0ZoyZYrj+UqNGzfWwYMHtWzZMp09e1ZhYWFKSUnRpEmTdO3aNZ09e1aJiYkqKSlRixYtNHz4cON7EP3kJz9xXLopfTeaIUl2u90J30DNLHZX78ENbDabgoODVVpaqqCgIE+XA9R79eXmTvuS9nm6BNTgypUrKigokNVqlb+/v6fLgWo+JrX5DeXR2AAAwAihAQCAOiQhIUFNmzatdnnttdc8WhtzGgAAqEPee+89Xb58udptISEhbq6mKkIDAAB1yP333+/pEmrE6QkAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAAAYOHbsmCwWi+M5Dw0Rl1wCANzK3bcpv9duN56Tk6P58+dr586dstlsioqK0vTp0zVmzBiX75uRBgAA6pHt27erZ8+e+uMf/6ivv/5a48aNU2Jioj777DOX75vQAADA91RWVuqNN95Qhw4d5Ofnp8jISL366qs3tauoqFBycrKsVqsCAgLUqVMnZWRkVGmTk5Oj3r17q0mTJmrWrJn69evneDrlV199pQEDBigwMFBBQUGKiYnRrl27blvfiy++qFdeeUV9+/ZV+/bt9dxzz+nxxx/XJ5984pwv4BY4PQEAwPekpaVp8eLFmj9/vvr376+ioiIdPHjwpnaVlZVq06aNVq1apebNm2v79u2aOHGiwsLCNHLkSF27dk3Dhg3ThAkT9PHHH6u8vFw7d+6UxWKRJI0ZM0bR0dFauHChvLy8lJ+fLx8fnzuqubS0VF26dLmrz22C0AAAwHXnz59XRkaGFixYoKSkJElS+/bt1b9/fx07dqxKWx8fH82dO9fx2mq1Kjc3VytXrtTIkSNls9lUWlqqIUOGqH379pJU5Ye9sLBQ06dPV+fOnSVJUVFRd1TzypUrlZeXp3ffffeO3l8bnJ4AAOC6b775RmVlZRo4cKBR+8zMTMXExKhly5Zq2rSpFi1apMLCQknfPVxq7Nixio+P19ChQ5WRkaGioiLHe1NTUzV+/HjFxcVp3rx5Onr0aK3r3bJli8aNG6fFixerW7dutX5/bREaAAC4LiAgwLjt8uXL9cILLyg5OVkbNmxQfn6+xo0bp/LyckebrKws5ebmqm/fvlqxYoU6duyoHTt2SJLmzJmjAwcOaPDgwdq8ebO6du2q1atXG+9/69atGjp0qObPn6/ExETzD3kXCA0AAFwXFRWlgIAAbdq06bZtv/jiC/Xt21dTpkxRdHS0OnToUO1oQXR0tNLS0rR9+3Z1795d2dnZjm0dO3bUtGnTtGHDBg0fPlxZWVlGdebk5Gjw4MF6/fXXNXHiRPMPeJeY0wAAwHX+/v6aOXOmZsyYIV9fX/Xr109nzpzRgQMHbjplERUVpQ8++EDr16+X1WrVhx9+qLy8PFmtVklSQUGBFi1apCeeeELh4eE6dOiQDh8+rMTERF2+fFnTp0/X008/LavVqpMnTyovL08jRoy4bY1btmzRkCFD9Nxzz2nEiBEqLi6WJPn6+iokJMT5X8r3EBoAAG5V12+2NGvWLHl7e2v27Nk6deqUwsLCNHny5JvaTZo0SXv37tWoUaNksVg0evRoTZkyRWvXrpUkNW7cWAcPHtSyZct09uxZhYWFKSUlRZMmTdK1a9d09uxZJSYmqqSkRC1atNDw4cOrTKysybJly3Tp0iWlp6crPT3dsf6RRx5RTk6O076H6ljsdrvdpXtwA5vNpuDgYJWWliooKMjT5QD1nrvv2Hen6vqPT0N25coVFRQUyGq1yt/f39PlQDUfk9r8hjKnAQAAGCE0AABQhyQkJKhp06bVLq+99ppHa2NOAwAAdch7772ny5cvV7vN1RMdb4fQAABAHXL//fd7uoQacXoCAOAy98Bc+3uGM44FoQEA4HQ3Hrx06dIlD1eCG24cizt9KJbE6QkAgAt4eXmpWbNmOn36tKTv7llw4+mOcC+73a5Lly7p9OnTatasmby8vO64L0IDAMAlQkNDJckRHOBZzZo1cxyTO0VoAFBvueImVNwwynksFovCwsLUqlUrXb161dPlNGg+Pj53NcJwA6EBAOBSXl5eTvnBgucxERIAABghNAAAACOEBgAAYITQAAAAjBAaAACAEZeHhvT0dP3whz9UYGCgWrVqpWHDhunQoUNV2jz66KOyWCxVlsmTJ7u6NAAAUAsuDw1bt25VSkqKduzYoY0bN+rq1asaNGiQLl68WKXdhAkTVFRU5FjeeOMNV5cGAABqweX3aVi3bl2V10uXLlWrVq20e/duPfzww471jRs3Nr5TVVlZmcrKyhyvbTabc4oFAAA1cvuchtLSUkk3PxP8o48+UosWLdS9e3elpaXd8iEn6enpCg4OdiwREREurRkAAEgWuxufW1pZWaknnnhC586d0+eff+5Yv2jRIrVt21bh4eH6+uuvNXPmTPXu3VuffPJJtf1UN9IQERGh0tJSBQUFufxzAPc6V9yeub7gNtJoaGw2m4KDg41+Q916G+mUlBTt37+/SmCQpIkTJzr+d48ePRQWFqaBAwfq6NGjat++/U39+Pn5yc/Pz+X1AgCA/+O20xNTp07VZ599pi1btqhNmza3bNunTx9J0pEjR9xRGgAAMODykQa73a5nn31Wq1evVk5OjqxW623fk5+fL0kKCwtzcXUAAMCUy0NDSkqKsrOz9emnnyowMFDFxcWSpODgYAUEBOjo0aPKzs7WT37yEzVv3lxff/21pk2bpocfflg9e/Z0dXkAAMCQy0PDwoULJX13A6fvy8rK0tixY+Xr66u//vWvevvtt3Xx4kVFRERoxIgR+s1vfuPq0gAAQC245fTErURERGjr1q2uLgMAANwlnj0BAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEZc/sAqAGjoeizr4ZJ+9yXtc0m/QE0YaQAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEa4IySAm+wrKHR6nz2skU7vE4B7MdIAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAIy4PDenp6frhD3+owMBAtWrVSsOGDdOhQ4eqtLly5YpSUlLUvHlzNW3aVCNGjFBJSYmrSwMAALXg8tCwdetWpaSkaMeOHdq4caOuXr2qQYMG6eLFi44206ZN05/+9CetWrVKW7du1alTpzR8+HBXlwYAAGrB29U7WLduXZXXS5cuVatWrbR79249/PDDKi0t1fvvv6/s7Gw99thjkqSsrCx16dJFO3bs0EMPPeTqEgEAgAG3z2koLS2VJIWEhEiSdu/eratXryouLs7RpnPnzoqMjFRubm61fZSVlclms1VZAACAa7k1NFRWVur5559Xv3791L17d0lScXGxfH191axZsyptW7dureLi4mr7SU9PV3BwsGOJiIhwdekAADR4bg0NKSkp2r9/v5YvX35X/aSlpam0tNSxnDhxwkkVAgCAmrh8TsMNU6dO1WeffaZt27apTZs2jvWhoaEqLy/XuXPnqow2lJSUKDQ0tNq+/Pz85Ofn5+qSAQDA97h8pMFut2vq1KlavXq1Nm/eLKvVWmV7TEyMfHx8tGnTJse6Q4cOqbCwULGxsa4uDwAAGHL5SENKSoqys7P16aefKjAw0DFPITg4WAEBAQoODlZycrJSU1MVEhKioKAgPfvss4qNjeXKCQAA6hCXh4aFCxdKkh599NEq67OysjR27FhJ0vz589WoUSONGDFCZWVlio+P17//+7+7ujQAAFALLg8Ndrv9tm38/f2VmZmpzMxMV5cDAADuEM+eAAAARggNAADACKEBAAAYcdt9GgDA2fYVFHq6BKBBYaQBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAY4Y6QANyCuzcC9R8jDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAh3hATquR7Leji9z31O7xHAvYCRBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABhxS2jYtm2bhg4dqvDwcFksFq1Zs6bK9rFjx8pisVRZHn/8cXeUBgAADLklNFy8eFG9evVSZmZmjW0ef/xxFRUVOZaPP/7YHaUBAABD3u7YSUJCghISEm7Zxs/PT6Ghoe4oBwAA3IE6M6chJydHrVq1UqdOnfTLX/5SZ8+erbFtWVmZbDZblQUAALiWW0Yabufxxx/X8OHDZbVadfToUb344otKSEhQbm6uvLy8bmqfnp6uuXPneqBSAPe6Hst6OL3PfQWFTu8T8IQ6ERp++tOfOv53jx491LNnT7Vv3145OTkaOHDgTe3T0tKUmprqeG2z2RQREeGWWgEAaKjqzOmJ7/vBD36gFi1a6MiRI9Vu9/PzU1BQUJUFAAC4Vp0MDSdPntTZs2cVFhbm6VIAAMB1bjk9ceHChSqjBgUFBcrPz1dISIhCQkI0d+5cjRgxQqGhoTp69KhmzJihDh06KD4+3h3lAQAAA24JDbt27dKAAQMcr2/MR0hKStLChQv19ddfa9myZTp37pzCw8M1aNAgvfLKK/Lz83NHeQAAwIBbQsOjjz4qu91e4/b169e7owwAAHAX6uScBgAAUPcQGgAAgBFCAwAAMFInbu4E4M5xt0EA7sJIAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADAiLenCwCAO9XuSrbT+wzUr53epyvqlKRjLukVqBkjDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAh3hASA79lXUOj0Pts5vUfAMxhpAAAARggNAADACKEBAAAYITQAAAAjhAYAAGDELaFh27ZtGjp0qMLDw2WxWLRmzZoq2+12u2bPnq2wsDAFBAQoLi5Ohw8fdkdpAADAkFtCw8WLF9WrVy9lZmZWu/2NN97Q73//e73zzjv68ssv1aRJE8XHx+vKlSvuKA8AABhwy30aEhISlJCQUO02u92ut99+W7/5zW/05JNPSpI++OADtW7dWmvWrNFPf/pTd5QIAABuw+NzGgoKClRcXKy4uDjHuuDgYPXp00e5ubnVvqesrEw2m63KAgAAXMvjoaG4uFiS1Lp16yrrW7du7dj2z9LT0xUcHOxYIiIiXF4nAAANncdDw51IS0tTaWmpYzlx4oSnSwIA4J7n8dAQGhoqSSopKamyvqSkxLHtn/n5+SkoKKjKAgAAXMvjocFqtSo0NFSbNm1yrLPZbPryyy8VGxvrwcoAAMD3ueXqiQsXLujIkSOO1wUFBcrPz1dISIgiIyP1/PPP67e//a2ioqJktVo1a9YshYeHa9iwYe4oDwAAGHBLaNi1a5cGDBjgeJ2amipJSkpK0tKlSzVjxgxdvHhREydO1Llz59S/f3+tW7dO/v7+7igPAAAYcEtoePTRR2W322vcbrFY9PLLL+vll192RzkAAOAOeHxOAwAAqB8IDQAAwAihAQAAGHHLnAag3pkT7KJ+S13Tbz3Q7kq2p0sAcJcYaQAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEa4IyQA1FM9lvVwep/7kvY5vU/cOxhpAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARrgjJFDPtbuS7ekSADQQjDQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAj3BESAL6HO2wCNWOkAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAICROhEa5syZI4vFUmXp3Lmzp8sCAADfU2cuuezWrZv++te/Ol57e9eZ0gAAgOpQaPD29lZoaKinywAAADWoE6cnJOnw4cMKDw/XD37wA40ZM0aFhYU1ti0rK5PNZquyAAAA16oTIw19+vTR0qVL1alTJxUVFWnu3Ln60Y9+pP379yswMPCm9unp6Zo7d64HKgXu0pxgF3TKHQwbqn0FNf/HFeAKFrvdbvd0Ef/s3Llzatu2rd566y0lJyfftL2srExlZWWO1zabTRERESotLVVQUJA7S8W9yiU/7q7BbY8brmP+P3N+p3NKnd8n6jSbzabg4GCj39A6MdLwz5o1a6aOHTvqyJEj1W738/OTn5+fm6sCAKBhqzNzGr7vwoULOnr0qMLCwjxdCgAAuK5OhIYXXnhBW7du1bFjx7R9+3Y99dRT8vLy0ujRoz1dGgAAuK5OnJ44efKkRo8erbNnz6ply5bq37+/duzYoZYtW3q6NAAAcF2dCA3Lly/3dAkAAOA26sTpCQAAUPcRGgAAgBFCAwAAMFIn5jSgAXHFTZO4GQ0AuAUjDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAh3hAQAOPRY1sPpfe5L2uf0PuEZjDQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAj3BES9d+cYE9XAHhEuyvZzu/0G+d3iXsHIw0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAId4S8HVfcbXBOqfP7RL3gkjv4AXVdPfl3tN2v/+z0Po/5/8zpfXryN4SRBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABG6lRoyMzMVLt27eTv768+ffpo586dni4JAABcV2dCw4oVK5SamqqXXnpJe/bsUa9evRQfH6/Tp097ujQAAKA6FBreeustTZgwQePGjVPXrl31zjvvqHHjxlqyZImnSwMAAKojN3cqLy/X7t27lZaW5ljXqFEjxcXFKTc396b2ZWVlKisrc7wuLf3uRhc2m835xZXZnd+nK+qsL1zxfdYjlWWXPF0C4HY2S/34d9QVf5/14bPf+O20229fa50IDd9++60qKirUunXrKutbt26tgwcP3tQ+PT1dc+fOvWl9RESEy2p0qnkuuDsa6omRni4AcDuX/ItXT/4drU+f/fz58woOvnXfdSI01FZaWppSU1MdrysrK/X3v/9dzZs3l8Vi8WBldYvNZlNERIROnDihoKAgT5eD6zgudRfHpu7i2LiO3W7X+fPnFR4eftu2dSI0tGjRQl5eXiopKamyvqSkRKGhoTe19/Pzk5+fX5V1zZo1c2WJ9VpQUBB/ZHUQx6Xu4tjUXRwb17jdCMMNdWIipK+vr2JiYrRp0ybHusrKSm3atEmxsbEerAwAANxQJ0YaJCk1NVVJSUl68MEH1bt3b7399tu6ePGixo0b5+nSAACA6lBoGDVqlM6cOaPZs2eruLhYDzzwgNatW3fT5EiY8/Pz00svvXTTqRx4Fsel7uLY1F0cm7rBYje5xgIAADR4dWJOAwAAqPsIDQAAwAihAQAAGCE0AAAAI4SGemrbtm0aOnSowsPDZbFYtGbNmlu2z8nJkcViuWkpLi52T8ENRHp6un74wx8qMDBQrVq10rBhw3To0KHbvm/VqlXq3Lmz/P391aNHD/3lL39xQ7UNy50cm6VLl970N+Pv7++mihuOhQsXqmfPno4bN8XGxmrt2rW3fA9/M55BaKinLl68qF69eikzM7NW7zt06JCKioocS6tWrVxUYcO0detWpaSkaMeOHdq4caOuXr2qQYMG6eLFizW+Z/v27Ro9erSSk5O1d+9eDRs2TMOGDdP+/fvdWPm9706OjfTdHQi//zdz/PhxN1XccLRp00bz5s3T7t27tWvXLj322GN68skndeDAgWrb8zfjQXbUe5Lsq1evvmWbLVu22CXZ//GPf7ilJnzn9OnTdkn2rVu31thm5MiR9sGDB1dZ16dPH/ukSZNcXV6DZnJssrKy7MHBwe4rCg733Xef/b333qt2G38znsNIQwPzwAMPKCwsTD/+8Y/1xRdfeLqce96Nx7aHhITU2CY3N1dxcXFV1sXHx1f7WHg4j8mxkaQLFy6obdu2ioiIuOV//cI5KioqtHz5cl28eLHGxwjwN+M5hIYGIiwsTO+8847++Mc/6o9//KMiIiL06KOPas+ePZ4u7Z5VWVmp559/Xv369VP37t1rbFdcXFztY+GZb+I6psemU6dOWrJkiT799FP94Q9/UGVlpfr27auTJ0+6sdqGYd++fWratKn8/Pw0efJkrV69Wl27dq22LX8znlNnbiMN1+rUqZM6derkeN23b18dPXpU8+fP14cffujByu5dKSkp2r9/vz7//HNPl4J/YnpsYmNjq/zXbt++fdWlSxe9++67euWVV1xdZoPSqVMn5efnq7S0VP/xH/+hpKQkbd26tcbgAM9gpKEB6927t44cOeLpMu5JU6dO1WeffaYtW7aoTZs2t2wbGhpq/Fh43L3aHJt/5uPjo+joaP5uXMDX11cdOnRQTEyM0tPT1atXL2VkZFTblr8ZzyE0NGD5+fkKCwvzdBn3FLvdrqlTp2r16tXavHmzrFbrbd8TGxtb5bHwkrRx40YeC+9kd3Js/llFRYX27dvH340bVFZWqqysrNpt/M14Dqcn6qkLFy5U+a+dgoIC5efnKyQkRJGRkUpLS9P//u//6oMPPpAkvf3227JarerWrZuuXLmi9957T5s3b9aGDRs89RHuSSkpKcrOztann36qwMBAxznW4OBgBQQESJISExN1//33Kz09XZL03HPP6ZFHHtHvfvc7DR48WMuXL9euXbu0aNEij32Oe9GdHJuXX35ZDz30kDp06KBz587pzTff1PHjxzV+/HiPfY57UVpamhISEhQZGanz588rOztbOTk5Wr9+vST+ZuoUT1++gTtz4xLKf16SkpLsdrvdnpSUZH/kkUcc7V9//XV7+/bt7f7+/vaQkBD7o48+at+8ebNnir+HVXdMJNmzsrIcbR555BHHcbph5cqV9o4dO9p9fX3t3bp1s//5z392b+ENwJ0cm+eff94eGRlp9/X1tbdu3dr+k5/8xL5nzx73F3+P+8UvfmFv27at3dfX196yZUv7wIED7Rs2bHBs52+m7uDR2AAAwAhzGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAA/3979x+UZZ3vf/xFCDeo/Ah/ASl2B4iWaA6nXNF1dWVCMst0x9TOiC6pJbYpJ3VxVpN2C3Nncmny6LF20faEmudozakx2aOhk2KpRxItPcpA6Ai6h/3KbSqgcH3/SO+JgPyU9y/g+Zi5Zryv63N/rvfN1dX9ms913dfHCKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADASBd37yA3N1fbt2/XyZMnFRwcrOTkZL322mtKSEhwthkzZoz27t3b7H3z5s3T+vXrjfbR1NSk8+fPKyQkRH5+fi6tHwCAjsyyLF2+fFnR0dG6664fHkvwsyzLcmcx48eP17Rp0/TQQw/pxo0bWrZsmY4fP64vv/xS3bp1k/RtaBgwYIBefvll5/u6du2q0NBQo32cO3dO/fr1c0v9AAB0BmfPnlXfvn1/sI3bRxo+/vjjZq83btyo3r1768iRIxo9erRzfdeuXRUZGfmT9hESEiLp2w9sGjQAAIDkcDjUr18/53fpD3F7aPi+2tpaSVJERESz9e+++67+/d//XZGRkZo4caKWL1+url27ttpHfX296uvrna8vX74sSQoNDSU0AADwE5hc3vdoaGhqatLChQs1cuRIDR482Ll+xowZ6t+/v6Kjo3Xs2DEtXbpUp06d0vbt21vtJzc3Vzk5OZ4qGwAAyAP3NHzXc889p507d+rTTz/9wesme/bs0bhx43TmzBnFxsa22P79kYZbQyu1tbWMNAAA8CM4HA6FhYUZfYd6bKRhwYIF+vDDD7Vv377b3mgxfPhwSWozNNhsNtlsNrfUCQAAWuf20GBZlp5//nnt2LFDRUVFstvtt31PSUmJJCkqKsrN1QEA3K2xsVHXr1/3dhmdWkBAgPz9/e+4H7eHhszMTBUUFOiDDz5QSEiIqqurJUlhYWEKDg5WWVmZCgoK9Oijj6pHjx46duyYFi1apNGjR2vIkCHuLg8A4CaWZam6ulqXLl3ydimQFB4ersjIyDt6npHb72loq7j8/HzNmjVLZ8+e1T//8z/r+PHjunLlivr166cnn3xSv/vd74zvT/gx12MAAJ5RVVWlS5cuqXfv3uratSsP3/MSy7J09epVXbx4UeHh4S1G8X3qnobbZZJ+/fq1eBokAKB9a2xsdAaGHj16eLucTi84OFiSdPHiRfXu3fsnX6pg7gkAgMvduoehreftwPNuHYs7ub+E0AAAcBsuSfgOVxwLQgMAADBCaAAAAEY8PvcEAKBzu/e3H3l0fxWrJrimn4oK2e12HT16VA8++KBL+mxvGGkAAKCdOXbsmH7+858rKChI/fr10+rVqz2yX0YaALSQuCnR5X2Wppe6vE+gM3I4HHrkkUeUkpKi9evXq7S0VL/+9a8VHh6uuXPnunXfjDQAAPAdTU1NWr16teLi4mSz2RQTE6NXXnmlRbvGxkZlZGTIbrcrODhYCQkJysvLa9amqKhIDz/8sLp166bw8HCNHDlSX3/9tSTpiy++0NixYxUSEqLQ0FAlJSXp8OHDt63v3XffVUNDg/7yl7/ogQce0LRp0/Sb3/xGr7/+umv+AD+AkQYAAL4jOztbb731ltasWaNRo0apqqpKJ0+ebNGuqalJffv21bZt29SjRw8dOHBAc+fOVVRUlKZOnaobN25o0qRJmjNnjjZv3qyGhgZ9/vnnzp8+Pv300xo2bJjWrVsnf39/lZSUKCAg4Lb1FRcXa/To0QoMDHSuS01N1Wuvvab/9//+n+6++27X/TG+h9AAAMBNly9fVl5ent58802lp6dLkmJjYzVq1ChVVFQ0axsQEKCcnBzna7vdruLiYr333nuaOnWqHA6Hamtr9dhjjzlnbB40aJCzfWVlpRYvXqyBAwdKkuLj441qrK6ubjH5Y58+fZzb3BkauDwBAMBNX331lerr6zVu3Dij9mvXrlVSUpJ69eql7t27a8OGDaqsrJQkRUREaNasWUpNTdXEiROVl5enqqoq53uzsrL0zDPPKCUlRatWrVJZWZlbPpMrERoAALjp1hwNJrZs2aIXX3xRGRkZKiwsVElJiWbPnq2GhgZnm/z8fBUXFys5OVlbt27VgAEDdPDgQUnSypUrdeLECU2YMEF79uzR/fffrx07dtx2v5GRkbpw4UKzdbdeR0ZGGtf/UxAaAAC4KT4+XsHBwdq9e/dt2+7fv1/JycmaP3++hg0bpri4uFZHC4YNG6bs7GwdOHBAgwcPVkFBgXPbgAEDtGjRIhUWFmry5MnKz8+/7X5HjBihffv2NZtD4m9/+5sSEhLcemlCIjQAAOAUFBSkpUuXasmSJXrnnXdUVlamgwcP6s9//nOLtvHx8Tp8+LB27dql//3f/9Xy5ct16NAh5/by8nJlZ2eruLhYX3/9tQoLC3X69GkNGjRI165d04IFC1RUVKSvv/5a+/fv16FDh5rd89CWGTNmKDAwUBkZGTpx4oS2bt2qvLw8ZWVlufRv0RpuhAQAeJSrntDoLsuXL1eXLl20YsUKnT9/XlFRUXr22WdbtJs3b56OHj2qp556Sn5+fpo+fbrmz5+vnTt3Svp2VsmTJ09q06ZNqqmpUVRUlDIzMzVv3jzduHFDNTU1mjlzpi5cuKCePXtq8uTJzW6sbEtYWJgKCwuVmZmppKQk9ezZUytWrHD7Mxokyc+yLMvte3Ezh8OhsLAw1dbWKjQ01NvlAO0eD3fCnaqrq1N5ebnsdruCgoK8XQ7U9jH5Md+hXJ4AAABGCA0AAPiQtLQ0de/evdXl1Vdf9Wpt3NMAAIAPefvtt3Xt2rVWt0VERHi4muYIDQAA+JB77rnH2yW0idAAtHPuuGkRAFrDPQ0AAMAIoQEAABghNAAAACOEBgAAYIQbIQEAnrUyzMP7q3VJNxUVFbLb7Tp69KgefPBBl/TZ3jDSAABAO1JXV6dZs2YpMTFRXbp00aRJkzy2b0IDAADtSGNjo4KDg/Wb3/xGKSkpHt03oQEAgO9oamrS6tWrFRcXJ5vNppiYGL3yyist2jU2NiojI0N2u13BwcFKSEhQXl5eszZFRUV6+OGH1a1bN4WHh2vkyJH6+uuvJUlffPGFxo4dq5CQEIWGhiopKUmHDx++bX3dunXTunXrNGfOHEVGRrrmQxvingYAAL4jOztbb731ltasWaNRo0apqqpKJ0+ebNGuqalJffv21bZt29SjRw8dOHBAc+fOVVRUlKZOnaobN25o0qRJmjNnjjZv3qyGhgZ9/vnn8vPzkyQ9/fTTGjZsmNatWyd/f3+VlJQoICDA0x/3RyE0AABw0+XLl5WXl6c333xT6enpkqTY2FiNGjVKFRUVzdoGBAQoJyfH+dput6u4uFjvvfeepk6dKofDodraWj322GOKjY2VJA0aNMjZvrKyUosXL9bAgQMlSfHx8W7+dHeOyxMAANz01Vdfqb6+XuPGjTNqv3btWiUlJalXr17q3r27NmzYoMrKSknfTi41a9YspaamauLEicrLy1NVVZXzvVlZWXrmmWeUkpKiVatWqayszC2fyZUIDQAA3BQcHGzcdsuWLXrxxReVkZGhwsJClZSUaPbs2WpoaHC2yc/PV3FxsZKTk7V161YNGDBABw8elCStXLlSJ06c0IQJE7Rnzx7df//92rFjh8s/kysRGgAAuCk+Pl7BwcHavXv3bdvu379fycnJmj9/voYNG6a4uLhWRwuGDRum7OxsHThwQIMHD1ZBQYFz24ABA7Ro0SIVFhZq8uTJys/Pd+nncTXuaQAA4KagoCAtXbpUS5YsUWBgoEaOHKm///3vOnHiRItLFvHx8XrnnXe0a9cu2e12/fWvf9WhQ4dkt9slSeXl5dqwYYMef/xxRUdH69SpUzp9+rRmzpypa9euafHixfrVr34lu92uc+fO6dChQ5oyZYpRnV9++aUaGhr0j3/8Q5cvX1ZJSYkkuf2hU4QGAIBnuegJje6yfPlydenSRStWrND58+cVFRWlZ599tkW7efPm6ejRo3rqqafk5+en6dOna/78+dq5c6ckqWvXrjp58qQ2bdqkmpoaRUVFKTMzU/PmzdONGzdUU1OjmTNn6sKFC+rZs6cmT57c7MbKH/Loo486f7opfTuaIUmWZbngL9A2P8vde/AAh8OhsLAw1dbWKjQ01NvlAB6VuCnR2yUYKU0v9XYJ8KC6ujqVl5fLbrcrKCjI2+VAbR+TH/Mdyj0NAADACKEBAAAfkpaWpu7du7e6vPrqq16tjXsaAADwIW+//bauXbvW6raIiAgPV9McoQEAAB9yzz33eLuENnF5AgAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARfj0BeFB7eXoj4E6ePg9c9TTSiooK2e12HT161O1zPPgqRhoAAGhHioqK9MQTTygqKkrdunXTgw8+qHfffdcj+yY0AADQjhw4cEBDhgzRf/7nf+rYsWOaPXu2Zs6cqQ8//NDt+yY0AADwHU1NTVq9erXi4uJks9kUExOjV155pUW7xsZGZWRkyG63Kzg4WAkJCcrLy2vWpqioSA8//LC6deum8PBwjRw50jk75RdffKGxY8cqJCREoaGhSkpK0uHDh29b37Jly/T73/9eycnJio2N1QsvvKDx48dr+/btrvkD/ADuaQAA4Duys7P11ltvac2aNRo1apSqqqp08uTJFu2amprUt29fbdu2TT169NCBAwc0d+5cRUVFaerUqbpx44YmTZqkOXPmaPPmzWpoaNDnn38uPz8/SdLTTz+tYcOGad26dfL391dJSYkCAgJ+Us21tbUaNGjQHX1uE4QGAABuunz5svLy8vTmm28qPT1dkhQbG6tRo0apoqKiWduAgADl5OQ4X9vtdhUXF+u9997T1KlT5XA4VFtbq8cee0yxsbGS1OyLvbKyUosXL9bAgQMlSfHx8T+p5vfee0+HDh3Sv/3bv/2k9/8YXJ4AAOCmr776SvX19Ro3bpxR+7Vr1yopKUm9evVS9+7dtWHDBlVWVkr6dnKpWbNmKTU1VRMnTlReXp6qqqqc783KytIzzzyjlJQUrVq1SmVlZT+63k8++USzZ8/WW2+9pQceeOBHv//HIjQAAHBTcHCwcdstW7boxRdfVEZGhgoLC1VSUqLZs2eroaHB2SY/P1/FxcVKTk7W1q1bNWDAAB08eFCStHLlSp04cUITJkzQnj17dP/992vHjh3G+9+7d68mTpyoNWvWaObMmeYf8g4QGgAAuCk+Pl7BwcHavXv3bdvu379fycnJmj9/voYNG6a4uLhWRwuGDRum7OxsHThwQIMHD1ZBQYFz24ABA7Ro0SIVFhZq8uTJys/PN6qzqKhIEyZM0Guvvaa5c+eaf8A7xD0NAADcFBQUpKVLl2rJkiUKDAzUyJEj9fe//10nTpxocckiPj5e77zzjnbt2iW73a6//vWvOnTokOx2uySpvLxcGzZs0OOPP67o6GidOnVKp0+f1syZM3Xt2jUtXrxYv/rVr2S323Xu3DkdOnRIU6ZMuW2Nn3zyiR577DG98MILmjJliqqrqyVJgYGBioiIcP0f5TvcHhpyc3O1fft2nTx5UsHBwUpOTtZrr72mhIQEZ5u6ujr9y7/8i7Zs2aL6+nqlpqbqX//1X9WnTx93lwcA8DBXPaHRXZYvX64uXbpoxYoVOn/+vKKiovTss8+2aDdv3jwdPXpUTz31lPz8/DR9+nTNnz9fO3fulCR17dpVJ0+e1KZNm1RTU6OoqChlZmZq3rx5unHjhmpqajRz5kxduHBBPXv21OTJk5vdWNmWTZs26erVq8rNzVVubq5z/S9+8QsVFRW57O/QGj/Lsix37mD8+PGaNm2aHnroId24cUPLli3T8ePH9eWXX6pbt26SpOeee04fffSRNm7cqLCwMC1YsEB33XWX9u/fb7QPh8OhsLAw1dbWKjQ01J0fB7gjnfkx0r7+RQHXqqurU3l5uex2u4KCgrxdDtT2Mfkx36FuH2n4+OOPm73euHGjevfurSNHjmj06NGqra3Vn//8ZxUUFOiXv/ylpG9vHBk0aJAOHjyon/3sZ+4uEQAAGPD4jZC1tbWS5LzucuTIEV2/fl0pKSnONgMHDlRMTIyKi4tb7aO+vl4Oh6PZAgBAR5CWlqbu3bu3urz66qterc2jN0I2NTVp4cKFGjlypAYPHixJqq6uVmBgoMLDw5u17dOnj/Pmju/Lzc01uu4DAEB78/bbb+vatWutbnP3jY6349HQkJmZqePHj+vTTz+9o36ys7OVlZXlfO1wONSvX787LQ8AAK+75557vF1CmzwWGhYsWKAPP/xQ+/btU9++fZ3rIyMj1dDQoEuXLjUbbbhw4YIiIyNb7ctms8lms7m7ZADAHXLzvfb4EVxxLNx+T4NlWVqwYIF27NihPXv2OH+/ektSUpICAgKaPUjj1KlTqqys1IgRI9xdHgDADW5NvHT16lUvV4Jbbh2LnzopluSBkYbMzEwVFBTogw8+UEhIiPM+hbCwMAUHByssLEwZGRnKyspSRESEQkND9fzzz2vEiBH8cgIA2il/f3+Fh4fr4sWLkr59ZsGt2R3hWZZl6erVq7p48aLCw8Pl7+//k/tye2hYt26dJGnMmDHN1ufn52vWrFmSpDVr1uiuu+7SlClTmj3cCQDQft26xHwrOMC7wsPD27zsb8rtD3fyBB7uhPaChzuhM2psbNT169e9XUanFhAQ0OYIg0893AkA0Ln5+/vf0ZA4fAezXAIAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwwq8nAA8qLa90eZ+J9hiX9wkArWGkAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMMGEV2r+VYW7os9b1fbpJe5kEK3FTosv7LE0vdXmfANrGSAMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEa6eLsAAPipEjclurzP0vRSl/cJdBSMNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABjxSGjYt2+fJk6cqOjoaPn5+en9999vtn3WrFny8/NrtowfP94TpQEAAEMeCQ1XrlzR0KFDtXbt2jbbjB8/XlVVVc5l8+bNnigNAAAY8shjpNPS0pSWlvaDbWw2myIjIz1RDgAA+Al85p6GoqIi9e7dWwkJCXruuedUU1PTZtv6+no5HI5mCwAAcC+fmLBq/Pjxmjx5sux2u8rKyrRs2TKlpaWpuLhY/v7+Ldrn5uYqJyfHC5Wi01gZ5u0KAMDn+ERomDZtmvPfiYmJGjJkiGJjY1VUVKRx48a1aJ+dna2srCzna4fDoX79+nmkVgAAOiufuTzxXffdd5969uypM2fOtLrdZrMpNDS02QIAANzLJ0PDuXPnVFNTo6ioKG+XAgAAbvLI5Ylvvvmm2ahBeXm5SkpKFBERoYiICOXk5GjKlCmKjIxUWVmZlixZori4OKWmpnqiPAAAYMAjoeHw4cMaO3as8/Wt+xHS09O1bt06HTt2TJs2bdKlS5cUHR2tRx55RL///e9ls9k8UR4AADDgkdAwZswYWZbV5vZdu3Z5ogwAAHAHfPKeBgAA4HsIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI128XQCAzqG0vNLlfSbaY1zeJ4C2MdIAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEaYsArt3r11Bd4uwVhF0Axvl4DbSNyU6PI+S9NLXd4n4A2MNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAESasAtBCaXmlt0sA4IMYaQAAAEYIDQAAwAihAQAAGCE0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEY8Ehr27duniRMnKjo6Wn5+fnr//febbbcsSytWrFBUVJSCg4OVkpKi06dPe6I0AABgyCOh4cqVKxo6dKjWrl3b6vbVq1frjTfe0Pr16/XZZ5+pW7duSk1NVV1dnSfKAwAABjwy90RaWprS0tJa3WZZlv70pz/pd7/7nZ544glJ0jvvvKM+ffro/fff17Rp01q8p76+XvX19c7XDofDPYUDAAAnr09YVV5erurqaqWkpDjXhYWFafjw4SouLm41NOTm5ionJ8eTZQIucW9dgcv7rAia4fI+AaA1Xr8Rsrq6WpLUp0+fZuv79Onj3PZ92dnZqq2tdS5nz551e50AAHR2Xh9p+ClsNptsNpu3ywAAoFPx+khDZGSkJOnChQvN1l+4cMG5DQAAeJ/XQ4PdbldkZKR2797tXOdwOPTZZ59pxIgRXqwMAAB8l0cuT3zzzTc6c+aM83V5eblKSkoUERGhmJgYLVy4UH/4wx8UHx8vu92u5cuXKzo6WpMmTfJEeQAAwIBHQsPhw4c1duxY5+usrCxJUnp6ujZu3KglS5boypUrmjt3ri5duqRRo0bp448/VlBQkCfKAwAABjwSGsaMGSPLstrc7ufnp5dfflkvv/yyJ8oBAAA/gdfvaQAAAO0DoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAkXY59wQAtCeJmxLd0m9peqlb+gXawkgDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABhhwioA7VZpeaXL+0y0x7i8T6CjYKQBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwwYRXQzt1bV+DyPiuCZri8z/aCSbCAtjHSAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIs1wCQDuVuCnR5X2Wppe6vE90HIw0AAAAI4QGAABghNAAAACMEBoAAIARQgMAADBCaAAAAEYIDQAAwAihAQAAGPGJ0LBy5Ur5+fk1WwYOHOjtsgAAwHf4zBMhH3jgAf33f/+383WXLj5TGgAAkA+Fhi5duigyMtLbZQAAgDb4xOUJSTp9+rSio6N133336emnn1ZlZWWbbevr6+VwOJotAADAvXxipGH48OHauHGjEhISVFVVpZycHP385z/X8ePHFRIS0qJ9bm6ucnJyvFAp0DncW1fg8j4rgma4vE8AnuVnWZbl7SK+79KlS+rfv79ef/11ZWRktNheX1+v+vp652uHw6F+/fqptrZWoaGhniwVPuDe337k7RJgoDOHhkR7jLdLMMYsl52Pw+FQWFiY0XeoT4w0fF94eLgGDBigM2fOtLrdZrPJZrN5uCoAADo3n7mn4bu++eYblZWVKSoqytulAACAm3wiNLz44ovau3evKioqdODAAT355JPy9/fX9OnTvV0aAAC4yScuT5w7d07Tp09XTU2NevXqpVGjRungwYPq1auXt0sDAAA3+URo2LJli7dLAAAAt+ETlycAAIDvIzQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMOITP7lEJ7IyzA2dun5yJcCVSsvbnrX3TrSnOS3QMTDSAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGmLAKHnVvHZNLdVbuOPYVQTNc3ieAtjHSAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABGCA0AAMAIs1x6wb2//cjbJRipWDXB2yUA+AGl5ZWu73RlmBv6rHVDn+2kzg6GkQYAAGCE0AAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIwQGgAAgBFCAwAAMMKEVbfjjklRVOCGPl2vvUysBbjSvXWuPz8rgma4vE+p/dSauCnR5X2WurxHN+lgE2sx0gAAAIwQGgAAgBFCAwAAMEJoAAAARggNAADACKEBAAAYITQAAAAjhAYAAGCE0AAAAIz4VGhYu3at7r33XgUFBWn48OH6/PPPvV0SAAC4yWdCw9atW5WVlaWXXnpJ//M//6OhQ4cqNTVVFy9e9HZpAABAPjT3xOuvv645c+Zo9uzZkqT169fro48+0l/+8hf99re/bda2vr5e9fX1zte1td8+h9vhcLi+sHrL5V021V91eZ9AZ+Twax/npzvqlNpPrY3XGl3ep8MN/29WO/kOcXWdt747LcugVssH1NfXW/7+/taOHTuarZ85c6b1+OOPt2j/0ksvWZJYWFhYWFhYXLScPXv2tt/XPjHS8H//939qbGxUnz59mq3v06ePTp482aJ9dna2srKynK+bmpr0j3/8Qz169JCfn5/b65W+TWb9+vXT2bNnFRoa6pF9onUcC9/BsfAdHAvf4evHwrIsXb58WdHR0bdt6xOh4cey2Wyy2WzN1oWHh3ulltDQUJ/8j6Az4lj4Do6F7+BY+A5fPhZhYWFG7XziRsiePXvK399fFy5caLb+woULioyM9FJVAADgu3wiNAQGBiopKUm7d+92rmtqatLu3bs1YsQIL1YGAABu8ZnLE1lZWUpPT9c//dM/6eGHH9af/vQnXblyxflrCl9js9n00ksvtbhMAs/jWPgOjoXv4Fj4jo50LPwsy+Q3Fp7x5ptv6o9//KOqq6v14IMP6o033tDw4cO9XRYAAJCPhQYAAOC7fOKeBgAA4PsIDQAAwAihAQAAGCE0AAAAI4SG29i3b58mTpyo6Oho+fn56f3332+23bIsrVixQlFRUQoODlZKSopOnz7tnWI7uNsdi1mzZsnPz6/ZMn78eO8U24Hl5ubqoYceUkhIiHr37q1Jkybp1KlTzdrU1dUpMzNTPXr0UPfu3TVlypQWD2/DnTM5FmPGjGlxXjz77LNeqrjjWrdunYYMGeJ86uOIESO0c+dO5/aOck4QGm7jypUrGjp0qNauXdvq9tWrV+uNN97Q+vXr9dlnn6lbt25KTU1VXV2dhyvt+G53LCRp/Pjxqqqqci6bN2/2YIWdw969e5WZmamDBw/qb3/7m65fv65HHnlEV65ccbZZtGiR/uu//kvbtm3T3r17df78eU2ePNmLVXdMJsdCkubMmdPsvFi9erWXKu64+vbtq1WrVunIkSM6fPiwfvnLX+qJJ57QiRMnJHWgc+LO56jsPCQ1m4mzqanJioyMtP74xz861126dMmy2WzW5s2bvVBh5/H9Y2FZlpWenm498cQTXqmnM7t48aIlydq7d69lWd+eAwEBAda2bducbb766itLklVcXOytMjuF7x8Ly7KsX/ziF9YLL7zgvaI6sbvvvtt6++23O9Q5wUjDHSgvL1d1dbVSUlKc68LCwjR8+HAVFxd7sbLOq6ioSL1791ZCQoKee+451dTUeLukDq+2tlaSFBERIUk6cuSIrl+/3uy8GDhwoGJiYjgv3Oz7x+KWd999Vz179tTgwYOVnZ2tq1eveqO8TqOxsVFbtmzRlStXNGLEiA51TvjMY6Tbo+rqaklqdUrvW9vgOePHj9fkyZNlt9tVVlamZcuWKS0tTcXFxfL39/d2eR1SU1OTFi5cqJEjR2rw4MGSvj0vAgMDW8w8y3nhXq0dC0maMWOG+vfvr+joaB07dkxLly7VqVOntH37di9W2zGVlpZqxIgRqqurU/fu3bVjxw7df//9Kikp6TDnBKEBHca0adOc/05MTNSQIUMUGxuroqIijRs3zouVdVyZmZk6fvy4Pv30U2+X0um1dSzmzp3r/HdiYqKioqI0btw4lZWVKTY21tNldmgJCQkqKSlRbW2t/uM//kPp6enau3evt8tyKS5P3IFb03Yzpbdvuu+++9SzZ0+dOXPG26V0SAsWLNCHH36oTz75RH379nWuj4yMVENDgy5dutSsPeeF+7R1LFpzaz4fzgvXCwwMVFxcnJKSkpSbm6uhQ4cqLy+vQ50ThIY7YLfbFRkZ2WxKb4fDoc8++4wpvX3AuXPnVFNTo6ioKG+X0qFYlqUFCxZox44d2rNnj+x2e7PtSUlJCggIaHZenDp1SpWVlZwXLna7Y9GakpISSeK88ICmpibV19d3qHOCyxO38c033zRL5OXl5SopKVFERIRiYmK0cOFC/eEPf1B8fLzsdruWL1+u6OhoTZo0yXtFd1A/dCwiIiKUk5OjKVOmKDIyUmVlZVqyZIni4uKUmprqxao7nszMTBUUFOiDDz5QSEiI85psWFiYgoODFRYWpoyMDGVlZSkiIkKhoaF6/vnnNWLECP3sZz/zcvUdy+2ORVlZmQoKCvToo4+qR48eOnbsmBYtWqTRo0dryJAhXq6+Y8nOzlZaWppiYmJ0+fJlFRQUqKioSLt27epY54S3f77h6z755BNLUoslPT3dsqxvf3a5fPlyq0+fPpbNZrPGjRtnnTp1yrtFd1A/dCyuXr1qPfLII1avXr2sgIAAq3///tacOXOs6upqb5fd4bR2DCRZ+fn5zjbXrl2z5s+fb919991W165drSeffNKqqqryXtEd1O2ORWVlpTV69GgrIiLCstlsVlxcnLV48WKrtrbWu4V3QL/+9a+t/v37W4GBgVavXr2scePGWYWFhc7tHeWcYGpsAABghHsaAACAEUIDAAAwQmgAAABGCA0AAMAIoQEAABghNAAAACOEBgAAYITQAAAAjBAaAACAEUIDAAAwQmgAAABG/j/b1tU4SXHY1gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x2000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def trova_bin(x):\n",
    "    m=min(x)\n",
    "    M=max(x)\n",
    "    dx=M-m\n",
    "    return np.linspace(m-dx/20, M+dx/20,21)\n",
    "    \n",
    "fig, axs=plt.subplots(nrows=4, figsize=(6,20))\n",
    "colori=['red','green','blue']\n",
    "for i in range(4):\n",
    "    xi=X[:,i]\n",
    "    bins=trova_bin(xi)\n",
    "    xic=[xi[y==qi] for qi in range(3)]\n",
    "    axs[i].hist(xic, bins=bins, label=qual, stacked=True)\n",
    "    axs[i].legend()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest=train_test_split(X, y, test_size=0.1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "lda.fit(Xtrain, ytrain)\n",
    "xt=lda.transform(Xtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd38d8b8350>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    plt.scatter(xt[:,0][ytrain==i], xt[:,1][ytrain==i], label=qual[i])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd38dd99090>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R5ZyQYC0jvbIVa2TkpojYiq2y+yZJwc2SjCRbubxgnVRwvxS4Wyb5XCfZoo1TMwAARJENFcnuu0IKZh9YcvCrksplc2+QLX3fQbroo4jEKGutvlqxQf994UOteO0zlRSVuo4EADHPJPZS7UdDJClYeTmvCyUv7i8hoRoGWdmCudFK5BSnZmLQ8vnv6l+3/582f761allKarLOu+psXf6HCfIn+x2mA4AYlnSulDdDsgU1jzNNpaTR0cn0PbboyXBGSeUfyFZkyyRkRDyTSxwRiTHPzl6oP074S7USIklF+cV6+p6X9JsRd6i0mKMjAHA4xiTJpE2vfVza7TImKQqJDqPqlEw4Y7fWPibOUURiyKY12brv148c8XVrrda+96Uen/FM9EIBQJwxyRfIBO6WTOr+JV4duKmZTKpMYJZM8o9cxZNMHY5quypLUUQRiSEv/uMVeTymxjHWSi/d96rKSsujlAoA4o9JvkCm9X9lAndJKZdKKZfKBO6qXOayhEiSf5jC2v2aNCnxxIjHcY05IjHk3Rc/VChoax1X8G2hvvzoa/Uc0i0KqQAgPhnjl5LHyiSPdbJ+G9wuFT8jW7FZMj4Z36lS0jkyKZfIFj9dy7uNlHKxjPFFJatLFJEYUlpcFv5YrqIBgJhkbbls3p+k4v/sX1J5pNsWz5fymss0+4tM01/LFtxzhE8wUkIvmaa/iEpe1zg1E0PadkoPe2ybTq0jmAQAcLRs7q1S8ROqvCeIVeVluvsv1bX7ZPdNlnwDKuexeI+t/maTLKVcLtPiXzImObrBHeGISAw5d/LZmnPNAzWOMUbqfsrx6tC1XZRSAQDCZctXSyXP1jIqKJs3U55Wz0hJP5LKP5GCOytLiO9kGU+TqGSNFRwRiSHDLz1dbTqly+M98oRVK+ny2ydELxQAIGyV9wipbddqpYpVsuVfyBgj4+snk3yuTNKwRldCJIpITElukqS7X5uu1sfuP0VzUB8xHiOP16ObHr5W/c/u7SYgAKBm5atU8x1TD1KxNqJR4gWnZmJMu85t9M/Vf9HSp/6rRf98Xbs25yipaZJO+/Egjfn5CLVlbggAxLC6/H3PsQCJIhKT/Ml+jZw4TCMnDnMdBQBQF74BUsUXCuuoSOJJEY8TD6hjAADUE5PyE9VeQjySb5BMQmY0IsU8iggAAPXEJHSRmkyuYYRHMn6Z1FujlinWUUQAAKhHpumNMk2vl3Tge2K8qtrdejNlWvxHJvF4R+liD3NEAACoR8YYqek1UsplUsnLssHNknwy/lOlxAGVr6MKRQQAgAgwnqZSygRRO2oW0VMzWVlZGjhwoFJTU9W6dWuNHTtW69ati+QqAQBAHIloEVm2bJmmTJmi9957T0uWLFF5ebnOOeccFRYWRnK1AAAgThhrbe3fO19PcnJy1Lp1ay1btkxnnHFGrePz8vIUCASUm5urtLS0KCQEAAA/VF3231GdI5KbmytJatGixWFfLy0tVWnpd19vn5eXF5VcAADAjahdvhsKhTR16lQNGTJEPXv2POyYrKwsBQKBqkdGRka04gEAAAeidmrmmmuu0aJFi/T222+rQ4cOhx1zuCMiGRkZnJoBACCOxNypmWuvvVYLFizQ8uXLj1hCJMnv98vv90cjEgAAiAERLSLWWv3yl7/Uc889p6VLlyozk/vqAwCA70S0iEyZMkVPPPGEXnjhBaWmpmrnzp2SpEAgoOTk5EiuGgAAxIGIzhE50m1sH374YU2cOLHW93P5LgAA8Sdm5ohE8RYliLLysnK9Nf89vXT/q9r8+VZ5E7zqO7ynfvSLUeo5pJvreACAOBHVG5rVFUdEYlPe3nxNGzVDX368QcZIB36CPF6jUNBq7C9H6xezJ/HFTgDQSNVl/x21+4igYbDW6raxf9ZXn2zc//y710LByifPz12kp/78vIN0AIBw2eB22fLVshVbnJ7BoIigTla99YXWvLNONlTzD+2Tf35epcWlNY4BAESfLXlDob0XyeYMld07TnbPCNm958sWP++kkFBEUCevPrJUHm/tp1wKc4v03oIVUUgEAAiXLfyn7LdXS+Urq79Q8aVs7k2y+VlRLyMUEdTJrs05VadgamI8RjnZe6KQCAAQDlv2oWz+XQeeHX5Q0SNS6eJoRZJEEUEdJacmhTXOhqySmoQ3FkD8sDZYObegIlvWlrmOgzqwhY+q9t2+kS18KBpxqlBEUCeDzx8Q1jhjjE4+t2+E0wCIFhsqki24TzbnzMq5BXuGy+4epFDeTNngbtfxUAtrQ1Lp65JCtY2Uyj+VDe6NRixJFBHU0bCLT1OTZilSDdNEjNdo8AUD1DqjVfSCAYgYG8qT/eZi2YLZUuig0mELpaJ/ye69QLZik6t4CEuppGD4w21BxJJ8H0UEdZKU4tftz/xGib4EGc+hbcR4jNp2bK2p91/lIB2ASLC5t0oV63T4eQUhKbRPdt/PK//qRoxKkky4X63ikTwtIprme2sD6qbPsJ6a886fNHBUn2pHRvwpfp1/9Tn6+/tZat464CwfgPpjg9v3T16sqWSEpOAmqeztKKVCXRljpORxqn2375H8Z8l4UqMRS1KEb/GOhqtrv87604LfKmfrXm39cru8CV4d1zdTKal8mSHQoJQs0RGvsKjGI1uySMZ/RqQT4SiZlMtli56WVPMkY9NkcnQC7UcRwQ+S3qGl0ju0dB0DQITYUK4kr2qfXxCSQrlRSISjZRIypeb3ye77haRyVT/KVXmkxATukvFF90IDTs0AAI7IeFsqvEmOXsnDHyWxzvhPl2m1SGryP5JpIckjmTQp5ScyrRbIJP8o6pk4IgIAODL/KEkzVHsZCTrZiaHuTEIHmdSbpNSbXEeRxBERAEANjLfl/kmONX21g0dK6CklhnefIeBgFBEAQI1M2q2Sb/CRB3iPlWl+X+WVGUAdUUQAADUyJkmm+YMyaVlSQo/vXvAeI5N6s0zLZ2W8bdwFRFxjjggAoFbGJEopF8qkXChrK6+4MMbvOhYaAIoIAKBOjEl0HQENCKdmAACAMxQRAADgDEUEAAA4QxEBAADOUEQAAIAzFBEAAOAMRQQAADjDfUTQoFlrtfLN1Vr2f++q4NsCNUsP6KyfnqbupxzP7agBIAZQRNBg7diwS7de8GdtXpMtj9ejUCgkj8ejF+5drO6nHK/bn71RLdo2dx0TABo1Ts2gQdq361tdf8atyl67VZIUCoYku/9/Ja398CvdOOx2FeUXu4wJAI0eRQQN0vx7XtI3O79VKGgP+7oNWmWv266XH3wtyskAAAejiKDBKS8r18IHX5MNHb6EHOyFexdHIREA4EgoImhw9m7fp8LcorDG7ty4W2UlZRFOBAA4EiarosHxeOvWr42Hq2eAeGNDRVIoRzJ+ydOGq+DiGEUEDU7L9s3Vol0zfbPj25oHGimz57FK9PGV5kC8sBUbZAselEpekrT/aKa3o5RyuZRysYxhtxZvODWDBsfr9epHvxgl1fYHkpXGXjs6KpkA/HC27CPZPT+WSp5TVQmRpOBm2fw/yu67RtZyqjXeUETQII277lxl9jz2iKddjDHqdUZ3nX3FmVFOBuBo2FC+7L6rJJVICh1+UNky2YK50YyFekARQYOU3DRZ9yz9g045b4BkKueBeBM8Vf89/NLT9aeFv+W0DBAvip+TbL6kWq6GK3pM1pZEJRLqByfT0GClNm+qO56/STs27NJbz7yn/H2Fat46oDPGn6JWx7R0HQ9ADawtk2yhZFJlTIJsycthvrFQKn1XShoW2YCoNxQRNHjtOrfRhN9c4DoGgDDY0ndkCx+Rypar8uiHTzbpfCm4uw4fkhuhdIgEiggAICbYgn/IFsxW5ayBA6dgyvZPTj3CvJDD8bSq92yIHOaIAACcsyWv7i8h0qGlow4lxDSXfIPqKRWigSICAHDOFj6o2q+5r51p8jMZwyT0eEIRAQA4ZYPbpfJPVesVMZIqy8r3C8v+XVnyeKnJlfUbDhHHHBEc0Tc792nhA6/ptX8vU+7efKW1SNXwS07XmJ+P4KoTAPUntK8Og63UZIpU/KQU2ivJSL6TZVIul/zDudV7HDLW2nAqqBN5eXkKBALKzc1VWlqa6ziNyidvrNKtP/qzyopLdfBPiPEYJfoTdfuzv9HAkX2c5QPQcNjgNtmcMC+3NQF52nxY+T5bKilBxngjFw5HpS77b07N4BBbv9qh3593p0q/V0IkyYasyorLNH3sn7VpTbabgAAaFOM9RkroqdrniHik5PO/e5/xU0IaAIoIDvHsXxeoory8xtO1wYqg5v/lpeiFAtCgmSY/U+1zRDwyKZdEIw6iiCKCaoIVQb36r2UKBWv+hRAKWr3++FsqK+ELpgD8cCb5XKnJNfuffX/X5JHklWl2j0xClygnQ6RRRFBNYW6RSotKwxpbUVahvL35EU4EoLHwpF4v02ye5Dv5oKUJUtIYmZbzZZL4tuyGiKtmUI0/xVen8UlNkiKUBEBjZJKGySQNkw0VSLZA8jSTMfyeacg4IoJq/Ml+9Tmrp4yn5kljxmPUY/DxatqsSZSSAWhMjKepjLctJaQRoIjgEOOuGyMbqnmOiA1ZjZt6XpQSAQAaKooIDnHKef01buqYGsecd/U5OuP/nRKlRACAhoo5IjiEMUZX33OFOp2YoSf//Ly2r99Z9VrbzNaa8JsLdN5VZ3MHQwDAD8adVVEja62+/nST8vbkK7VFU3Xp00keDwfSAABHVpf9N0dEUCNjjI7rk+k6BgCggeJPWwAA4AxFBAAAOEMRAQAAzlBEAACAMxQRAADgDEUEAAA4QxEBAADOUEQAAIAzES8i9957rzp16qSkpCQNGjRIH3zwQaRXCQAA4kREi8hTTz2lG264QdOnT9eKFSvUu3dvjRw5Urt3747kagEAQJyIaBH5y1/+osmTJ2vSpEnq0aOH7r//fqWkpOihhx6K5GoBAECciFgRKSsr08cff6wRI0Z8tzKPRyNGjNC777572PeUlpYqLy+v2gMAADRcESsie/bsUTAYVJs2baotb9OmjXbu3HnY92RlZSkQCFQ9MjIyIhUPAADEgJi6ambatGnKzc2temRnZ7uOBAAAIighUh/cqlUreb1e7dq1q9ryXbt2qW3btod9j9/vl9/vj1QkAAAQYyJ2RMTn86l///56/fXXq5aFQiG9/vrrGjx4cKRWCwAA4kjEjohI0g033KArrrhCAwYM0Mknn6zZs2ersLBQkyZNiuRqAQBAnIhoEbnooouUk5Oj2267TTt37lSfPn20ePHiQyawAgCAxslYa63rEEeSl5enQCCg3NxcpaWluY4DAADCUJf9d0xdNQMAABoXiggAAHCGIgIAAJyhiAAAAGcoIgAAwBmKCAAAcIYiAgAAnKGIAAAAZygiAADAGYoIAABwhiICAACcoYgAAABnKCIAAMAZiggAAHCGIgIAAJyhiAAAAGcoIgAAwBmKCAAAcIYiAgAAnKGIAAAAZygiAADAGYoIAABwhiICAACcoYgAAABnKCIAAMAZiggAAHCGIgIAAJyhiAAAAGcoIgAAwBmKCAAAcIYiAgAAnKGIAAAAZygiAADAGYoIAABwhiICAACcoYgAAABnElwHAABER3kwqCUbvtbTn6/S1rw8NfH5dE7n4zThxF5qlZLiOh4aKYoIGhVrrdbk7Nb6b/bK6/GoX7v2OiY1zXUsIOJ25Ofr8ufn6+t938hIsvuXr9q1U3/74F3NHjlGo47r6jIiGimKCBqNd7I3a+Zby/TFnpyqZUbSsE6dNf3Ms5QRCLgLB0RQSUW5LnnuaW35dp+k70rIgf8uCwZ17aKX9MS4CTr5mA5OMqLxYo4IGoVXv/5KVzz/TLUSIlX+El66aYPGPvWYNn/7rZNsQKS9uG6tNn27T6EaxlhrNef9/0YtE3AARQQNXn5pqa5/5WWFrD3s6yFJuSUluvm1V6IbDIiSJ1Z/WusYK+ndrdnKzs2NfCDgIBQRNHjPr/tCxRUVNY4JSfpg+1Z9tXdvdEIBUbSpDkf7tuSFPxaoDxQRNHhvb9kU9th3sjdHLgjgiN/rrcNYpg4iuigiaPBKKoJhjfOoctIe0NAM69Q5rF/2qT6ferZuHfE8wMEoImjwMps1C+sHPSRx5QwapEtP6lPjRFWp8gqyn/Q8SUkJidGIBFShiKDBm3Bir1p/CUtSs6QkDc/sEvE8QLT1bN1GUwedesTXjaTu6en61cmDoxcK2I8iggavR3prjezSVaaWcdefMkS+OpxLB+LJrwYN1p3Dz1G7pqnVlvu8Xl3cq7eevPAnauLzOUqHxsxYe4RrGmNAXl6eAoGAcnNzlZbG3S9x9IrLy/XLRQv0xqYN8khVR0g8qrxs8fpThujak09xFxCIkpC1+mDbVu0syFdyYqIGd8hQmj/JdSw0MHXZf1NE0GhYa/Xh9m16YtWnWrs3RwkerwZ3yNAlvXqrU7PmruMBQINRl/0312mh0TDG6ORjOnALawCIIcwRAQAAzlBEAACAMxQRAADgDEUEAAA4QxEBAADOUEQAAIAzFBEAAOAMRQQAADhDEQEAAM5QRAAAgDMUEQAA4AxFBAAAOEMRAQAAzlBEAACAMxEpIps2bdKVV16pzMxMJScnq0uXLpo+fbrKysoisToAABCnEiLxoWvXrlUoFNK8efN03HHHafXq1Zo8ebIKCws1a9asSKwSAADEIWOttdFY0d1336377rtPGzZsCPs9eXl5CgQCys3NVVpaWgTTAQCA+lKX/XdEjogcTm5urlq0aFHjmNLSUpWWllY9z8vLi3QsAADgUFQmq65fv15z587VVVddVeO4rKwsBQKBqkdGRkY04gEAAEfqVERuueUWGWNqfKxdu7bae7Zt26ZRo0Zp/Pjxmjx5co2fP23aNOXm5lY9srOz6/4vAgAAcaNOc0RycnK0d+/eGsd07txZPp9PkrR9+3YNHTpUp5xyih555BF5PHU7AMMcEQAA4k/E5oikp6crPT09rLHbtm3TsGHD1L9/fz388MN1LiEAAKDhi8hk1W3btmno0KHq2LGjZs2apZycnKrX2rZtG4lVAgCAOBSRIrJkyRKtX79e69evV4cOHaq9FqWrhQEAQByIyPmSiRMnylp72AcAAMABTNwAAADOUEQAAIAzFBEAAOAMRQQAADhDEQEAAM5QRAAAgDMUEQAA4AxFBAAAOEMRAQAAzlBEAACAMxQRAADgDEUEAAA4QxEBAADOUEQAAIAzFBEAAOAMRQQAADhDEQEAAM5QRAAAgDMUEQAA4AxFBAAAOEMRAQAAzlBEAACAMxQRAADgDEUEAAA4QxEBAADOJLgOAKDxWbd3j7bm5So5IVF927ZTcmKi60gAHKGIAIia1zd8rb++/44+z8mpWtYkMVE/6XmSrj9liFIoJECjw6kZAFHx5OrPNHnB89VKiCQVlpfroU8+1sXPPKXCsjJH6QC4whERABG3Jfdb/f7N1474upW0evcu/fW9/+r3ZwyNWq7aFJSV6cV1X2jV7l2SpB7prTX2hO5K9fsdJwMaDooIgIh7fNWnkrU1jrGSnlzzmW4YHBunaJ5a/ZnuWP6miisq5DVGkhS0VllvL9PvTh+qS3r1dpwQaBg4NQMg4pZsWK9QGOOKysu1Ysf2iOepzf+tWaVpbyxRcUWFpMoCEtxfpEoqKnTrm69VlisAPxhFBEDEFZdXhD22pKI8gklqV1xerhlvLa113My3ljGnBagHFBEAEZeRliYT5tj2qWkRzVKbhV+tU0EYBaO4olwvfrk2ComAho0iAiDiftLzJNU8Q6RSt1at1L1VesTz1OTznN1Vc0Jq4jVGn+fsjkIioGGjiACIuDFdT1DHQLNaf+FMHXSqTBglIJLqsn63SYGGgSICIOL8CQl67MfjlRFoJqn6Dtyz//kfh43QOV26OkhXXa/WbasmptYkaK1OatM2ComAho3LdwFExTFpaVp8yRVa+NU6/Wf1Z9qSm6vkxASd3fk4/bRXb2U2a+46oiRp9HFd9YdlfuWWltY4rqnPp/OOPyFKqYCGiyICIGr8CQka1/1Ejet+ousoR+RPSNAfh43QrxYvrHHcHUOHKynB/f1OgHhHEQGA7znv+G4yMrp16Wv6tqSk2g3NAv4k3T70LF1wQnfHKYGGgSICAIcx5vgTdHaX4/Tq119V3eL9xPTWGtmlq/wJ/OoE6gv/bwKAI/B5vTrv+G467/hurqMADRZXzQAAAGcoIgAAwBmKCAAAcIYiAgAAnKGIAAAAZygiAADAGYoIAABwhiICAACcoYgAAABnKCIAAMAZiggAAHCGIgIAAJyhiAAAAGcoIgAAwBmKCAAAcIYiAgAAnKGIAAAAZygiAADAGYoIAABwhiICAACcoYgAAABnKCIAAMAZiggAAHAm4kWktLRUffr0kTFGK1eujPTqAABAHIl4EbnpppvUvn37SK8GAADEoYgWkUWLFunVV1/VrFmzIrkaAAAQpxIi9cG7du3S5MmT9fzzzyslJSWs95SWlqq0tLTqeV5eXqTiAQCAGBCRIyLWWk2cOFFXX321BgwYEPb7srKyFAgEqh4ZGRmRiAcAAGJEnYrILbfcImNMjY+1a9dq7ty5ys/P17Rp0+oUZtq0acrNza16ZGdn1+n9AAAgvhhrrQ13cE5Ojvbu3VvjmM6dO2vChAl66aWXZIypWh4MBuX1enXJJZfo0UcfDWt9eXl5CgQCys3NVVpaWrgxAQCAQ3XZf9epiIRry5Yt1eZ3bN++XSNHjtT8+fM1aNAgdejQIazPoYgAABB/6rL/jshk1WOPPbba86ZNm0qSunTpEnYJAQAADR93VgUAAM5E7PLdg3Xq1EkROAMEAADiHEdEAACAMxQRAADgDEUEAAA4QxEBAADOUEQAAIAzFBEAAOAMRQQAADhDEQEAAM5QRAAAgDMUEQAA4AxFBAAAOEMRAQAAzlBEAACAMxQRALX6tqRY2/PzVFpR4ToKgAYmwXUAALHJWqsX1q3VIys/1me7d0mSfF6vLjihm37Wd6C6tmzpOCGAhoAiAuAQIWt182uv6Jkv1sgctLwsGNQzn6/Ri+vW6sHzf6zTju3oLCOAhoFTMwAO8cjKFXrmizWSJPu910KSSoNBXbXgeeUUFUY9G4CGhSICoJpgKKQHV3xU67iSigr935pVUUgEoCGjiACo5rNdO7WrsKDWcVbS82u/iHwgAA0aRQRANd+WloQ/tiT8sQBwOBQRANW0SEoOf2xy+GMB4HAoIgCq6dWmrY5JTQtr7LjuPSKcBkBDRxEBUI3HGF3Vf2CNY4ykJok+je/RMzqhADRYFBEAh7ikV29ddlIfSYf+kvBISk5I1EMX/FgtklOiHQ1AA8MNzQAcwhij2888S6dldNSjn67Qf7dmS5JSEhM14cRemti7r44NNHMbEkCDQBEBcFjGGJ3d5Tid3eU4lVZUqKSiQk19Pnk9HEgFUH8oIgBq5U9IkD+BXxcA6h9/2gAAAGcoIgAAwBmKCAAAcIYiAgAAnKGIAAAAZygiAADAGYoIAABwhiICAACciek7FFlrJUl5eXmOkwAAgHAd2G8f2I/XJKaLSH5+viQpIyPDcRIAAFBX+fn5CgQCNY4xNpy64kgoFNL27duVmpoqY4zy8vKUkZGh7OxspaWluY4XN9huR4ftdnTYbkeH7XZ02G5HJ9LbzVqr/Px8tW/fXp5avp8qpo+IeDwedejQ4ZDlaWlp/MAdBbbb0WG7HR2229Fhux0dttvRieR2q+1IyAFMVgUAAM5QRAAAgDNxVUT8fr+mT58uv9/vOkpcYbsdHbbb0WG7HR2229Fhux2dWNpuMT1ZFQAANGxxdUQEAAA0LBQRAADgDEUEAAA4QxEBAADOxHURWbhwoQYNGqTk5GQ1b95cY8eOdR0pbpSWlqpPnz4yxmjlypWu48S0TZs26corr1RmZqaSk5PVpUsXTZ8+XWVlZa6jxZx7771XnTp1UlJSkgYNGqQPPvjAdaSYlpWVpYEDByo1NVWtW7fW2LFjtW7dOtex4s6dd94pY4ymTp3qOkrM27Ztmy699FK1bNlSycnJ6tWrlz766COnmeK2iDzzzDO67LLLNGnSJH366ad655139NOf/tR1rLhx0003qX379q5jxIW1a9cqFApp3rx5WrNmjf7617/q/vvv129/+1vX0WLKU089pRtuuEHTp0/XihUr1Lt3b40cOVK7d+92HS1mLVu2TFOmTNF7772nJUuWqLy8XOecc44KCwtdR4sbH374oebNm6eTTjrJdZSYt2/fPg0ZMkSJiYlatGiRPv/8c91zzz1q3ry522A2DpWXl9tjjjnG/vOf/3QdJS69/PLLtlu3bnbNmjVWkv3kk09cR4o7d911l83MzHQdI6acfPLJdsqUKVXPg8Ggbd++vc3KynKYKr7s3r3bSrLLli1zHSUu5Ofn265du9olS5bYM88801533XWuI8W0m2++2Z522mmuYxwiLo+IrFixQtu2bZPH41Hfvn3Vrl07jR49WqtXr3YdLebt2rVLkydP1r///W+lpKS4jhO3cnNz1aJFC9cxYkZZWZk+/vhjjRgxomqZx+PRiBEj9O677zpMFl9yc3MliZ+tME2ZMkVjxoyp9nOHI3vxxRc1YMAAjR8/Xq1bt1bfvn314IMPuo4Vn6dmNmzYIEm6/fbb9fvf/14LFixQ8+bNNXToUH3zzTeO08Uua60mTpyoq6++WgMGDHAdJ26tX79ec+fO1VVXXeU6SszYs2ePgsGg2rRpU215mzZttHPnTkep4ksoFNLUqVM1ZMgQ9ezZ03WcmPfkk09qxYoVysrKch0lbmzYsEH33XefunbtqldeeUXXXHONfvWrX+nRRx91miumisgtt9wiY0yNjwPn6yXpd7/7nS688EL1799fDz/8sIwxevrppx3/K6Iv3O02d+5c5efna9q0aa4jx4Rwt9vBtm3bplGjRmn8+PGaPHmyo+RoiKZMmaLVq1frySefdB0l5mVnZ+u6667T448/rqSkJNdx4kYoFFK/fv00c+ZM9e3bVz//+c81efJk3X///U5zJThd+/f8+te/1sSJE2sc07lzZ+3YsUOS1KNHj6rlfr9fnTt31pYtWyIZMSaFu93eeOMNvfvuu4d8t8CAAQN0ySWXOG/F0Rbudjtg+/btGjZsmE499VQ98MADEU4XX1q1aiWv16tdu3ZVW75r1y61bdvWUar4ce2112rBggVavny5OnTo4DpOzPv444+1e/du9evXr2pZMBjU8uXL9fe//12lpaXyer0OE8amdu3aVdtvSlL37t31zDPPOEpUKaaKSHp6utLT02sd179/f/n9fq1bt06nnXaaJKm8vFybNm1Sx44dIx0z5oS73f72t79pxowZVc+3b9+ukSNH6qmnntKgQYMiGTEmhbvdpMojIcOGDas6+ubxxNTBROd8Pp/69++v119/veoy+lAopNdff13XXnut23AxzFqrX/7yl3ruuee0dOlSZWZmuo4UF4YPH65Vq1ZVWzZp0iR169ZNN998MyXkCIYMGXLI5eFffvml8/1mTBWRcKWlpenqq6/W9OnTlZGRoY4dO+ruu++WJI0fP95xuth17LHHVnvetGlTSVKXLl34K6wG27Zt09ChQ9WxY0fNmjVLOTk5Va/x1/53brjhBl1xxRUaMGCATj75ZM2ePVuFhYWaNGmS62gxa8qUKXriiSf0wgsvKDU1tWo+TSAQUHJysuN0sSs1NfWQeTRNmjRRy5YtmV9Tg+uvv16nnnqqZs6cqQkTJuiDDz7QAw884PwIb1wWEUm6++67lZCQoMsuu0zFxcUaNGiQ3njjDffXQ6PBWbJkidavX6/169cfUtgsX15d5aKLLlJOTo5uu+027dy5U3369NHixYsPmcCK79x3332SpKFDh1Zb/vDDD9d62hCoq4EDB+q5557TtGnTdMcddygzM1OzZ8/WJZdc4jSXsfwmBQAAjnCiGwAAOEMRAQAAzlBEAACAMxQRAADgDEUEAAA4QxEBAADOUEQAAIAzFBEAAOAMRQQAADhDEQEAAM5QRAAAgDMUEQAA4Mz/BwlOpwuCxCKwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yp=lda.predict(Xtest)\n",
    "xt=lda.transform(Xtest)\n",
    "plt.scatter(xt[:,0], xt[:,1], s=50, c=ytest, label=\"test\")\n",
    "plt.scatter(xt[:,0], xt[:,1], s=25, c=yp, label=\"true\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fd38d8b6350>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#metrics.plot_confusion_matrix(lda, Xtest, ytest)\n",
    "metrics.ConfusionMatrixDisplay.from_predictions(ytest,yp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}