tutorials/example_hand_written_digits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Using `IntelELM` for Recognizing Hand-written Digits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Install `IntelELM` library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: intelelm==1.0.3 in /usr/local/python/3.10.8/lib/python3.10/site-packages (1.0.3)\n",
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      "Requirement already satisfied: pandas>=1.3.5 in /home/codespace/.local/lib/python3.10/site-packages (from intelelm==1.0.3) (2.1.1)\n",
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      "Requirement already satisfied: pytz>=2020.1 in /home/codespace/.local/lib/python3.10/site-packages (from pandas>=1.3.5->intelelm==1.0.3) (2023.3.post1)\n",
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      "Requirement already satisfied: cycler>=0.10 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib>=3.3.0->mealpy>=2.5.4->intelelm==1.0.3) (0.12.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib>=3.3.0->mealpy>=2.5.4->intelelm==1.0.3) (4.43.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib>=3.3.0->mealpy>=2.5.4->intelelm==1.0.3) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib>=3.3.0->mealpy>=2.5.4->intelelm==1.0.3) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib>=3.3.0->mealpy>=2.5.4->intelelm==1.0.3) (10.0.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/codespace/.local/lib/python3.10/site-packages (from matplotlib>=3.3.0->mealpy>=2.5.4->intelelm==1.0.3) (3.1.1)\n",
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      "Requirement already satisfied: six>=1.5 in /home/codespace/.local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=1.3.5->intelelm==1.0.3) (1.16.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.27.0->opfunu>=1.0.0->mealpy>=2.5.4->intelelm==1.0.3) (3.2.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.27.0->opfunu>=1.0.0->mealpy>=2.5.4->intelelm==1.0.3) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.27.0->opfunu>=1.0.0->mealpy>=2.5.4->intelelm==1.0.3) (2.0.5)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.27.0->opfunu>=1.0.0->mealpy>=2.5.4->intelelm==1.0.3) (2023.7.22)\n"
     ]
    }
   ],
   "source": [
    "!python -m pip install intelelm==1.0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets, metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from intelelm import ElmClassifier, MhaElmClassifier\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Digits Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset: Train/Test Split & Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = len(digits.images)\n",
    "data = digits.images.reshape((n_samples, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, digits.target, test_size=0.5, shuffle=False, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remember to scale features before model fitting. For this example, we use MinMaxScaler.\n",
    "min_max_scaler = MinMaxScaler()\n",
    "min_max_scaler.fit(X_train)\n",
    "X_train_scaled = min_max_scaler.transform(X_train)\n",
    "X_test_scaled = min_max_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using label encoding for classification\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(digits.target)\n",
    "\n",
    "y_train_le = label_encoder.transform(y_train)\n",
    "y_test_le = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification with Standard ELM Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Train an `ElmClassifier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/codespace/.local/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "ElmClassifier(act_name='relu', hidden_size=100)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elm_clf = ElmClassifier(hidden_size=100, act_name=\"relu\")\n",
    "elm_clf.fit(X_train_scaled, y_train_le)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "elm_predicted = elm_clf.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Report classification results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.99      0.92        88\n",
      "           1       0.91      0.86      0.88        91\n",
      "           2       0.95      0.91      0.93        86\n",
      "           3       0.89      0.82      0.86        91\n",
      "           4       0.96      0.88      0.92        92\n",
      "           5       0.85      0.91      0.88        91\n",
      "           6       0.92      0.99      0.95        91\n",
      "           7       0.93      0.92      0.93        89\n",
      "           8       0.83      0.72      0.77        88\n",
      "           9       0.74      0.82      0.77        92\n",
      "\n",
      "    accuracy                           0.88       899\n",
      "   macro avg       0.88      0.88      0.88       899\n",
      "weighted avg       0.88      0.88      0.88       899\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test_le, elm_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix [ELM]:\n",
      "[[87  0  0  0  0  0  1  0  0  0]\n",
      " [ 3 78  1  1  1  0  1  0  2  4]\n",
      " [ 3  3 78  2  0  0  0  0  0  0]\n",
      " [ 0  2  1 75  0  5  0  2  3  3]\n",
      " [ 1  0  0  0 81  0  0  2  3  5]\n",
      " [ 0  0  0  1  0 83  1  0  0  6]\n",
      " [ 0  0  1  0  0  0 90  0  0  0]\n",
      " [ 0  0  0  1  2  2  0 82  1  1]\n",
      " [ 0  3  1  2  0  6  4  1 63  8]\n",
      " [ 7  0  0  2  0  2  1  1  4 75]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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IIYSowqSQCyGEEFWYFHIhhBCiCpNCLoQQQlRhUsiFEEKISpKens6YMWMIDAzEwcGBDh06cOTIEcPziqIwbdo0atasiYODAyEhIURFRZm0DynkQgghRCUZPnw427dvZ+3atZw6dYpu3boREhLC1atXAZg3bx5LlixhxYoVHDp0CCcnJ0JDQ8nJySn1PjSKoiiV9QIqm16vJz4+HhcXFzQajdpxhBBCmEhRFNLT0/H398fKqvLaljk5OeTl5ZV7O4qiFKs39vb22NvbF1s2OzsbFxcXvv/+e3r06GGY365dO7p3787s2bPx9/fnrbfeYvz48QCkpqbi6+vL6tWref7550uVyaYcr0d18fHxBAQEqB1DCCFEOcXFxVG7du1K2XZOTg5Bgc4kXNeVe1vOzs5kZGQYzZs+fTozZswotmxBQQE6nQ6tVms038HBgX379nHp0iUSEhIICQkxPOfm5kb79u05cOBA9SjkLi4uAJw44o2Ls+WcJRje9B9qRxCicllZq52gOH35P6SF+RWQzz7+Z/g8rwx5eXkkXNcRc6wuri5lrxVp6XoC210mLi4OV1dXw/ySWuNQWKOCg4OZPXs2TZs2xdfXly+//JIDBw7QoEEDEhISAPD19TVaz9fX1/BcaVTpQl7UveHibIVLOf44Fc1GY6t2BCEql8YCC7nGcj4DhAn+OrlrjtOjzi4anF3Kvh89heu6uroaFfK/s3btWoYNG0atWrWwtrbmoYceYtCgQRw7dqzMOe4m73whhBDVgk7Rl3syVf369dm9ezcZGRnExcVx+PBh8vPzqVevHn5+fgAkJiYarZOYmGh4rjSkkAshhKgW9CjlnsrKycmJmjVrkpyczNatW+nVqxdBQUH4+fmxc+dOw3JpaWkcOnSI4ODgUm+7SnetCyGEEJZs69atKIpC48aNuXDhAhMmTKBJkyYMHToUjUbDmDFjePfdd2nYsCFBQUFMnToVf39/evfuXep9SCEXQghRLejRY3rnuPH6pkpNTWXy5MlcuXIFT09P+vbty5w5c7C1LRxLNXHiRDIzMxkxYgQpKSl06tSJLVu2FBvp/neq9O/I09LScHNz48JZX4sa7DY4oKPaEYSoXDJqXVSQAiWfXXxPampqqQeQmaqoVsSdq1XuUesBTa5WataysJzqJ4QQQgiTSde6EEKIaqG8A9bKs25lkkIuhBCiWtCjoHsAC7l0rQshhBBVmLTIhRBCVAvStf4A0Ovg24V1+G2jNynXbfHwzePx/tfp/eYViq4OeK8R54PeucwzI6+aLWvPIUn0e/U6nt4FRJ9x4KMptYg86Wi2/UsmyVSZWrRPp//IRBq2zMbLL58ZL9fjwFZ31fIUsbTjJJkqlk5R0JXjh1rlWbcyVauu9c0f1WbHWj/CZkcz/9cTPP+fGH5cUZutq2oalll27LDRNOL/otBoFB7tnmS2nJ2fTWbE9HjWLfRjVGgjos9omRMRjZtXvtkySCbJVJm0jnqizzjy4RTLuXuhJR4nySRKwyIK+bJly6hbty5arZb27dtz+PDhStnP+WMutOt2i7ZPJuMdkEv7Hjdp+Xgy0SedDcu4++QbTce2edKsQyo+gbmVkqkkfUYksSXCk21feRIbpWXJpNrkZmsIHXTLbBkkk2SqTEd/dWPNfH/2b3FXLcPdLPE4SaaKpa+AyRKpXsi/+uorxo0bx/Tp0zl+/DitW7cmNDSU69evV/i+GrVL5/RvblyLLrxiTswZRyKPuNK6a0qJy6fesOXkLx50HphY4vOVwcZWT8NWWRzfe/uWfoqi4cReF5q1yzJbDskkmaoTSzxOkqni6f4atV6eyRKpXsgXLlzIK6+8wtChQ2nWrBkrVqzA0dGRzz//vML31XPUFYKfTWJCl4d4KSiYd55uw9Mvx9PxuRslLr/nGx+0Tjoe6X6zwrPci6unDmsbSLlhPHwhOckGD+8Cs+WQTJKpOrHE4ySZKp5OKf9kiVQd7JaXl8exY8eYPHmyYZ6VlRUhISEcOHCg2PK5ubnk5t7u4k5LSzNpf4c21+C3jd6MWnqeWo2yiDnjxH9nBP016K14Md/9lQ8dn7uBndZC/3pCCCGqPVVb5ElJSeh0Onx9fY3m+/r6kpCQUGz58PBw3NzcDFNAgGkDZSLm1KXna1cI7pVEnaZZPNb3Bk8Pj+eHZbWLLXvukCvXLjrSZZD5utUB0m5ZoysA97u+3XrUKCD5hjrfuySTZHrQWeJxkkwVT86RW4DJkyeTmppqmOLi4kxaPy/bCqu7XrGVtYKi1xRbdtd6H4JaZhDYzLznfQryrYj6w5G2ndIN8zQahTadMjhzTJ2fd0gmyfSgs8TjJJkqnh4NunJMeorXCkug6leoGjVqYG1tTWKicas3MTERPz+/Ysvb29tjb29f5v21DbnFpqW18aqVS+1GWVz+04mfP6lVbDBbVro1h3+qwQtTL5d5X+Xx3coajF8cx/nfHYk84chzr9xA66hn23pPVfJIJslU0bSOOvzr3j5N5heQS71mWaSn2HAj3k6VTJZ4nCSTKA1VC7mdnR3t2rVj586dhpuo6/V6du7cyejRoyt8f2GzL/HN/9Vh1Tv1SEsqvCDME4MT6DPGuGV/8IcaKAp06FXyILjKtvsHD9y8dLw0IQEP7wKiTzvwzuAgUpJsVckjmSRTRWvUOov5X0cZHo+cUXixpW0bPFkwrq4qmSzxOEmmiqVXCqfyrG+JVL8f+VdffUVYWBgff/wxjz76KIsXL2bDhg2cO3eu2Lnzu8n9yIVQidyPXFQQc96P/NBpP5zLUSsy0vW0b55gcfcjV310wsCBA7lx4wbTpk0jISGBNm3asGXLlvsWcSGEEEJYQCEHGD16dKV0pQshhBBFigatlWd9S2QRhVwIIYSobHpFg14pezEuz7qVyXJOLAshhBDCZNIiF0IIUS1I17oQQghRhemwQleOjmhL/V2EFHIhhBDVglLOc+SKnCMXQgghREWTFrkQQohqQc6RCyGEEFWYTrFCp5TjHLmFXqJVutaFEEKIKkxa5EIIIaoFPRr05Wi/6rHMJrkUciGEENWCnCO3YCMefgIbjTr3MC7J0MgzakcoZlXTempHEGUld/WqsjT29mpHKMbKQat2BCOKkgcpaqeo2uQcuRBCiGqhaLBbeSaT9qfTMXXqVIKCgnBwcKB+/frMnj2bO+8erigK06ZNo2bNmjg4OBASEkJUVJRJ+5FCLoQQolooPEdevskUc+fOZfny5Xz44YecPXuWuXPnMm/ePJYuXWpYZt68eSxZsoQVK1Zw6NAhnJycCA0NJScnp9T7eSC61oUQQghzSUtLM3psb2+PfQmnUfbv30+vXr3o0aMHAHXr1uXLL7/k8OHDQGFrfPHixUyZMoVevXoB8MUXX+Dr68umTZt4/vnnS5VHWuRCCCGqBf1f11ov61Q04j0gIAA3NzfDFB4eXuL+OnTowM6dOzl//jwAv//+O/v27aN79+4AXLp0iYSEBEJCQgzruLm50b59ew4cOFDq1yUtciGEENVC+S8IU3huOy4uDldXV8P8klrjAG+//TZpaWk0adIEa2trdDodc+bMYfDgwQAkJCQA4Ovra7Ser6+v4bnSkEIuhBCiWtDf0aou2/qFhdzV1dWokN/Lhg0bWLduHRERETRv3pyTJ08yZswY/P39CQsLK3OOu0khF0IIISrBhAkTePvttw3nulu2bElMTAzh4eGEhYXh5+cHQGJiIjVr1jSsl5iYSJs2bUq9HzlHLoQQolrQKZpyT6bIysrCysq4zFpbW6PX6wEICgrCz8+PnTt3Gp5PS0vj0KFDBAcHl3o/0iIXQghRLRQNWiv7+qZdorVnz57MmTOHOnXq0Lx5c06cOMHChQsZNmwYABqNhjFjxvDuu+/SsGFDgoKCmDp1Kv7+/vTu3bvU+5FCLoQQQlSCpUuXMnXqVF577TWuX7+Ov78///73v5k2bZphmYkTJ5KZmcmIESNISUmhU6dObNmyBa229Ffgk0IuhBCiWtArVujLMWpdr5jWIndxcWHx4sUsXrz4nstoNBpmzZrFrFmzypxLCrkQQohqwdxd6+ZSrQt5jxcS6DEoAd/auQDERDkQ8WEAR/d4mC3D10/UIuNq8T9DkxfSCZ5+i6wbVhyd50H8fgfyMzW4BuXTemQadUOzzJYRoEX7dPqPTKRhy2y8/PKZ8XI9Dmx1N2sGyVR2PYck0e/V63h6FxB9xoGPptQi8qSjannkON3fwFfj6RiaTO362eTlWHHmuDOfzw3gSrSDKnlK0n94LEPHXWbTF7VY+X59teNUW9V61HpSgh2r/i+Q13u34o3nWvH7ATemLT9HnQbmK5I9v7nGwH1xhil0VSIAdZ/OBGDvpBqkXrLlyeXX6b35GoFPZbNrTA1unrE1W0YAraOe6DOOfDglwKz7/TuSqXQ6P5vMiOnxrFvox6jQRkSf0TInIho3r3zVMslxur+W7dPZvNaHsX2aMfmlJtjYKMz5IhJ7B8u4G17DFul0H3CN6HNOakcpNT3lG7muV/sF3IOqhXzPnj307NkTf39/NBoNmzZtMuv+D/3iyZHdHsTHOHD1sgNrFgWSk2VNkzbpZsug9dTj6H17ivvVAZc6+fg9WthLcP2EPU1fTMe7VR4uAQW0eS0VO1c9N0+b9/aIR391Y818f/ZvcTfrfv+OZCqdPiOS2BLhybavPImN0rJkUm1yszWEDrqlWiY5Tvc3ZUhjtn/rTUyUI5fOOrJgQj18a+XRsGWmKnnupHXUMXHeOZZMb0RGWtXp2C26IEx5JkukaqrMzExat27NsmXL1IwBgJWVQuceSWgddZw76aJKBl0eXPzBiYZ9M9D89XNFn7a5XPrZkdwUKxQ9RP/kiC5Xg9+jpb8zjqi+bGz1NGyVxfG9t9/TiqLhxF4XmrUz7+kZS1YVjpOjS2FLPD1F/cL52pQoDu/25OQB852GFPem6juie/fuhovHq6Vuo0wWbjiFnb2e7CxrZr/WhNgL6pwTi93hSF66FQ2fu/2Nu8viG+wa601E+wA0Ngo2WoUnPryBa2CBKhlF1eLqqcPaBlJuGP9TT06yIaBBrkqpLI+lHyeNRmHk1BhOH3Em5rx6YxsAHu9+nQbNMnhzwEOq5iiL8l9r3TJb5Op/tTNBbm4uubm3/1HdfSu5srhyyYFRz7bGyUVHp6dv8ta8KCYObqFKMT//rTO1H8/G0ff2ObATH7iTl2ZF6OpEtB46YnY4smuMN93XJeDZWL1znEII8xk1K4a6jbN5q38zVXPU8Mvh35Mv8s7wluTnWWZR+ztluaf43etboipVyMPDw5k5c2aFbrMg34prsYWjQC+cdqZRywx6hV1j6VTzjsDMuGrNtf1aui69YZiXFmvD2f+60vvHeDwaFhZtzyapJB6159w6FzrMUu8cp6ga0m5ZoysAd2/jHhyPGgUk36hS//wrlSUfp9dmXqb9EymMH9iUpAQ7VbM0bJ6BR418ln5z3DDP2gZaPJxKzxeu0qvNY+j1llns4MFtkVtmqnuYPHkyqamphikuLq7C96GxUrC1M//YxKjvnNF66Qjokm2YV5CtMWS6k8YaTLwugaimCvKtiPrDkbadbg/g1GgU2nTK4MwxdbtoLYllHieF12ZepkO3ZCYNbkLiFfMOcC3JyQPuvPpsO0b3uT2dP+XMrh99GN2nnUUX8QdZlfpKbm9vf8/7vpbFkLdiOLrHnevx9jg66ejSM4lW7dOYMsy83VeKvrCQN+ididUdfxH3evm4BOazf5oXj0xKxt5dT+wOB+J/0xLy8XWzZtQ66vCve/u0hl9ALvWaZZGeYsONeHVaCZKpdL5bWYPxi+M4/7sjkSccee6VG2gd9Wxb76lKHpDjVBqjZsXQtddNZo5oSHaGFR418gDITLchL1edNlh2lg0xF4zLRk62NWkptsRcsPyfoZX/gjCW2fatUoW8orl75TN+3gU8ffLITLfm0jknpgxrxonf3M2aI36/lsx4Gxr2zTCab2ULT628zrEF7uwY6UNBlgaXOgU89v5NAjqbd9R6o9ZZzP86yvB45IyrAGzb4MmCcXXNmkUymWb3Dx64eel4aUICHt4FRJ924J3BQaQkmfdaBHeS43R/Pf9V+GV9/vpzRvMXjA9i+7feakSq8vSKBr2JdzC7e31LpFEU9TppMzIyuHDhAgBt27Zl4cKFdO3aFU9PT+rUqXPf9dPS0nBzc+MJp0HYaNQ9d3SnsONn1I5QzKqm9dSOIMpKbxkXADFiZa12guIs8DhpKrAHsaJYOZT+ZhzmUKDksTNlLampqbi6ulbKPopqxbwjj+HgXPb2a3ZGARMf2VupWctC1Rb50aNH6dq1q+HxuHHjAAgLC2P16tUqpRJCCPEg0peza91SLwijaiHv0qULKnYICCGEqEbKf/czyyzklplKCCGEEKVSrQe7CSGEqD50aNCV46Iu5Vm3MkkhF0IIUS1I17oQQgghLI60yIUQQlQLOsrXPW55P3AsJIVcCCFEtfCgdq1LIRdCCFEtyE1ThBBCCGFxpEUuhBCiWlDKeT9yRX5+JoQQQqhHutaFEEIIYXGkRV4J1jxk3vuZl8bQs5Z3R7bVrRqpHaEYJTf3/gsJi7zTmCWyxPeTzsIy6ZR8s+3rQb2NqRRyIYQQ1YKunHc/K8+6lckyUwkhhBCiVKRFLoQQolqQrnUhhBCiCtNjhb4cHdHlWbcyWWYqIYQQooqrW7cuGo2m2DRq1CgAcnJyGDVqFF5eXjg7O9O3b18SExNN3o8UciGEENWCTtGUezLFkSNHuHbtmmHavn07AP379wdg7NixbN68ma+//prdu3cTHx9Pnz59TH5d0rUuhBCiWqioc+RpaWlG8+3t7bG3ty+2vLe3t9Hj999/n/r169O5c2dSU1P57LPPiIiI4IknngBg1apVNG3alIMHD/KPf/yj1LmkRS6EEKJaUP66+1lZJ+WvK7sFBATg5uZmmMLDw++777y8PP773/8ybNgwNBoNx44dIz8/n5CQEMMyTZo0oU6dOhw4cMCk1yUtciGEEMIEcXFxuLq6Gh6X1Bq/26ZNm0hJSWHIkCEAJCQkYGdnh7u7u9Fyvr6+JCQkmJRHCrkQQohqQYcGXTlufFK0rqurq1EhL43PPvuM7t274+/vX+b934sUciGEENWCXinfb8H1StnWi4mJYceOHXz33XeGeX5+fuTl5ZGSkmLUKk9MTMTPz8+k7cs5ciGEEKISrVq1Ch8fH3r06GGY165dO2xtbdm5c6dhXmRkJLGxsQQHB5u0/WrdIu/xQgI9BiXgW7vwJgIxUQ5EfBjA0T0e1TrT10/UIuNq8bdGkxfSCZ5+i6wbVhyd50H8fgfyMzW4BuXTemQadUOzzJZx4KvxdAxNpnb9bPJyrDhz3JnP5wZwJdrBbBnupeeQJPq9eh1P7wKizzjw0ZRaRJ50lEySSTKprGjQWnnWN3kdvZ5Vq1YRFhaGjc3tz1U3Nzdefvllxo0bh6enJ66urrz++usEBwebNGIdqnmLPCnBjlX/F8jrvVvxxnOt+P2AG9OWn6NOA/MVJEvM1PObawzcF2eYQlcVXqCg7tOZAOydVIPUS7Y8ufw6vTdfI/CpbHaNqcHNM7Zmy9iyfTqb1/owtk8zJr/UBBsbhTlfRGLvoO5duTo/m8yI6fGsW+jHqNBGRJ/RMiciGjcv893hSTJJpuqWqbT0aMo9mWrHjh3ExsYybNiwYs8tWrSIZ555hr59+/L444/j5+dn1P1eWqoW8vDwcB555BFcXFzw8fGhd+/eREZGmm3/h37x5MhuD+JjHLh62YE1iwLJybKmSZt0s2WwxExaTz2O3renuF8dcKmTj9+jhb0E10/Y0/TFdLxb5eESUECb11Kxc9Vz8/T9R25WlClDGrP9W29iohy5dNaRBRPq4Vsrj4YtM82WoSR9RiSxJcKTbV95EhulZcmk2uRmawgddEsySSbJVA1169YNRVFo1Kj4bZu1Wi3Lli3j1q1bZGZm8t1335l8fhxULuS7d+9m1KhRHDx4kO3bt5Ofn0+3bt3IzDT/h7GVlULnHkloHXWcO+li9v2XxBIy6fLg4g9ONOybgeavL6M+bXO59LMjuSlWKHqI/skRXa4Gv0dzVMkI4OhS2BJPT1HvbJGNrZ6GrbI4vvf230pRNJzY60Kzdur08kgmyfSgZzKFua/sZi6qniPfsmWL0ePVq1fj4+PDsWPHePzxx82SoW6jTBZuOIWdvZ7sLGtmv9aE2AvqnuuxpEyxOxzJS7ei4XO3v1x1WXyDXWO9iWgfgMZGwUar8MSHN3ANLFAlo0ajMHJqDKePOBNzXr2/naunDmsbSLlh/M8qOcmGgAa5kkkySSaVqXGO3BwsarBbamoqAJ6eniU+n5ubS27u7TfL3ZfJK4srlxwY9WxrnFx0dHr6Jm/Ni2Li4BaqFnNLynT+W2dqP56No+/tc88nPnAnL82K0NWJaD10xOxwZNcYb7qvS8CzsfnPk42aFUPdxtm81b+Z2fcthBBqs5ivF3q9njFjxtCxY0datGhR4jLh4eFGl8ULCAgo934L8q24FuvAhdPOrF4QSPRZJ3qFXSv3dh+ETBlXrbm2X0vDfhmGeWmxNpz9ryud3ruJf3AOnk3yaTs6Fa8WuZxbZ/7u/9dmXqb9EylMHNSUpAQ7s+//Tmm3rNEVgLu3cc+ER40Ckm+o851ZMkmmBz2TKfRoDNdbL9NUjovJVCaLKeSjRo3izz//ZP369fdcZvLkyaSmphqmuLi4Cs+hsVKwtdNX+HbLQ61MUd85o/XSEdAl2zCvIFtjyHQnjTUoZbxYQtkovDbzMh26JTNpcBMSr5hvoN29FORbEfWHI2073R6YqNEotOmUwZlj6vTwSCbJ9KBnMoVSzhHrioUWcov4CjV69Gh+/PFH9uzZQ+3ate+53L3uMFNWQ96K4eged67H2+PopKNLzyRatU9jyjD1umgtJZOiLyzkDXpnYnXHu8S9Xj4ugfnsn+bFI5OSsXfXE7vDgfjftIR8fN1s+UbNiqFrr5vMHNGQ7AwrPGrkAZCZbkNernrfT79bWYPxi+M4/7sjkSccee6VG2gd9WxbX/LpIskkmSST+VTU3c8sjaqFXFEUXn/9dTZu3MiuXbsICgoy6/7dvfIZP+8Cnj55ZKZbc+mcE1OGNePEb+5mzWGJmeL3a8mMt6Fh3wyj+Va28NTK6xxb4M6OkT4UZGlwqVPAY+/fJKCz+Uat9/xX4ZeG+evPGc1fMD6I7d96l7SKWez+wQM3Lx0vTUjAw7uA6NMOvDM4iJQk8/3GXjJJpuqWqbrTKIp5O0Tv9NprrxEREcH3339P48aNDfPd3NxwcLj/FbrS0tJwc3PjCadB2GjUPT9q6cKOn1E7QjGrWxX/XaXalFzLH3krxIOkQMlnF9+Tmppq8o1ISquoVjy3fSi2TmWvFfmZeWx8alWlZi0LVVvky5cvB6BLly5G81etWmW41ZsQQghREaRrvRKo2BkghBBCPBAsYrCbEEIIUdnKer30O9e3RFLIhRBCVAsPate6xfyOXAghhBCmkxa5EEKIauFBbZFLIRdCCFEtPKiFXLrWhRBCiCpMWuRCCCGqhQe1RS6FXAghRLWgUL6fkFnqlU+kkAshhKgWHtQWuZwjF0IIIaowaZELIYSoFh7UFvkDUcj1mVnoNflqxzDQVOA90yvKqqb11I5QzDOnEtSOUMyPzT3UjlCMdQ0vtSMUo0u6qXaEYizx353G5oH4iK1UVkoeZJpnXw9qIZeudSGEEKIKk6+LQgghqoUHtUUuhVwIIUS1oCgalHIU4/KsW5mka10IIYSowqRFLoQQolqQ+5ELIYQQVdiDeo5cutaFEEKIKkxa5EIIIaoFGewmhBBCVGFFXevlmUx19epVXnzxRby8vHBwcKBly5YcPXrU8LyiKEybNo2aNWvi4OBASEgIUVFRJu1DCrkQQohqoahFXp7JFMnJyXTs2BFbW1t+/vlnzpw5w4IFC/DwuH0FyXnz5rFkyRJWrFjBoUOHcHJyIjQ0lJycnFLvR7rWhRBCCBOkpaUZPba3t8e+hEsEz507l4CAAFatWmWYFxQUZPh/RVFYvHgxU6ZMoVevXgB88cUX+Pr6smnTJp5//vlS5ZEWuRBCiGpBKWe3elGLPCAgADc3N8MUHh5e4v5++OEHHn74Yfr374+Pjw9t27blk08+MTx/6dIlEhISCAkJMcxzc3Ojffv2HDhwoNSvS1rkQM8hSfR79Tqe3gVEn3Hgoym1iDzpqEqWga/G0zE0mdr1s8nLseLMcWc+nxvAlWgHVfIUadE+nf4jE2nYMhsvv3xmvFyPA1vdzbb/nU+5kh1vXWx+4PM5tJyazf4hztw6Ymv0XJ0BubSanmWuiAaW9H4a/Go0g1+9bDQv7pIj/+71D1Xy3MmSjpMl/rvr8UICPQYl4Fs7F4CYKAciPgzg6B71buxjiZlMoQCKUr71AeLi4nB1dTXML6k1DhAdHc3y5csZN24c//nPfzhy5AhvvPEGdnZ2hIWFkZBQeOMoX19fo/V8fX0Nz5VGtS/knZ9NZsT0eJa+XZtzxx157pUbzImI5uXHGpN60/b+G6hgLduns3mtD+f/cMLKBoaOj2POF5GMeKoludnFC5m5aB31RJ9xZOtXNZj+abTZ99/pq3QU3e3H6ResOTTchZqht+96V6dfLo1GZxseWzuU419sGVna+wng8gUn3nmljeGxTqf+yFtLO06W+O8uKcGOVf8XyNXLWjQaCHnuOtOWn2N0r9bEXlDnC48lZlKDq6urUSG/F71ez8MPP8x7770HQNu2bfnzzz9ZsWIFYWFhFZZH1a715cuX06pVK8NBCQ4O5ueffzZrhj4jktgS4cm2rzyJjdKyZFJtcrM1hA66ZdYcRaYMacz2b72JiXLk0llHFkyoh2+tPBq2NNN9/u7h6K9urJnvz/4t7qrs395TQet9e7q+yxbHAB1ejxQYlrHWGi9j62z+nJb2fgLQFWhIvmlvmNJS7FTLUsTSjpMl/rs79IsnR3Z7EB/jwNXLDqxZFEhOljVN2qRLpjIqurJbeSZT1KxZk2bNmhnNa9q0KbGxsQD4+fkBkJiYaLRMYmKi4bnSULWQ165dm/fff59jx45x9OhRnnjiCXr16sXp06fNsn8bWz0NW2VxfK+LYZ6iaDix14Vm7czfJVsSR5fCZmh6SrXvPDHQ58GVH+0I6JOH5o5/V1d/smNrRzd293Ll7CItuux7b6MyWOr7qVZgFmt37OOz/+1nQvhpvP1KPxq2MljqcbqTpf27s7JS6NwjCa2jjnMnXe6/ghlYYqb7Mfeo9Y4dOxIZGWk07/z58wQGBgKFA9/8/PzYuXOn4fm0tDQOHTpEcHBwqfej6ru0Z8+eRo/nzJnD8uXLOXjwIM2bNy+2fG5uLrm5uYbHd48cNJWrpw5rG0i5YXwYkpNsCGiQe4+1zEejURg5NYbTR5yJOV99uq3uJ+EXWwrSNQT0vv03qvXPPBz89Wh99KSdt+HcQgcyL1vz8Afma1FZ4vsp8pQbC6c048plRzy9c3lh5CXmrz7Gq33ak52lzj9/SzxOd7Kkf3d1G2WycMMp7Oz1ZGdZM/u1Jqp3YVtiJks1duxYOnTowHvvvceAAQM4fPgwK1euZOXKlQBoNBrGjBnDu+++S8OGDQkKCmLq1Kn4+/vTu3fvUu/HMr5uAjqdjq+//prMzMx7fhMJDw9n5syZZk6mnlGzYqjbOJu3+je7/8LVSNy39nh3ykfrc/sceOCAPMP/uzbKQ1tDz8GXXciMzcapjl6NmBbh6D4vw/9fjnIm8pQrq7fs57HQ62zb6K9iMstlSf/urlxyYNSzrXFy0dHp6Zu8NS+KiYNbqFo4LTFTaekVDRozXmv9kUceYePGjUyePJlZs2YRFBTE4sWLGTx4sGGZiRMnkpmZyYgRI0hJSaFTp05s2bIFrVZb6v2oXshPnTpFcHAwOTk5ODs7s3HjxmLnFIpMnjyZcePGGR6npaUREBBQ5n2n3bJGVwDu3gVG8z1qFJB8Q91D89rMy7R/IoXxA5uSlKD+OU1LkRVvxY2DNvdtabu3KvybZsZama2QW/L7qUhmui1XYxzxDzDzeYc7WPJxsrR/dwX5VlyLLRw5f+G0M41aZtAr7BpLp9aXTGWgKOUctV6GdZ955hmeeeaZez6v0WiYNWsWs2bNKnMu1X9H3rhxY06ePMmhQ4d49dVXCQsL48yZMyUua29vbxgYV9pRg3+nIN+KqD8cadvp9kANjUahTacMzhxT69ulwmszL9OhWzKTBjch8UrJP2uoruI22mHvqeDzeP7fLpd2rnCksdbbfCPXLfP9ZEzrUEDNgGxuJalXpCzzOFWNf3caKwVbO8vqYbLETNWN6s0EOzs7GjRoAEC7du04cuQIH3zwAR9//LFZ9v/dyhqMXxzH+d8diTxR+DMYraOebes9zbL/u42aFUPXXjeZOaIh2RlWeNQo7DLOTLchL1e9711aRx3+dW+fv/QLyKVesyzSU2y4EW+eoqDo4cpGO2r3ysPqjnduZqwVV3+yw+fxfOzcFdIirTkzzwHPh/Nxbay79wYrgaW9n15+K4pDu2pw/ZoWL+88XnwtGr1Ow66ffe+/ciWytONkif/uhrwVw9E97lyPt8fRSUeXnkm0ap/GlGHqdflbYiZTPKg3TVG9kN9Nr9cbDWirbLt/8MDNS8dLExLw8C4g+rQD7wwOIiVJnd/89vzXdQDmrz9nNH/B+CC2f+utRiQAGrXOYv7Xty/kP3LGVQC2bfBkwbi6ZsmQdMCG7GvWBPTJMJpvZauQdNCGS2vt0WVr0Prp8QvJp+FI83cfW9r7qYZPLpPmnsbVPZ/UZDtOH3dj7IvtSEtWt9vY0o6TJf67c/fKZ/y8C3j65JGZbs2lc05MGdaME7+5q5LHUjOZ4kEt5BpFKc8Zg/KZPHky3bt3p06dOqSnpxMREcHcuXPZunUrTz311H3XT0tLw83NjS70wkajzgdASTT3uMqPmpT8gvsvZGbPnEpSO0IxPza3vCtUWdfwuv9CZqZLuql2hGIs8d+dxsbi2koWp0DJ45fML0lNTS336dJ7KaoVjSPextqx7O8TXVYukS+8X6lZy0LVd9n169d56aWXuHbtGm5ubrRq1arURVwIIYQQKhfyzz77TM3dCyGEqEbUGLVuDtLvI4QQolooLOTlOUdegWEqkOo/PxNCCCFE2UmLXAghRLXwoI5al0IuhBCiWlC4fU/xsq5viaRrXQghhKjCpEUuhBCiWpCudSGEEKIqe0D71qWQCyGEqB7K2SLHQlvkco5cCCGEqMKkRS6EEKJakCu7CSGEEFWYDHazZFbWoLFWO4WBYsbbsFZllninsckX/1A7QjHh9VupHUE8QDQOWrUjGNHorSBT7RRV24NRyIUQQoj7UTTlG7AmLXIhhBBCPQ/qOXIZtS6EEEJUYdIiF0IIUT3IBWGEEEKIqqtaj1r/4YcfSr3BZ599tsxhhBBCCGGaUhXy3r17l2pjGo0GnU5XnjxCCCFE5bHQ7vHyKFUh1+v1lZ1DCCGEqFQPatd6uUat5+TkVFQOIYQQonIpFTBZIJMLuU6nY/bs2dSqVQtnZ2eio6MBmDp1Kp999lmFBxRCCCHEvZlcyOfMmcPq1auZN28ednZ2hvktWrTg008/rdBwQgghRMXRVMBkeUwu5F988QUrV65k8ODBWFvfvr5569atOXfuXIWGE0IIISrMA9q1bvLvyK9evUqDBg2Kzdfr9eTn51dIKHNq0T6d/iMTadgyGy+/fGa8XI8DW93VjkXPIUn0e/U6nt4FRJ9x4KMptYg86SiZLCiTXgd7P/Dl9PceZN6wwdk3n5Z9kuk4+jqav764R2515XiEFwl/OpCTYsOwzefxbWb+sSXyt/t7A1+Np2NoMrXrZ5OXY8WZ4858PjeAK9EOquQB6PFCAj0GJeBbu/AmTDFRDkR8GMDRPerdbGjwq9EMfvWy0by4S478u9c/1Alk4WbMmMHMmTON5jVu3NjQ6M3JyeGtt95i/fr15ObmEhoaykcffYSvr69J+zG5Rd6sWTP27t1bbP4333xD27ZtTd2c6rSOeqLPOPLhlAC1oxh0fjaZEdPjWbfQj1GhjYg+o2VORDRuXup9UZJMxR382JsTEV50m3GVV7ZF0nViAoc+8eboGi/DMnlZVgQ8nEnXiQlmyVQStY9TVcjUsn06m9f6MLZPMya/1AQbG4U5X0Ri76Dez2mTEuxY9X+BvN67FW8814rfD7gxbfk56jTIUi0TwOULTgzu2tEwTQh7SNU8JlGhRd68eXOuXbtmmPbt22d4buzYsWzevJmvv/6a3bt3Ex8fT58+fUzeh8mFfNq0aYwePZq5c+ei1+v57rvveOWVV5gzZw7Tpk0zOUCR999/H41Gw5gxY8q8jbI4+qsba+b7s3+Lu1n3+3f6jEhiS4Qn277yJDZKy5JJtcnN1hA66JZksqBMV4470TAkjQZd03GvnU+T7qkEdcrg2h+3W5Utn0uh0+vXqdsx3SyZSqL2caoKmaYMacz2b72JiXLk0llHFkyoh2+tPBq2VO/+mod+8eTIbg/iYxy4etmBNYsCycmypkkb9d5LALoCDck37Q1TWord/VeyFEV3PyvPBKSlpRlNuX9z62obGxv8/PwMU40aNQBITU3ls88+Y+HChTzxxBO0a9eOVatWsX//fg4ePGjSyzK5kPfq1YvNmzezY8cOnJycmDZtGmfPnmXz5s089dRTpm4OgCNHjvDxxx/TqpXcd9nGVk/DVlkc3+timKcoGk7sdaFZO3W+iUumktV+KJOY/c7cvFT4QZZ4VkvcUUfqdVb3g/ZOlnCcqkKmuzm6FLbE01Ms4yrWVlYKnXskoXXUce6ky/1XqES1ArNYu2Mfn/1vPxPCT+PtV/1+hhwQEICbm5thCg8Pv+eyUVFR+Pv7U69ePQYPHkxsbCwAx44dIz8/n5CQEMOyTZo0oU6dOhw4cMCkPGV6lz722GNs3769LKsWk5GRweDBg/nkk0949913/3bZ3Nxco28+aWlpFZLBkrh66rC2gZQbxn+a5CQbAhrc+1ufZDJ/puCRN8jNsGblU42xsi48Z975rQRa9Eoxy/5LwxKOU1XIdCeNRmHk1BhOH3Em5ry64wjqNspk4YZT2Nnryc6yZvZrTYi9oF6myFNuLJzSjCuXHfH0zuWFkZeYv/oYr/ZpT3aWZXzp+TsVdRvTuLg4XF1dDfPt7e1LXL59+/asXr2axo0bc+3aNWbOnMljjz3Gn3/+SUJCAnZ2dri7uxut4+vrS0KCaafiynzkjx49ytmzZ4HC8+bt2rUr03ZGjRpFjx49CAkJuW8hDw8PLzZwQAi1nP3JjdPfu9NrUSw1GuWSeEbLjnf9cfYpoFXfZLXjiTIaNSuGuo2zeat/M7WjcOWSA6OebY2Ti45OT9/krXlRTBzcQrVifnTf7fEfl6OciTzlyuot+3ks9DrbNvqrkskkFXT3M1dXV6NCfi/du3c3/H+rVq1o3749gYGBbNiwAQeHihtIaXIhv3LlCoMGDeK3334zfJNISUmhQ4cOrF+/ntq1a5d6W+vXr+f48eMcOXKkVMtPnjyZcePGGR6npaUREGA5g9QqQtota3QF4O5dYDTfo0YByTfU+cYrmUr2y/s1CR55g2Y9UwHwaZxD2lU7DqzwtphCbgnHqSpkKvLazMu0fyKF8QObkpSg/rnfgnwrrsUWfuBfOO1Mo5YZ9Aq7xtKp9VVOVigz3ZarMY74B2SrHaVKcHd3p1GjRly4cIGnnnqKvLw8UlJSjFrliYmJ+Pn5mbRdk8+RDx8+nPz8fM6ePcutW7e4desWZ8+eRa/XM3z48FJvJy4ujjfffJN169ah1WpLtY69vb3hm1BpvxFVNQX5VkT94UjbTrfPs2o0Cm06ZXDmmDrfwiVTyfJzrNBYGX+911grKHrLuWiEJRynqpAJFF6beZkO3ZKZNLgJiVdK7ipVm8ZKwdbOcu59oXUooGZANreS1P/SUyoVNNitrDIyMrh48SI1a9akXbt22NrasnPnTsPzkZGRxMbGEhwcbNJ2Tf76u3v3bvbv30/jxo0N8xo3bszSpUt57LHHSr2dY8eOcf36dR566PZPF3Q6HXv27OHDDz8kNzfX6IIzlUXrqMO/7u3zcn4BudRrlkV6ig034tV5c363sgbjF8dx/ndHIk848twrN9A66tm23lOVPJKpZA2fSGP/Rz64+udTo2EOiacdOPy5N6373R55nZ1iTVq8LemJtgDcjC4sEE7eBTjf1SKtLGofp6qQadSsGLr2usnMEQ3JzrDCo0YeAJnpNuTlluuWFGU25K0Yju5x53q8PY5OOrr0TKJV+zSmDFOvy//lt6I4tKsG169p8fLO48XXotHrNOz62bTfPatFoxRO5VnfFOPHj6dnz54EBgYSHx/P9OnTsba2ZtCgQbi5ufHyyy8zbtw4PD09cXV15fXXXyc4OJh//MO03+WbXMgDAgJKvPCLTqfD37/050iefPJJTp06ZTRv6NChNGnShEmTJpmliAM0ap3F/K+jDI9HzrgKwLYNniwYV9csGe62+wcP3Lx0vDQhAQ/vAqJPO/DO4CBSkmxVySOZSvbU9Hj2LPJl67RaZN0svCBM2+dv0un164Zlona48tOk26d/vn8zEIBObyTy2JuJZsmp9nGqCpl6/qvwbzZ/vfHVKReMD2L7t95qRMLdK5/x8y7g6ZNHZro1l845MWVYM0785q5KHoAaPrlMmnsaV/d8UpPtOH3cjbEvtiMtuaq0yKmQc+SlVXQq+ubNm3h7e9OpUycOHjyIt3fhe2rRokVYWVnRt29fowvCmEqjKKaN4fv+++957733WLZsGQ8//DBQOPDt9ddfZ9KkSaW+d3lJunTpQps2bVi8eHGplk9LS8PNzY0uVn2w0aj3oVSMXu7JXlVNvviH2hGKCa8vP8ssDc09Rg6rSWNjeSO5NQ6lO5VpLgX6PHbeXEVqamqlnS4tqhUBi2dhVY7Xr8/OIW7MtErNWhalepd5eHig0dw+N5CZmUn79u2x+etNWlBQgI2NDcOGDStXIRdCCCEqTXnPc1vo/chLVchL20Iur127dpllP0IIIaohM3etm0upCnlYWFhl5xBCCCFEGZTrBE5OTg55eXlG8yzpvIEQQghh8IC2yE3+XUVmZiajR4/Gx8cHJycnPDw8jCYhhBDCIj2g9yM3uZBPnDiRX375heXLl2Nvb8+nn37KzJkz8ff354svvqiMjEIIIYS4B5O71jdv3swXX3xBly5dGDp0KI899hgNGjQgMDCQdevWMXjw4MrIKYQQQpTPAzpq3eQW+a1bt6hXrx5QeD781q3Cq1h16tSJPXv2VGw6IYQQooIUXdmtPJMlMrmQ16tXj0uXLgGF907dsGEDUNhSv/t2bEIIIYSoXCYX8qFDh/L7778D8Pbbb7Ns2TK0Wi1jx45lwoQJFR5QCCGEqBAP6GA3k8+Rjx071vD/ISEhnDt3jmPHjtGgQQNatZJLSQohhBDmVO4LAQcGBhIYGFgRWYQQQohKo6Gcdz+rsCQVq1SFfMmSJaXe4BtvvFHmMEIIIYQwTakK+aJFi0q1MY1Go04h1+tAo849g0U5WJnnVrWmsMQ7jc29dEjtCMVMqt9B7QjFKLm5akcoxhIz2bg4qx3BiEavN9/OHtCfn5WqkBeNUhdCCCGqLLlEqxBCCCEsjeXd9V4IIYSoDA9oi1wKuRBCiGqhvFdne2Cu7CaEEEIIyyEtciGEENXDA9q1XqYW+d69e3nxxRcJDg7m6tWrAKxdu5Z9+/ZVaDghhBCiwjygl2g1uZB/++23hIaG4uDgwIkTJ8j963eSqampvPfeexUeUAghhBD3ZnIhf/fdd1mxYgWffPIJtra2hvkdO3bk+PHjFRpOCCGEqCgP6m1MTT5HHhkZyeOPP15svpubGykpKRWRSQghhKh4D+iV3Uxukfv5+XHhwoVi8/ft20e9evUqJJQQQghR4eQceaFXXnmFN998k0OHDqHRaIiPj2fdunWMHz+eV199tTIyCiGEEOIeTO5af/vtt9Hr9Tz55JNkZWXx+OOPY29vz/jx43n99dcrI2Ol6zkkiX6vXsfTu4DoMw58NKUWkScdJZOFZ2rRPp3+IxNp2DIbL798ZrxcjwNb3VXLU0TN46TXwfbFtTmxyYv0G3a4+ubRru8Nnnw9Hs1fvYLbF9fi981epFyzw8ZWoVbLTELfiqNO20yzZAT521XlTF7eOQx9M4p2HZKw1+q4FufIohnNuXDWTbVMpSUXhPmLRqPhnXfe4datW/z5558cPHiQGzduMHv27MrIV+k6P5vMiOnxrFvox6jQRkSf0TInIho3r3zJZOGZtI56os848uGUANUy3E3t47RrhT8H1/nQa2YMb+34ne6T4ti90p/9q30Ny9QIyqHXzMuM3XKKkV+fwaNWLp+GNSHjpvkuKyF/u6qZydkln/mrDlNQoGH66w/xar8OfLqoERnptvdf2RJI17oxOzs7mjVrxqOPPoqzc9luizdjxgw0Go3R1KRJk7JGKpM+I5LYEuHJtq88iY3SsmRSbXKzNYQOumXWHJLJdEd/dWPNfH/2b3FXLcPd1D5OMcedafZUMk2fSMGzdh6t/nmLRo+lEvf77X+jbXvdpGGnNLzq5OLXKJtnpsSSm25DwjnztfLkb1c1M/UbcokbiVoWz2jB+dNuJMY7cuJgDRKuqNtrUd2Z/BW8a9euaDT3Hrn3yy+/mLS95s2bs2PHjtuBbMzXKrCx1dOwVRbrP/QxzFMUDSf2utCsXZbZckimB4MlHKfAhzI4/KUPN6K1eNfLIf6MI5ePuPDMlJgSly/I03DoS2+0LgXUbFp9/5aW8LerCpnad77B8QNeTJ77Oy3a3eLmdS0/fR3A1o21VcljsvL+hMxCW+QmV802bdoYPc7Pz+fkyZP8+eefhIWFmR7AxgY/P79SLZubm2u4AA1AWlqayfu7k6unDmsbSLlhfBiSk2wIaJB7j7Uql2SquizhOHV5NZ7cDGsWhLRCY62g6DSEjr9C2943jZY7u9OdiDcakJ9thYtPPsPXnsPJs8AsGS2RJfztqkImv1rZ/LPfFTauC+Srz4No1DyNf084R0G+hp0/1lIlk0lUvETr+++/z+TJk3nzzTdZvHgxADk5Obz11lusX7+e3NxcQkND+eijj/D19f37jd3F5EK+aNGiEufPmDGDjIwMUzdHVFQU/v7+aLVagoODCQ8Pp06dOiUuGx4ezsyZM03ehxDVxR8/eXLiey+e/+ACvg2zuXbGic2z6/w16C3JsFz94DTe/OkUmcm2HF7vzbrRDRi98TTONapvMRf3p7FSuHDGlS8+bAhAdKQrgfUz6N7vStUo5Co5cuQIH3/8Ma1atTKaP3bsWH766Se+/vpr3NzcGD16NH369OG3334zafsVdvezF198kc8//9ykddq3b8/q1avZsmULy5cv59KlSzz22GOkp6eXuPzkyZNJTU01THFxceXKnHbLGl0BuHsbf3h51Cgg+YY695ORTFWXJRyn/4XXocvIa7TpeYuaTbJ5qE8SnYYl8OtH/kbL2TnqqVE3l8C2GfSfewkrGziyweceW33wWcLfripkSk6yJzbaeExU3CUnvP1yVMljsgoa7JaWlmY03dlTfLeMjAwGDx7MJ598goeHh2F+amoqn332GQsXLuSJJ56gXbt2rFq1iv3793Pw4EGTXlaFFfIDBw6g1WpNWqd79+7079+fVq1aERoayv/+9z9SUlLYsGFDicvb29vj6upqNJVHQb4VUX840rbT7S8OGo1Cm04ZnDmmzuANyVR1WcJxys+2QmNl3P9nZQ2K/u/XU/SF58urK0v421WFTGdOulOrrvHPFGsFZnLjmmmf/WqpqEu0BgQE4ObmZpjCw8Pvuc9Ro0bRo0cPQkJCjOYfO3aM/Px8o/lNmjShTp06HDhwwKTXZfLXuj59+hg9VhSFa9eucfToUaZOnWrq5oy4u7vTqFGjEq8cV1m+W1mD8YvjOP+7I5EnHHnulRtoHfVsW+9ptgySqWy0jjr8697+JuwXkEu9Zlmkp9hwI95OlUxqH6emT6bwy7JauPvn4dsoi/jTTuz9zI+H+98AIC/Lil+W+dM0JAVX7zwyk204sNaXtAQ7Wv7TfCOh5W9XNTNtWhfI/606zIBh0ezd7kej5qk83ecKS99trkoetcTFxRk1JO3t7Utcbv369Rw/fpwjR44Uey4hIQE7Ozvc3d2N5vv6+pKQkGBSHpMLuZub8Y/+raysaNy4MbNmzaJbt26mbs5IRkYGFy9e5F//+le5tmOK3T944Oal46UJCXh4FxB92oF3BgeRkqTe7yIlU+k0ap3F/K+jDI9Hzii8pe62DZ4sGFdXlUxqH6deMy6zdWFtNk2tS8ZNW1x982g/6DpPvlF4bDTWCtcvOnDsW28yk21wdC8goFUmIzecwa9RtlkygvztqmqmqDNuvDu+DUNGRzHolWgS4x1Y+X9N2PVzTVXyqKU0PcJxcXG8+eabbN++3eTealNpFEUp9Tg8nU7Hb7/9RsuWLY36+stq/Pjx9OzZk8DAQOLj45k+fTonT57kzJkzeHt733f9tLQ03Nzc6EIvbDRV5IIE4jYra7UTFKfXqZ2gmLmXDqkdoZhJ9TuoHaE4C/zbWSIbP9NGRFe2An0eOxI/ITU1tdynS++lqFbUn/we1uUoqrqcHC6G/6dUWTdt2sRzzz2HtfXtzzmdTodGo8HKyoqtW7cSEhJCcnKyUas8MDCQMWPGMHbs2FLnMqlFbm1tTbdu3Th79myFFPIrV64waNAgbt68ibe3N506deLgwYOlKuJCCCGEKcx5idYnn3ySU6dOGc0bOnQoTZo0YdKkSQQEBGBra8vOnTvp27cvUHh30djYWIKDg03KZXLXeosWLYiOjiYoKMjUVYtZv359ubchhBBCWBoXFxdatGhhNM/JyQkvLy/D/Jdffplx48bh6emJq6srr7/+OsHBwfzjH/8waV8mF/J3332X8ePHM3v2bNq1a4eTk5PR85XVNSKEEEKUmwVdnW3RokVYWVnRt29fowvCmKrUhXzWrFm89dZb/POf/wTg2WefNbpUq6IoaDQadDo5TyWEEMICqXhlN4Bdu3YZPdZqtSxbtoxly5aVa7ulLuQzZ85k5MiR/Prrr+XaoRBCCCEqTqkLedHg9s6dO1daGCGEEKKyPKj3IzfpHPnf3fVMCCGEsGgqd61XFpMKeaNGje5bzG/dUu/evUIIIUR1Y1IhnzlzZrEruwkhhBBVgXStA88//zw+PtX3DklCCCGqsAe0a73Udz+T8+NCCCGE5TF51LoQQghRJT2gLfJSF3K9/j43NBZCCCEsmJwjF1Wb3GmsypoU1F7tCMVsjT+mdoRiQv3bqB2hSihISFQ7gpECJd98O3tAW+SlPkcuhBBCCMsjLXIhhBDVwwPaIpdCLoQQolp4UM+RS9e6EEIIUYVJi1wIIUT1IF3rQgghRNUlXetCCCGEsDjSIhdCCFE9SNe6EEIIUYU9oIVcutaFEEKIKkxa5EIIIaoFzV9Teda3RFLIhRBCVA/Stf7g6jkkiTWHzrA5+g8++DGKxm2y1I5kcZlatE9n5qoLRBw9xdYrxwkOTVE1TxFLO06SqbisDCuWT6vFvx5pRs96rRjTsyGRJx0MzysKrJnnx6A2zelZrxWTBtTnarSd2fLdSf52VTdTaRT9/Kw8kyWq9oW887PJjJgez7qFfowKbUT0GS1zIqJx8zLjHXmqQCato57oM458OCVAtQx3s8TjJJmKW/RWAMf3ODNxaQwrdp6jXed03h7YgKRrtgBsWObD95978/r7cXzw43m0jnr+80J98nLM25Gp9nGSTKKsVC/kV69e5cUXX8TLywsHBwdatmzJ0aNHzbb/PiOS2BLhybavPImN0rJkUm1yszWEDrpltgxVIdPRX91YM9+f/VvcVctwN0s8TpLJWG62hn3/c2f4lGu0/EcmtYLy+Nf4BPzr5vLjF14oCmz61JtBbybQ4ek06jXLYeKSGG4m2rJ/i1ul57uT/O2qbqZSUypgskCqFvLk5GQ6duyIra0tP//8M2fOnGHBggV4eHiYZf82tnoatsri+F4XwzxF0XBirwvN2qnTVWSJmSyRJR4nyVScTqdBr9NgZ683mm+v1XP6sDMJsXbcum7LQ49lGJ5zctXTpG0WZ485VXq+ImofJ8lkRg9YEQeVB7vNnTuXgIAAVq1aZZgXFBR0z+Vzc3PJzc01PE5LSyvX/l09dVjbQMoN48OQnGRDQIPce6xVuSwxkyWyxOMkmYpzdNbTtF0mEYv9qNPwMu7eBeza5MHZY074183l1vXCXO7ext2y7t75hufMQe3jJJlEeajaIv/hhx94+OGH6d+/Pz4+PrRt25ZPPvnknsuHh4fj5uZmmAICLOd8rRCiZBOXxqAo8MJDLXimbms2fVaDLr2T0ah+Yk9UNzLYrRJER0ezfPlyGjZsyNatW3n11Vd54403WLNmTYnLT548mdTUVMMUFxdXrv2n3bJGVwDu3gVG8z1qFJB8Q53OCkvMZIks8ThJppL5183j/767wPcX/uC/R0+z9H9RFORrqBmYi6dPYa6UG7ZG66TcsDU8Zw6WcJwkkxnIOfKKp9freeihh3jvvfdo27YtI0aM4JVXXmHFihUlLm9vb4+rq6vRVB4F+VZE/eFI207phnkajUKbThmcOeZYrm0/SJkskSUeJ8n097SOerx8C0hPsebYbleCQ9Pwq5OHp08+J/Y5G5bLTLfi3AlHmrbLNFs2SzpOkkmYStVCXrNmTZo1a2Y0r2nTpsTGxpotw3cra9D9hVuE9L9FQIMcXn//ClpHPdvWe5otQ1XIpHXUUa9ZFvWaFQ5o8QvIpV6zLLz981TLZInHSTIVd3SXC0d+dSEh1o5ju52Z2K8BAQ1y6DbwJhoN9B5+gy8/8OXAVlcundUy/41AvHzz6fB0qlnyFVH7OEmmymfurvXly5fTqlUrQ8MzODiYn3/+2fB8Tk4Oo0aNwsvLC2dnZ/r27UtiYqLJr0vVvpCOHTsSGRlpNO/8+fMEBgaaLcPuHzxw89Lx0oQEPLwLiD7twDuDg0hJsr3/ytUoU6PWWcz/OsrweOSMqwBs2+DJgnF1VclkicdJMhWXmWbNqvCaJF2zxcVdR8d/pjD07WvY/LX7AaOuk5NlxQcTA8hIs6b5I5nMWReNnda8/ZhqHyfJZAZmvrJb7dq1ef/992nYsCGKorBmzRp69erFiRMnaN68OWPHjuWnn37i66+/xs3NjdGjR9OnTx9+++03k/ajURRFtV7/I0eO0KFDB2bOnMmAAQM4fPgwr7zyCitXrmTw4MH3XT8tLQ03Nze60AsbTRV4E6nJylrtBMXpdWonEGW0Nf6k2hGKCfVvo3YEUQYFSj67+J7U1NRyny69l6Ja0fLl97C205Z5O7q8HE599p9yZfX09GT+/Pn069cPb29vIiIi6NevHwDnzp2jadOmHDhwgH/84x+l3qaqXeuPPPIIGzdu5Msvv6RFixbMnj2bxYsXl6qICyGEEKaoqK71tLQ0o+nOn0Xfi06nY/369WRmZhIcHMyxY8fIz88nJCTEsEyTJk2oU6cOBw4cMOl1qT7M8JlnnuGZZ55RO4YQQogHXQV1rd/90+fp06czY8aMElc5deoUwcHB5OTk4OzszMaNG2nWrBknT57Ezs4Od3d3o+V9fX1JSEgwKZbqhVwIIYQwiwoq5HFxcUZd6/b29vdcpXHjxpw8eZLU1FS++eYbwsLC2L17dzlCFCeFXAghhDCBKT9/trOzo0GDBgC0a9eOI0eO8MEHHzBw4EDy8vJISUkxapUnJibi5+dnUh65tpIQQohqwRKu7KbX68nNzaVdu3bY2tqyc+dOw3ORkZHExsYSHBxs0jalRS6EEKJ6MPPPzyZPnkz37t2pU6cO6enpREREsGvXLrZu3Yqbmxsvv/wy48aNw9PTE1dXV15//XWCg4NNGrEOUsiFEEKISnH9+nVeeuklrl27hpubG61atWLr1q089dRTACxatAgrKyv69u1Lbm4uoaGhfPTRRybvRwq5EEKIakGjKGjKcekUU9f97LPP/vZ5rVbLsmXLWLZsWZkzgRRyIYQQ1YWZu9bNRQa7CSGEEFWYtMiFEEJUC+UdeW6p9yOXQi6EEKJ6kK51IYQQQlgaaZFXF5Z4pzELvCObxlb+SZSGJd5pbGb0MbUjFDO9waNqR7B8ih705tmVdK0LIYQQVdkD2rUuhVwIIUS18KC2yOUcuRBCCFGFSYtcCCFE9SBd60IIIUTVZqnd4+UhXetCCCFEFSYtciGEENWDohRO5VnfAkkhF0IIUS3IqHUhhBBCWBxpkQshhKgeZNS6EEIIUXVp9IVTeda3RNK1LoQQQlRh0iIHeg5Jot+r1/H0LiD6jAMfTalF5ElHyWThmVq0T6f/yEQatszGyy+fGS/X48BWd9XyDHw1no6hydSun01ejhVnjjvz+dwArkQ7SKYSqPl+0uvg1w/8+WOTJxk3bHHxzadN3yQ6j05AowFdPuxcUIuoXW4kx9mhddFRr2M6IROv4uqbb5aMlvb+ttRMJnlAu9arfYu887PJjJgez7qFfowKbUT0GS1zIqJx8zLPP1bJVHZaRz3RZxz5cEqAahnu1LJ9OpvX+jC2TzMmv9QEGxuFOV9EYu+g3p3nLDETqP9+2rfCj6PrvOkxI5bR20/z1MQr/LbSj0NrvAHIz7bi2mlHOr9+jZGbzzJweTRJ0Vq+fKW+WfKB5b2/wTIzmaJo1Hp5JkukaiGvW7cuGo2m2DRq1CizZegzIoktEZ5s+8qT2CgtSybVJjdbQ+igW2bLIJnK5uivbqyZ78/+Le6qZbjTlCGN2f6tNzFRjlw668iCCfXwrZVHw5aZkukuar+f4o470TgkhUZPpOFRO4/m/0yhfqc0rv7uBIDWVU/Y2iha9EimRr1cAtpm0mNGLPF/OpFy1dYsGS3t/Q2WmckkRb8jL89kgVQt5EeOHOHatWuGafv27QD079/fLPu3sdXTsFUWx/e6GOYpioYTe11o1i7LLBkk04PL0aWw1ZueYjlnsCwhkyW8nwIeyuTSfheSou0BSDjrQOxRZxp2TrvnOjnp1mg0ClpXdXszhLibqp8w3t7eRo/ff/996tevT+fOnUtcPjc3l9zcXMPjtLR7/6MrDVdPHdY2kHLD+DAkJ9kQ0CD3HmtVLsn0YNBoFEZOjeH0EWdizqs7tqGIpWSyhPdTp1cTyM2w5sOnmqOxBkUHT7wVT6veJfcI5Odq2D6vFi163kLrYqFDl8V9PagXhLGYpkJeXh7//e9/GTduHBqNpsRlwsPDmTlzppmTCWG6UbNiqNs4m7f6N1M7ioElZlLL6Z88+OMHT/ouvoRPw2wSzjry8+wAXH3zaNPXuJjr8uHr0fVA0fDM7FiVEosKIYPdKtemTZtISUlhyJAh91xm8uTJpKamGqa4uLhy7TPtljW6AnD3LjCa71GjgOQb6nzHkUxV32szL9P+iRQmDmpKUoKd2nEAy8pkCe+nbe/XptO/E2jZMxnfJjm0fu4WwcMS2bu8ptFyunzY8Ho9Uq7a8dIX56U1LiySxRTyzz77jO7du+Pv73/PZezt7XF1dTWayqMg34qoPxxp2yndME+jUWjTKYMzx9TpepRMVZnCazMv06FbMpMGNyHxir3agbDETJbwfsrPtkJjZdy80liBckedLirity5rCVsbhaOHnBuv6h7UUesW0ZyKiYlhx44dfPfdd2bf93crazB+cRznf3ck8oQjz71yA62jnm3rPc2eRTKZRuuow7/u7XOqfgG51GuWRXqKDTfizd/qHDUrhq69bjJzREOyM6zwqJEHQGa6DXm56nxntsRMoP77qfGTKez9qCbu/nl4N8oh4bQjBz73oW2/m0BhEf9qVH2unXZk8KcX0Osh/a/eAgc3HTZ2lf+Jbmnvb0vNZBK5+1nlWbVqFT4+PvTo0cPs+979gwduXjpempCAh3cB0acdeGdwEClJ5vmJiWQqu0ats5j/dZTh8cgZVwHYtsGTBePqmj1Pz39dB2D++nNG8xeMD2L7t94lrVLpLDETqP9++uf0OH5Z6M+P0+qQebPwgjAPD0qi8+vXAEhLtCNyhzsAy3sYjykYEhFJ0D8yKj2jpb2/LTWTAI2iqPsVQ6/XExQUxKBBg3j//fdNWjctLQ03Nze60AsbjXoFRZSRlbXaCYrR2FrEd1uLp+Ra3q8VZkYfUztCMdMbPKp2BItXoOSzS/8dqamp5T5dei9FtSK4+yxsbLVl3k5Bfg4Hfp5WqVnLQvVPrR07dhAbG8uwYcPUjiKEEOJBJqPWK0e3bt1QFIVGjRqpHUUIIYSoMOHh4TzyyCO4uLjg4+ND7969iYyMNFomJyeHUaNG4eXlhbOzM3379iUxMdGk/aheyIUQQghzMPeo9d27dzNq1CgOHjzI9u3byc/Pp1u3bmRm3r5E8tixY9m8eTNff/01u3fvJj4+nj59+pi0H9W71oUQQgiz0CuFU3nWN8GWLVuMHq9evRofHx+OHTvG448/TmpqKp999hkRERE88cQTQOHg76ZNm3Lw4EH+8Y9/lGo/0iIXQghRPSgVMFE4eO7OKbeUgz9TU1MB8PQs/JnlsWPHyM/PJyQkxLBMkyZNqFOnDgcOHCj1y5JCLoQQQpggICAANzc3wxQeHn7fdfR6PWPGjKFjx460aNECgISEBOzs7HB3dzda1tfXl4SEhFLnka51IYQQ1YKGct405a//xsXFGf38zN7+/ldMHDVqFH/++Sf79u0re4B7kEIuhBCieqigK7uZeonw0aNH8+OPP7Jnzx5q165tmO/n50deXh4pKSlGrfLExET8/PxKvX3pWhdCCCEqgaIojB49mo0bN/LLL78QFBRk9Hy7du2wtbVl586dhnmRkZHExsYSHBxc6v1Ii1wIIUS1YO77kY8aNYqIiAi+//57XFxcDOe93dzccHBwwM3NjZdffplx48bh6emJq6srr7/+OsHBwaUesQ5SyIUQQlQXZr6y2/LlywHo0qWL0fxVq1YZbtm9aNEirKys6Nu3L7m5uYSGhvLRRx+ZtB8p5EIIIUQlKM2tTLRaLcuWLWPZsmVl3o8UciGEENWCRlHQlGOwW3nWrUxSyCuBlZOT2hGK0WfnqB2hGEu805gl3tXLxs9X7QjFKDmW936yyDuNba+pdoJibIZp7r+QOelzIdZc+/prKs/6FkhGrQshhBBVmOU1iYQQQohKIF3rQgghRFX2gN6PXAq5EEKI6qGCruxmaeQcuRBCCFGFSYtcCCFEtWDuK7uZixRyIYQQ1YN0rQshhBDC0kiLXAghRLWg0RdO5VnfEkkhF0IIUT1I17oQQgghLI20yIUQQlQPckGYB1fPIUn0e/U6nt4FRJ9x4KMptYg86ahKlh4vJNBjUAK+tQtv3hET5UDEhwEc3eOhSp4iLdqn039kIg1bZuPll8+Ml+txYKu7ankGvhpPx9BkatfPJi/HijPHnfl8bgBXoh1Uy1TEkt5PAF7eOQx9M4p2HZKw1+q4FufIohnNuXDWTbVMd+o/PJah4y6z6YtarHy/vioZLOb9fUMHn6TC4RzI1UMtG5jgCY3tCp9fkwq/ZhcuZwM0soNhrtDU3izxrKwUXhh+nq6hV/DwyuXWDS07/leb9asaAhZ2M5YSPKiXaK32Xeudn01mxPR41i30Y1RoI6LPaJkTEY2bV74qeZIS7Fj1f4G83rsVbzzXit8PuDFt+TnqNMhSJU8RraOe6DOOfDglQNUcRVq2T2fzWh/G9mnG5JeaYGOjMOeLSOwddKrmsrT3k7NLPvNXHaagQMP01x/i1X4d+HRRIzLSbVXJc7eGLdLpPuAa0efUvWOgRby/0/Xw5vXCAv1+DfjcD0a6g8sdH9O1beF1d/jEFz7wAV9rmJQEKeZ53/f71wX++dxlVixowcjnu7Dqoyb0HXyRnv0vm2X/omSqFnKdTsfUqVMJCgrCwcGB+vXrM3v27FLdjL2i9BmRxJYIT7Z95UlslJYlk2qTm60hdNAts2W406FfPDmy24P4GAeuXnZgzaJAcrKsadImXZU8RY7+6saa+f7s3+Kuao4iU4Y0Zvu33sREOXLprCMLJtTDt1YeDVtmqprL0t5P/YZc4kailsUzWnD+tBuJ8Y6cOFiDhCvq9RAU0TrqmDjvHEumNyIjTd3OQYt4f69PB29rmOgJTeygpg08rAX/O47Nk47Q7q95dW3hVXfIVCDaPF8Um7ZM5tBeP47s9+V6giO//erPicPeNG6WYpb9l1vRYLfyTBZI1UI+d+5cli9fzocffsjZs2eZO3cu8+bNY+nSpWbZv42tnoatsji+18UwT1E0nNjrQrN26raAobAbq3OPJLSOOs6ddLn/CtWYo0thiyQ9Rb2CYInvp/adb3DhjCuT5/7Ouh2/siTiAKHPXVEly91emxLF4d2enDyg7mkji7E/u7ALfeZN6BsP/06EnzLuvXy+Aj9lgpMG6punh+XsKQ9aP5yEf0BhrqAGaTRrfYujB7zNsv9yU7h9T/KyTJZZx9U9R75//3569epFjx49AKhbty5ffvklhw8fLnH53NxccnNzDY/T0tLKtX9XTx3WNpByw/gwJCfZENAg9x5rVb66jTJZuOEUdvZ6srOsmf1aE2IvqN+CslQajcLIqTGcPuJMzHn1jpMlvp/8amXzz35X2LgukK8+D6JR8zT+PeEcBfkadv5YS5VMAI93v06DZhm8OeAh1TJYnGsF8EMG9HOBF1wgMg8+TAEbDYTecerhQDa8ewtyFfC0gnne4GZtlohff9EAR8cCPl6/C71eg5WVwhcfN2HXttpm2X95yTnyStChQwd27tzJ+fPnAfj999/Zt28f3bt3L3H58PBw3NzcDFNAgGWcr61oVy45MOrZ1ozp14qfIvx4a16U6ufILdmoWTHUbZxN+BsN1I5icTRWChfPufDFhw2JjnRly3e12bqxNt37qdcqr+GXw78nX2TexCbk51X7YTq3KUBDOxjuVvjfZ5yhhzNsvut0URt7WOkLS7zhES3MvgnJ5jlH/tiT8XQJvcr86W15Y8hjLJzdhj4vXOTJf8aZZf+iZKq2yN9++23S0tJo0qQJ1tbW6HQ65syZw+DBg0tcfvLkyYwbN87wOC0trVzFPO2WNboCcPcuMJrvUaOA5BvqHZqCfCuuxRaOvr5w2plGLTPoFXaNpVPVGdFryV6beZn2T6QwfmBTkhLsVM1iie+n5CR7YqOdjebFXXKiw5OJquQBaNg8A48a+Sz95rhhnrUNtHg4lZ4vXKVXm8fQ6y1/BHSF87SGwLveJ3VsYM9dX+IdrKCWVeGI9mb28FIC/JwJL7hWesRho8/y9doG7NlR2JsTc9EVH79s+r90gZ3/qwINK4VyXhCmwpJUKFUL+YYNG1i3bh0RERE0b96ckydPMmbMGPz9/QkLCyu2vL29Pfb2Ffczi4J8K6L+cKRtp3QObCn8KY5Go9CmUwY/rPaqsP2Ul8ZKwdbOQq8NqBqF12bG0KFbMhMHNSXxinl+fvN3LPH9dOakO7XqGrfoagVmcuOaVpU8ACcPuPPqs+2M5o2dE8mVS458/WlA9SziAC3sIM74SyBXCsD3Ph/TeqXwfLkZ2Gt1KHd9FOn1Gqyqyp/sAb2ym6qFfMKECbz99ts8//zzALRs2ZKYmBjCw8NLLOSV4buVNRi/OI7zvzsSecKR5165gdZRz7b1nmbZ/92GvBXD0T3uXI+3x9FJR5eeSbRqn8aUYc1UyVNE66jDv+7t87x+AbnUa5ZFeooNN+LN3xIeNSuGrr1uMnNEQ7IzrPCokQdAZroNebnqddda2vtp07pA/m/VYQYMi2bvdj8aNU/l6T5XWPpuc1XyAGRn2RBzwfijJyfbmrQUW2IuqPMzNIt4f/d1gTeuw7o06OII5/IKB7ON/WswYLYe1qVDBy14WUOqHr7PgCQddDbP2JDD+3wZOOQCNxIdiIl2oX7jVJ57PprtP1aB1vgDTNVCnpWVhZWV8YeutbU1er35Wp+7f/DAzUvHSxMS8PAuIPq0A+8MDiIlSZ3f2bp75TN+3gU8ffLITLfm0jknpgxrxonf3FXJU6RR6yzmfx1leDxyxlUAtm3wZMG4umbP0/Nf1wGYv/6c0fwF44PY/q16I2gt7f0UdcaNd8e3YcjoKAa9Ek1ivAMr/68Ju36uqUoeS2UR7+8mdjDTCz5LhbVphT8/e80NQv4q0tYaiMuHGZmQpgdXq8JR7ot9Cn+KZgYrFrbgxRGRvDb+T9w8Cy8I8/OmOnz5eSOz7L/c9JTvujUW2jGqUcz5o+27DBkyhB07dvDxxx/TvHlzTpw4wYgRIxg2bBhz58697/ppaWm4ubnRhV7YaCzjAhcAVk7qXtyiJPrsHLUjFKOxtbwLCyq56v1a4V5s/HzVjlCMkmN57ydd2t/8VEst2y3vC5PNMMvqBy/Q57Ij9iNSU1Nxda2c8/xFteLJFhOxsS77abgCXS47/5xXqVnLQtVP0qVLlzJ16lRee+01rl+/jr+/P//+97+ZNm2amrGEEEKIKkPVQu7i4sLixYtZvHixmjGEEEJUBzLYTQghhKjCHtBCLldjEEIIIaowaZELIYSoHqRFLoQQQlRh5blhStFkgj179tCzZ0/8/f3RaDRs2rTJ6HlFUZg2bRo1a9bEwcGBkJAQoqKiSt7Y35BCLoQQolooumlKeSZTZGZm0rp1a5YtW1bi8/PmzWPJkiWsWLGCQ4cO4eTkRGhoKDkm/rxTutaFEEKIStC9e/d73gRMURQWL17MlClT6NWrFwBffPEFvr6+bNq0yXDF09KQFrkQQojqoegceXkmCi8wc+eUW4YLSV26dImEhARCQkIM89zc3Gjfvj0HDhwwaVtSyIUQQlQPeqX8ExAQEGB0S+3w8HCToyQkJADg62t85UZfX1/Dc6UlXetCCCGECeLi4owu0VqRd+UsC2mRCyGEqB4qqGvd1dXVaCpLIffz8wMgMTHRaH5iYqLhudKSQi6EEKKaKG8Rr7jfkQcFBeHn58fOnTsN89LS0jh06BDBwcEmbeuB6Fq38ffDxkrdro07FVyNVztCFfFAvP0qnS45Re0IxSj5BWpHqBKs+6arHaGYp3+7rHYEIzkZBex4VO0UlSMjI4MLFy4YHl+6dImTJ0/i6elJnTp1GDNmDO+++y4NGzYkKCiIqVOn4u/vT+/evU3aj3ySCiGEqB7MfGW3o0eP0rVrV8PjcePGARAWFsbq1auZOHEimZmZjBgxgpSUFDp16sSWLVvQarUm7UcKuRBCiOpBX87ucb1p63bp0gXlb4q/RqNh1qxZzJo1q+yZkHPkQgghRJUmLXIhhBDVg6IvnMqzvgWSQi6EEKJ6eEDvfiaFXAghRPVg5nPk5iLnyIUQQogqTFrkQgghqgfpWhdCCCGqMIVyFvIKS1KhpGtdCCGEqMKkRS6EEKJ6kK51IYQQogrT64Fy/BZcL78jtziff78LX//sYvN//LoOy+c1VyHRbT2HJNHv1et4ehcQfcaBj6bUIvKko2T6y8BX4+kYmkzt+tnk5Vhx5rgzn88N4Eq0gyp57iTH6f5atE+n/8hEGrbMxssvnxkv1+PAVnfJ8zf6D49l6LjLbPqiFivfr2+Wfe58ypXseOti8wOfz6Hl1Gz2D3Hm1hFbo+fqDMil1fQss+QThap1IR8TFoz1He/RwPrpzFl2hH07TLsXbEXr/GwyI6bHs/Tt2pw77shzr9xgTkQ0Lz/WmNSbtvffQDXI1LJ9OpvX+nD+DyesbGDo+DjmfBHJiKdakptd/IPHXOQ4lY7WUU/0GUe2flWD6Z9Gq5bDUvPcrWGLdLoPuEb0OSez7rfTV+koutuP0y9Yc2i4CzVD8w3z6vTLpdHo2w0iawfL7H4GHtiudVUHu6WnpzNmzBgCAwNxcHCgQ4cOHDlyxGz7T0uxJ/nm7emRTteJj3Pk1HFPs2UoSZ8RSWyJ8GTbV57ERmlZMqk2udkaQgfdkkx/mTKkMdu/9SYmypFLZx1ZMKEevrXyaNgyU5U8ReQ4lc7RX91YM9+f/VvcVc1RxNLy3EnrqGPivHMsmd6IjDTztr3sPRW03ren67tscQzQ4fXI7dvYWmuNl7F1NmtE05TnXuTl/RJQiVQt5MOHD2f79u2sXbuWU6dO0a1bN0JCQrh69arZs9jY6OnaPZ7tP9QGNGbfvyGHrZ6GrbI4vtfFME9RNJzY60Kzdup0V1liprs5uhQ2G9JT1OtkkuMkKsNrU6I4vNuTkwc8VM2hz4MrP9oR0CcPzR0fkVd/smNrRzd293Ll7CItuuJnK0UlU+1fc3Z2Nt9++y3ff/89jz/+OAAzZsxg8+bNLF++nHfffbfYOrm5ueTm5hoep6WlVVief3RJxNm5gB0/1qqwbZaFq6cOaxtIuWH8p0lOsiGgQe491qp+me6k0SiMnBrD6SPOxJxXbxyBHCdR0R7vfp0GzTJ4c8BDakch4RdbCtI1BPS+/V6u9c88HPz1aH30pJ234dxCBzIvW/PwB+r2+NzTA3qJVtUKeUFBATqdrtgN1B0cHNi3b1+J64SHhzNz5sxKydPt2SscPVCDW0mm3dBdqG/UrBjqNs7mrf7N1I5i0eQ4VS01/HL49+SLvDO8Jfl56l/yI+5be7w75aP1uV3MAgfkGf7ftVEe2hp6Dr7sQmZsNk51LG+Et6LoUcpxB7PyrFuZVHt3uLi4EBwczOzZs4mPj0en0/Hf//6XAwcOcO3atRLXmTx5MqmpqYYpLi6uQrJ4+2XT5tEktm0KqJDtlUfaLWt0BeDuXWA036NGAck31PneZYmZirw28zLtn0hh4qCmJCXYqZpFjpOoSA2bZ+BRI5+l3xxn8x972PzHHlo9msqzL15l8x97sLIyX+swK96KGwdtqNMv72+Xc29V+N7PjFX/i0eJFKWwVV3WSc6RF7d27VoURaFWrVrY29uzZMkSBg0ahJVVybHs7e1xdXU1mirCUz2vkJpsz+HfvCtke+VRkG9F1B+OtO2Ubpin0Si06ZTBmWPqdIdaYiZQeG3mZTp0S2bS4CYkXrFXKcdtcpxERTp5wJ1Xn23H6D63p/OnnNn1ow+j+7RDrzffWJ64jXbYeyr4PJ7/t8ulnSv8JYTW2zIL3oNK1WZC/fr12b17N5mZmaSlpVGzZk0GDhxIvXr1zJZBo1F4qucVdv5UC73OMr5FfreyBuMXx3H+d0ciTxT+hEnrqGfbevVG01taplGzYuja6yYzRzQkO8MKjxqFLYXMdBvyctX7O8pxKh2tow7/urfPtfoF5FKvWRbpKTbciDd/j4Gl5QHIzrIh5oLxR3ROtjVpKbbEXDDfz9AUPVzZaEftXnlY3REnM9aKqz/Z4fN4PnbuCmmR1pyZ54Dnw/m4Ntbde4NqUsp5jtxCW+QWMXTVyckJJycnkpOT2bp1K/PmzTPbvts8moRPzRy2/VDbbPu8n90/eODmpeOlCQl4eBcQfdqBdwYHkZKkzm/ILTFTz39dB2D++nNG8xeMD2L7t+r1rMhxKp1GrbOY/3WU4fHIGYW/VNm2wZMF4+pW+zyWJOmADdnXrAnok2E038pWIemgDZfW2qPL1qD10+MXkk/DkRY8bF2vB005znNb6DlyjaKo9xVj69atKIpC48aNuXDhAhMmTECr1bJ3715sbe//wZeWloabmxsh/v/GxspyugwLrsarHaFK0Nhbzt+siJKr/ujyu1nkccovuP9CAmtXy/tRdfffLqsdwUhORgFvP7qb1NTUCjtdereiWvGky2BsNGXvYSlQ8tiZvq5Ss5aFqi3y1NRUJk+ezJUrV/D09KRv377MmTOnVEVcCCGEMIl0rVe8AQMGMGDAADUjCCGEqCYUvR6lHF3r8vMzIYQQQlQ4ixjsJoQQQlQ66VoXQgghqjC9ApoHr5BL17oQQghRhUmLXAghRPWgKEB5fkdumS1yKeRCCCGqBUWvoJSja13Fy678LSnkQgghqgdFT/la5PLzMyGEEKLaWbZsGXXr1kWr1dK+fXsOHz5coduXQi6EEKJaUPRKuSdTffXVV4wbN47p06dz/PhxWrduTWhoKNevX6+w1yWFXAghRPWg6Ms/mWjhwoW88sorDB06lGbNmrFixQocHR35/PPPK+xlVelz5EUDDwr0f3+ze3MrUP7+nr2ikEaxvO+RigX+7SzzOMlNU0pDUSzrswkKb1JiSYrymGMgWQH55boeTAGFnw9paWlG8+3t7bEv4eZGeXl5HDt2jMmTJxvmWVlZERISwoEDB8oe5C5VupCnp6cDsCthlcpJRJlY3o3GLJMcp6orRe0Axe18VO0EJUtPT8fNza1Stm1nZ4efnx/7Ev5X7m05OzsTEBBgNG/69OnMmDGj2LJJSUnodDp8fX2N5vv6+nLu3Lliy5dVlS7k/v7+xMXF4eLigkajKde20tLSCAgIIC4uzmJuTyeZSsfSMllaHpBMpSWZSqciMymKQnp6Ov7+/hWUrjitVsulS5fIyyt/D4miKMXqTUmtcXOq0oXcysqK2rVrV+g2XV1dLeYfSxHJVDqWlsnS8oBkKi3JVDoVlamyWuJ30mq1aLXaSt/PnWrUqIG1tTWJiYlG8xMTE/Hz86uw/VjeyTchhBDiAWBnZ0e7du3YuXOnYZ5er2fnzp0EBwdX2H6qdItcCCGEsGTjxo0jLCyMhx9+mEcffZTFixeTmZnJ0KFDK2wfUsj/Ym9vz/Tp01U/13EnyVQ6lpbJ0vKAZCotyVQ6lpjJUg0cOJAbN24wbdo0EhISaNOmDVu2bCk2AK48NIqlXjxWCCGEEPcl58iFEEKIKkwKuRBCCFGFSSEXQgghqjAp5EIIIUQVJoWcyr/FnKn27NlDz5498ff3R6PRsGnTJlXzhIeH88gjj+Di4oKPjw+9e/cmMjJS1UzLly+nVatWhgtSBAcH8/PPP6ua6W7vv/8+Go2GMWPGqJZhxowZaDQao6lJkyaq5Sly9epVXnzxRby8vHBwcKBly5YcPXpUtTx169Ytdpw0Gg2jRo1SLZNOp2Pq1KkEBQXh4OBA/fr1mT17tlmuSf530tPTGTNmDIGBgTg4ONChQweOHDmiaqbqrtoXcnPcYs5UmZmZtG7dmmXLlqmW4U67d+9m1KhRHDx4kO3bt5Ofn0+3bt3IzMxULVPt2rV5//33OXbsGEePHuWJJ56gV69enD59WrVMdzpy5Agff/wxrVq1UjsKzZs359q1a4Zp3759quZJTk6mY8eO2Nra8vPPP3PmzBkWLFiAh4eHapmOHDlidIy2b98OQP/+/VXLNHfuXJYvX86HH37I2bNnmTt3LvPmzWPp0qWqZQIYPnw427dvZ+3atZw6dYpu3boREhLC1atXVc1VrSnV3KOPPqqMGjXK8Fin0yn+/v5KeHi4iqluA5SNGzeqHcPI9evXFUDZvXu32lGMeHh4KJ9++qnaMZT09HSlYcOGyvbt25XOnTsrb775pmpZpk+frrRu3Vq1/Zdk0qRJSqdOndSO8bfefPNNpX79+oper1ctQ48ePZRhw4YZzevTp48yePBglRIpSlZWlmJtba38+OOPRvMfeugh5Z133lEplajWLfKiW8yFhIQY5lXGLeYeNKmpqQB4enqqnKSQTqdj/fr1ZGZmVuhlD8tq1KhR9OjRw+h9paaoqCj8/f2pV68egwcPJjY2VtU8P/zwAw8//DD9+/fHx8eHtm3b8sknn6ia6U55eXn897//ZdiwYeW+GVN5dOjQgZ07d3L+/HkAfv/9d/bt20f37t1Vy1RQUIBOpyt2zXIHBwfVe3qqs2p9ZTdz3WLuQaLX6xkzZgwdO3akRYsWqmY5deoUwcHB5OTk4OzszMaNG2nWrJmqmdavX8/x48ct5pxh+/btWb16NY0bN+batWvMnDmTxx57jD///BMXFxdVMkVHR7N8+XLGjRvHf/7zH44cOcIbb7yBnZ0dYWFhqmS606ZNm0hJSWHIkCGq5nj77bdJS0ujSZMmWFtbo9PpmDNnDoMHD1Ytk4uLC8HBwcyePZumTZvi6+vLl19+yYEDB2jQoIFquaq7al3IhelGjRrFn3/+aRHfvhs3bszJkydJTU3lm2++ISwsjN27d6tWzOPi4njzzTfZvn272e+ydC93tt5atWpF+/btCQwMZMOGDbz88suqZNLr9Tz88MO89957ALRt25Y///yTFStWWEQh/+yzz+jevXul3lazNDZs2MC6deuIiIigefPmnDx5kjFjxuDv76/qcVq7di3Dhg2jVq1aWFtb89BDDzFo0CCOHTumWqbqrloXcnPdYu5BMXr0aH788Uf27NlT4bePLQs7OztDK6Bdu3YcOXKEDz74gI8//liVPMeOHeP69es89NBDhnk6nY49e/bw4Ycfkpubi7W1tSrZiri7u9OoUSMuXLigWoaaNWsW+7LVtGlTvv32W5US3RYTE8OOHTv47rvv1I7ChAkTePvtt3n++ecBaNmyJTExMYSHh6tayOvXr8/u3bvJzMwkLS2NmjVrMnDgQOrVq6dapuquWp8jN9ct5qo6RVEYPXo0Gzdu5JdffiEoKEjtSCXS6/Xk5uaqtv8nn3ySU6dOcfLkScP08MMPM3jwYE6ePKl6EQfIyMjg4sWL1KxZU7UMHTt2LPbzxfPnzxMYGKhSottWrVqFj48PPXr0UDsKWVlZWFkZf0RbW1uj1+tVSmTMycmJmjVrkpyczNatW+nVq5fakaqtat0iB/PcYs5UGRkZRi2mS5cucfLkSTw9PalTp47Z84waNYqIiAi+//57XFxcSEhIAMDNzQ0HBwez5wGYPHky3bt3p06dOqSnpxMREcGuXbvYunWrKnmg8Pzh3eMGnJyc8PLyUm08wfjx4+nZsyeBgYHEx8czffp0rK2tGTRokCp5AMaOHUuHDh147733GDBgAIcPH2blypWsXLlStUxQ+EVw1apVhIWFYWOj/kdjz549mTNnDnXq1KF58+acOHGChQsXMmzYMFVzbd26FUVRaNy4MRcuXGDChAk0adJE1c/Mak/tYfOWYOnSpUqdOnUUOzs75dFHH1UOHjyoap5ff/1VAYpNYWFhquQpKQugrFq1SpU8iqIow4YNUwIDAxU7OzvF29tbefLJJ5Vt27aplude1P752cCBA5WaNWsqdnZ2Sq1atZSBAwcqFy5cUC1Pkc2bNystWrRQ7O3tlSZNmigrV65UO5KydetWBVAiIyPVjqIoiqKkpaUpb775plKnTh1Fq9Uq9erVU9555x0lNzdX1VxfffWVUq9ePcXOzk7x8/NTRo0apaSkpKiaqbqT25gKIYQQVVi1PkcuhBBCVHVSyIUQQogqTAq5EEIIUYVJIRdCCCGqMCnkQgghRBUmhVwIIYSowqSQCyGEEFWYFHIhhBCiCpNCLkQ5DRkyhN69exsed+nShTFjxpg9x65du9BoNKSkpNxzGY1Gw6ZNm0q9zRkzZtCmTZty5bp8+TIajYaTJ0+WaztCiJJJIRcPpCFDhqDRaNBoNIa7pM2aNYuCgoJK3/d3333H7NmzS7VsaYqvEEL8HfXvDCBEJXn66adZtWoVubm5/O9//2PUqFHY2toyefLkYsvm5eVhZ2dXIfv19PSskO0IIURpSItcPLDs7e3x8/MjMDCQV199lZCQEH744Qfgdnf4nDlz8Pf3p3HjxgDExcUxYMAA3N3d8fT0pFevXly+fNmwTZ1Ox7hx43B3d8fLy4uJEydy9+0K7u5az83NZdKkSQQEBGBvb0+DBg347LPPuHz5Ml27dgXAw8MDjUbDkCFDgMI7cYWHhxMUFISDgwOtW7fmm2++MdrP//73Pxo1aoSDgwNdu3Y1yllakyZNolGjRjg6OlKvXj2mTp1Kfn5+seU+/vhjAgICcHR0ZMCAAaSmpho9/+mnn9K0aVO0Wi1NmjTho48+MjmLEKJspJCLasPBwYG8vDzD4507dxIZGcn27dv58ccfyc/PJzQ0FBcXF/bu3ctvv/2Gs7MzTz/9tGG9BQsWsHr1aj7//HP27dvHrVu32Lhx49/u96WXXuLLL79kyZIlnD17lo8//hhnZ2cCAgL49ttvAYiMjOTatWt88MEHAISHh/PFF1+wYsUKTp8+zdixY3nxxRfZvXs3UPiFo0+fPvTs2ZOTJ08yfPhw3n77bZOPiYuLC6tXr+bMmTN88MEHfPLJJyxatMhomQsXLrBhwwY2b97Mli1bOHHiBK+99prh+XXr1jFt2jTmzJnD2bNnee+995g6dSpr1qwxOY8QogxUvvuaEJUiLCxM6dWrl6IoiqLX65Xt27cr9vb2yvjx4w3P+/r6Gt0Scu3atUrjxo0VvV5vmJebm6s4ODgoW7duVRRFUWrWrKnMmzfP8Hx+fr5Su3Ztw74UxfjWpZGRkQqgbN++vcScRbesTU5ONszLyclRHB0dlf379xst+/LLLyuDBg1SFEVRJk+erDRr1szo+UmTJhXb1t0AZePGjfd8fv78+Uq7du0Mj6dPn65YW1srV65cMcz7+eefFSsrK+XatWuKoihK/fr1lYiICKPtzJ49WwkODlYURVEuXbqkAMqJEyfuuV8hRNnJOXLxwPrxxx9xdnYmPz8fvV7PCy+8wIwZMwzPt2zZ0ui8+O+//86FCxdwcXEx2k5OTg4XL14kNTWVa9eu0b59e8NzNjY2PPzww8W614ucPHkSa2trOnfuXOrcFy5cICsri6eeespofl5eHm3btgXg7NmzRjkAgoODS72PIl999RVLlizh4sWLZGRkUFBQgKurq9EyderUoVatWkb70ev1REZG4uLiwsWLF3n55Zd55ZVXDMsUFBTg5uZmch4hhOmkkIsHVteuXVm+fDl2dnb4+/tjY2P8dndycjJ6nJGRQbt27Vi3bl2xbXl7e5cpg4ODg8nrZGRkAPDTTz8ZFVAoPO9fUQ4cOMDgwYOZOXMmoaGhuLm5sX79ehYsWGBy1k8++aTYFwtra+sKyyqEuDcp5OKB5eTkRIMGDUq9/EMPPcRXX32Fj49PsVZpkZo1a3Lo0CEef/xxoLDleezYMR566KESl2/ZsiV6vZ7du3cTEhJS7PmiHgGdTmeY16xZM+zt7YmNjb1nS75p06aGgXtFDh48eP8XeYf9+/cTGBjIO++8Y5gXExNTbLnY2Fji4+Px9/c37MfKyorGjRvj6+uLv78/0dHRDB482KT9CyEqhgx2E+IvgwcPpkaNGvTq1Yu9e/dy6dIldu3axRtvvMGVK1cAePPNN3n//ffZtGkT586d47XXXvvb34DXrVuXsLAwhg0bxqZNmwzb3LBhAwCBgYFoNBp+/PFHbty4QUZGBi4uLowfP56xY8eyZs0aLl68yPHjx1m6dKlhANnIkSOJiopiwoQJREZGEhERwerVq016vQ0bNiQ2Npb169dz8eJFlixZUuLAPa1WS1hYGL///jt79+7ljTfeYMCAAfj5+QEwc+ZMwsPDWbJkCefPn+fUqVOsWrWKhQsXmpRHCFE2UsiF+IujoyN79uyhTp069OnTh6ZNm/Lyyy+Tk5NjaKG/9dZb/Otf/yIsLIzg4GBcXFx47rnn/na7y5cvp1+/frz22ms0adKEV155hczMTABq1arFzJkzefvtt/H19WX06NEAzJ49m6lTpxIeHk7Tpk15+umn+emnnwgKCgIKz1t/++23bNq0idatW7NixQree+89k17vs88+y9ixYxk9ejRt2rRh//79TJ06tdhyDRo0oE+fPvzzn/+kW7dutGrVyujnZcOHD+fTTz9l1apVtGzZks6dO7N69WpDViFE5dIo9xqlI4QQQgiLJy1yIYQQogqTQi6EEEJUYVLIhRBCiCpMCrkQQghRhUkhF0IIIaowKeRCCCFEFSaFXAghhKjCpJALIYQQVZgUciGEEKIKk0IuhBBCVGFSyIUQQogq7P8BBjL0f9I44kAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test_le, elm_predicted)\n",
    "disp.figure_.suptitle(\"Confusion Matrix [ELM]\")\n",
    "print(f\"Confusion Matrix [ELM]:\\n{disp.confusion_matrix}\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A standard ElmClassifier with hidden size of 100 and relu activation function results in 88% accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification with Metaheuristic-based ELM Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Train an `MhaElmClassifier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_params = {\n",
    "    \"name\": \"GA\",\n",
    "    \"epoch\": 10,\n",
    "    \"pop_size\": 30,\n",
    "}\n",
    "\n",
    "mha_elm_classifier = MhaElmClassifier(hidden_size=100, act_name=\"relu\", obj_name=\"KLDL\", optimizer=\"BaseGA\", optimizer_paras=opt_params, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/codespace/.local/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:975: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "2023/10/05 03:41:59 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: Solving single objective optimization problem.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/10/05 03:42:00 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 1, Current best: 0.24399, Global best: 0.24399, Runtime: 0.34016 seconds\n",
      "2023/10/05 03:42:01 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 2, Current best: 0.2543, Global best: 0.24399, Runtime: 0.60120 seconds\n",
      "2023/10/05 03:42:01 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 3, Current best: 0.24905, Global best: 0.24399, Runtime: 0.47027 seconds\n",
      "2023/10/05 03:42:01 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 4, Current best: 0.24466, Global best: 0.24399, Runtime: 0.29067 seconds\n",
      "2023/10/05 03:42:02 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 5, Current best: 0.24237, Global best: 0.24237, Runtime: 0.27694 seconds\n",
      "2023/10/05 03:42:02 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 6, Current best: 0.24255, Global best: 0.24237, Runtime: 0.32962 seconds\n",
      "2023/10/05 03:42:02 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 7, Current best: 0.23039, Global best: 0.23039, Runtime: 0.35134 seconds\n",
      "2023/10/05 03:42:03 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 8, Current best: 0.23715, Global best: 0.23039, Runtime: 0.37898 seconds\n",
      "2023/10/05 03:42:03 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 9, Current best: 0.24108, Global best: 0.23039, Runtime: 0.31639 seconds\n",
      "2023/10/05 03:42:03 PM, INFO, mealpy.evolutionary_based.GA.BaseGA: >Problem: P, Epoch: 10, Current best: 0.24384, Global best: 0.23039, Runtime: 0.38648 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MhaElmClassifier(act_name=&#x27;relu&#x27;, hidden_size=100, obj_name=&#x27;KLDL&#x27;,\n",
       "                 optimizer=&lt;mealpy.evolutionary_based.GA.BaseGA object at 0x7fbeb7fe2020&gt;,\n",
       "                 optimizer_paras={&#x27;epoch&#x27;: 10, &#x27;name&#x27;: &#x27;GA&#x27;, &#x27;pop_size&#x27;: 30},\n",
       "                 verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MhaElmClassifier</label><div class=\"sk-toggleable__content\"><pre>MhaElmClassifier(act_name=&#x27;relu&#x27;, hidden_size=100, obj_name=&#x27;KLDL&#x27;,\n",
       "                 optimizer=&lt;mealpy.evolutionary_based.GA.BaseGA object at 0x7fbeb7fe2020&gt;,\n",
       "                 optimizer_paras={&#x27;epoch&#x27;: 10, &#x27;name&#x27;: &#x27;GA&#x27;, &#x27;pop_size&#x27;: 30},\n",
       "                 verbose=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MhaElmClassifier(act_name='relu', hidden_size=100, obj_name='KLDL',\n",
       "                 optimizer=<mealpy.evolutionary_based.GA.BaseGA object at 0x7fbeb7fe2020>,\n",
       "                 optimizer_paras={'epoch': 10, 'name': 'GA', 'pop_size': 30},\n",
       "                 verbose=True)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mha_elm_classifier.fit(X_train_scaled, y_train_le)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mha_elm_predicted = mha_elm_classifier.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Report classification results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98        88\n",
      "           1       0.84      0.93      0.89        91\n",
      "           2       0.94      0.92      0.93        86\n",
      "           3       0.88      0.92      0.90        91\n",
      "           4       0.98      0.93      0.96        92\n",
      "           5       0.89      0.92      0.91        91\n",
      "           6       0.96      0.88      0.92        91\n",
      "           7       0.97      0.93      0.95        89\n",
      "           8       0.88      0.80      0.83        88\n",
      "           9       0.85      0.89      0.87        92\n",
      "\n",
      "    accuracy                           0.91       899\n",
      "   macro avg       0.92      0.91      0.91       899\n",
      "weighted avg       0.92      0.91      0.91       899\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test_le, mha_elm_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix [MHA-ELM]:\n",
      "[[88  0  0  0  0  0  0  0  0  0]\n",
      " [ 1 85  0  2  0  0  0  0  1  2]\n",
      " [ 0  3 79  2  0  0  0  0  0  2]\n",
      " [ 0  0  2 84  0  4  0  1  0  0]\n",
      " [ 1  0  0  0 86  0  1  0  1  3]\n",
      " [ 0  0  0  2  1 84  1  0  0  3]\n",
      " [ 0  8  0  3  0  0 80  0  0  0]\n",
      " [ 0  0  1  1  0  0  0 83  3  1]\n",
      " [ 0  4  2  1  1  4  1  1 70  4]\n",
      " [ 1  1  0  0  0  2  0  1  5 82]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test_le, mha_elm_predicted)\n",
    "disp.figure_.suptitle(\"Confusion Matrix [MHA-ELM]\")\n",
    "print(f\"Confusion Matrix [MHA-ELM]:\\n{disp.confusion_matrix}\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A Metaheuristic-based ElmClassifier with hidden size of 100 and relu activation function results in 93% accuracy. It is an improvement of 5%."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}