quantum-classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e9c29de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from qiskit import IBMQ, Aer, QuantumCircuit\n",
    "from qiskit.circuit import ParameterVector\n",
    "from qiskit.circuit.library import TwoLocal, ZFeatureMap, ZZFeatureMap\n",
    "from qiskit.utils import QuantumInstance\n",
    "from qiskit.visualization import plot_error_map\n",
    "from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier\n",
    "from qiskit_machine_learning.kernels import QuantumKernel\n",
    "from qiskit_machine_learning.neural_networks import CircuitQNN, OpflowQNN\n",
    "from qiskit_machine_learning.utils.loss_functions import CrossEntropyLoss, L2Loss\n",
    "from sklearn import preprocessing, svm\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "os.environ[\"QISKIT_IN_PARALLEL\"] = \"TRUE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "43c1f60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 2508\n",
    "random.seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af6fccbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# IBMQ.save_account('YOUR TOKEN HERE')\n",
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(\n",
    "    hub=\"ibm-q-education\", group=\"qscitech-quantum\", project=\"qc-bc-workshop\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad14a070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ERCQ4JboiIiIiEktqjCYiIiIisaREV0RERERiSYmuiIiIiMSSEl0RERERiSUluiIiIiISS0p0RURERCSWlOiKiIiISCwp0RURERGRWFKiKyIiIiKxpERXRERERGJJia6IiIiIxJISXRERERGJJSW6IiIiIhJLSnRFREREJJaU6IqIiIhILCnRFREREZFYUqIrIiIiIrGkRFdEREREYkmJroiIiIjEkhJdEREREYklJboiIiIiEktKdEVEREQklpToioiIiEgsKdEVERERkVhSoisiIiIisaREV0RERERiSYmuiIiIiMSSEl0RERERiSUluiIiIiISS0p0RURERCSWlOiKiIiISCwp0RURERGRWFKiKyIiIiKxpERXRERERGJJia6IiIiIxJISXRERERGJJSW6IiIiIhJLSnRFREREJJaU6IqIiIhILCnRFREREZFYUqIrIiIiIrGkRFdEREREYkmJroiIiIjEkhJdEREREYklJboiIiIiEktKdEVEREQklpToioiIiEgsKdEVERERkVhSoisiIiIisaREV0RERERiSYmuiIiIiMSSEl0RERERiSUluiIiIiISS0p0RURERCSWlOiKiIiISCwp0RURERGRWFKiKyIiIiKxVHKJrpk5M9shdBzFZGbTzGxkO+brZmYvmdnmBQhLREREpKzllOia2etm1mhmy83sPTO70cx6Fzq4jjCzB83s9BbGD4yS6uXNXscVOc4qYHfg79H73c3seTP7yMx+mDZdFzN73My2TQ1zzq0E/gxcUMyYRURERMpBW0p0j3LO9QaGAHsAPy1IRMXXzznXO+11R6aJzKxTs/ed27KSFqb/LnCbc85F738D/Aif/P7MzLaIho8HZjjn3mw2/+3AyWbWrS3xiIiIiMRdm6suOOfeA/6FT3gBMLN9zexRM1tqZs+a2YFp4041sxfNbJmZLTSz76Yvz8wmmNm7ZvaOmZ3WbNxGZnazmX1oZovN7GdmVhGNu9jMbk2bNlVC29nMfgUcAFwVldJe1dbtjEqtrzaz2WZWDxwUlWxfYGZ1QH20rm9GJbBLo1LkndOWscH0GVb1DWBe2vtBwFzn3NvAK8B2ZrYdMBq4vPnMzrm3gE+Afdu6jSIiIiJx1uZE18y2wSdnr0bvtwbuBX4JVOJLI2eY2WbRLB8ARwJ9gVOBy81sz2jew6LpDwV2BA5ptrorgY2AwcBw4KRoGS1yzl0EPAycE5XSntPW7Yx8G/gV0AeYHw0bCxwB9IvimgacB2wGzAbuNrOuacv4fHrn3Jr0hZtZL3xi+3La4OeAr0Wf80DgNeAPwI+dc6uzxPkivgRYRERERCJtSXRnmdky4E188vrzaPh3gNnOudnOuSbn3BygFjgcwDl3r3PuNefNA+7Dl7YCjAFucM4955yrBy5OrSyqKnAc8FPn3DLn3OvAZcCJ7dzWbD6KSmNTr53Txv3dOfdItF0romF/cM696ZxrjOK71zk3J0pCLwV6APulLSN9+ub6RX+XpQ37EXAWcBfwQ2D/aPxCM/u7mc0zs281W86ytGWJiIiICNCWeqYjnXP3m9lwfL3QTYGlwADgW2Z2VNq0XYB/A5jZN/BJ8U74xLon8N9ouq2Ap9LmW5z2/6ZA12bDFgNbtyHmXGzavKQ1TfP6sM2HbUVafM65JjN7k/VjzLSMlKXR3z7AimgZi4kuEsysJ/Ao8HV86fYd+NLz58zsAefckrT5lyIiIiIin2tPHd15wI340kvwidwtzrl+aa9ezrnfRg2kZkTT9nfO9cPf3rdo3neBbdMWv13a/x8Bq/GJdPr4t6P/6/FJc8oWrM/RcZmWkT7sHdLiMzPDb8/bWaZff0G+FPs1/EVAJtXA9c6594EvAbXOuU+Bt4D0Lth2Bp7NvhkiIiIiydPefnSvAA41syHArcBRZvZ1M+tkZt3N7MCojmlXoBvwIbAmKt39WtpypgOnmNkuUellqjoEzrm10fhfmVkfMxuA73kg1QBtAfBVM9vOzDZiw14g3sfXoS2k6cARZnawmXUBzgdW4kthczUbX/94PWa2C3AgcHU0aBEwwsz64+szvxFNtzW+bvRj7dwGERERkVhqV6LrnPsQuBmYGHV3dTRwIT6hfROYAFQ455YB38cnhJ/gG3fdlbacf+CT5rn4xm1zm63qXHzJ7UJ8Y7Db8f3GEtUFvgOow1d/uKfZvL8HjjWzT8zsDy1sztJm/eiOb8Pn8DK+jvKV+BLoo/DdsK3KdRnAtcAJUWlwuj8CP4gSfvCJ/PeB54FfR71fgP9Mb4r61BURERGRiK3rvlVCMbPbgenOuVltnK8bvsrCV51zHxQiNhEREZFypURXpAwsnD5rE2AvYCiwG75+ei+gO766TH30ehHf68lTg8eM1MVPiRoyaMRGwJ74/VmFb1DaE78/VwGNwHL8na4n8Xet3lmwaK4O2CVoyuhJvfEPUtoL38f8Rvj92QPf1qQR//tcxLr9uXj8jInanyIFpkRXpMQsnD6rAt8F3/74etp74PuhbsSfPLu0MPtqoAGfMNXj67LPw9cb//fgMSPXZp9VCmHIoBEG7AN8FV8ffy9gE/x+6oFvy5DNWnzC2w2fANcBDwL/AeYsWDQ3W9/aUkBTRk/aHTgIv0/3xjeGrsfvp+4tzNqE35+d8Q2V/ws8hG9j8c/xMyZm6oZSRDpAia5IiVg4fdbG+AeinI8v4etOy0ltrlIlSivxT9e7fvCYkR/mYbnSgiGDRvQGTgB+DGyOT2hbSmpztRafVDXh6/Jfs2DR3LfysFxpwZTRk7oD3wIuwD/op4KWk9pcpZLfCmAqcOX4GRNfy8NyRQQluiLBLZw+ay98jyLH4E96PVueo0Ma8d373Yt/AMtjg8eM1EEgj4YMGrEL/mmJ38Enpb0LuLrUg2zmAZOBuarekF9TRk8ahG8YfXo0qE8BV7cKfwyoBS4BZo+fMVF3YUQ6oJCJbkkebMeOHQvAtGnTAkeSk+Y9MUiMLJw+awhwE75P5G5ApyKuvgmf9L4FnDx4zMjHi7juWBoyaMQO+D7G98SXxLflgTwd5fClvJ8A4xYsmjuniOuOpSmjJ20NXI+vPlRBfkrj22IZ/kLme+NnTLyzyOsWiQ0luqVNiW4MLZw+qyswEV9FoTvh93Mj8P+Anw4eM3JFaxPL+oYMGlEB/AD4JcW/YMmkAbgTOHfBormfBY6l7EwZPcmAk4Cr8PszH9WHOqIe3/XmuPEzJqrKkUgbJS7RXbLEPzW3srIycCQ5CZ0ASZ5Fpbh/BbbE95pQKhrwfUGPUelu7qJS3On4pxuW0v5MtfL/tkp3cxeV4t6CbzxYSvsz1RPH6SrdFWmbxCW6ZUaJbkwsnD6rC/6RzqVSipuNSndzUIKluNmodDcHJViKm41Kd0XaKHGJ7p13+ovhY489NnAkOSnVZEjaYOH0WX2A+/D9pRayoVm+NOD7bz1o8JiRS0IHU2qGDBrRDZiBr7tZSqV+2azAPxJ9/wWL5r4dOphSM2X0pAp8XdwxlMf+XAV8ChwwfsbEl0MHI1LqEpfoqo6uFNPC6bMq8S3idyA/XREVy0p8Q7Vhg8eMfK+1iZNiyKARvYB/4huclcNFS8oa4GN8squuqyJTRk/qgq96cijlkeSmNAGfASPGz5j4TOhgREpZRegAROIqeprZk/j6m+WU5IK/fbsd8NTC6bO2Ch1MKYiS3IfwD3wopyQXfA8QmwJPDhk0YqfQwZSCKMmdDXyN8kpywZ+7NwIemjJ60t6hgxEpZUp0RQogqq4wD9iG4ndLlC9d8A86mB8l7YkVVVf4J7AL/mlm5agTPjmaP2TQiG1DBxNSVF3hL8B+lN9FS4rh+2i+f8roSbuEDkakVCnRFcmzhdNndQPmANtTvkluSmdga+DBhdNnFfLBByVryKARnfB1cvek/Ermm6sANgYeGTJoxGahgwkhanh2LXAY5ZvkpuuDL9kdGDoQkVKkRFck/36Lb3hW7klRSld8HeOrQgcSyHn4hmdxSIrAX7xsAdw2ZNCIJLYDOD56xWV/GtAP+PuU0ZNKtfcPkWAS1xitsbERgB49yuLuYxJPQmVt4fRZ++K7/ymLL1gbNQBHDx4z8v7QgRRLVJ91AfHcn/XAdxcsmntb6ECKZcroSVsA/6Owj/ENpR6oGT9j4iWhAxEpJYlLdMuMEt0ysnD6rO74k2ic6z9+CGw/eMzIZaEDKbSoykItvnQ+rne/lgE7LVg0N/Y9a0RVFv6JL50v9ypF2TQCe6jbMZF14nrwzuqWW27hlltuCR2GxNNvgLg32uoDXBk6iCI5D9iReB8nuwM3J6QKw/HA/sQ3yQXfW8p0VWEQWSfOB/CMZs+ezezZs0OHITETVVn4LvGp95dNd+BbC6fPOiR0IIUUVVmYRPl1O9VWXfA9D3w7dCCFFFVZ+H/Ef39W4BvBnh86EJFSkbhEVyTfFk6fVQFMI571ODPpCdwWPdY4rm7Gl44lQS/g6iGDRmwUOpACuppk7c+Lp4yeFOcqVCI5U6Ir0nEHE/8qC831AI4OHUQhDBk0ogr4Esk6PlYAJ4UOohCmjJ60Fb4rsThXWWiuAjg7dBAipaBzoRa88P45hVr0egYfcmhR1iPSggn4jtuTpA9wAXBn6EAK4DySlRSBLwWcMGTQiKsWLJobt4bEZ4UOIIBuwFlTRk+qHj9j4qrQwYiElKQSC5G8Wzh91rbAASSzh4xdF06ftXPoIPJpyKARffGNlgpWCFDCNgaGhw4in6LH/J5NfPq0bgsDjgkdhEhoiTuYT5s2LXQIEi/fI5lJLviGTOfhG+HFxUlAU+ggAumFvzvxYOA48umbJPA8F0nddflL6EBEQlKJrkg7LZw+qyv+tmhSGrk01xk4MS6PBo662JpA/FvmZ2PAwUMGjdgqdCB5dAHxfDhErr4wZfSkL4UOQiSkxCW61113Hdddd13oMCQevkkCf0PNrAWOCx1EnuwPVIYOIjAHnB46iHyYMnrSjsBuoeMIrAtwbuggREJK3El67ty5zJ07N3QYEg+HkOzSIvCN8L4ROog8GU5yS+dTuhOf/XkAya2GktIZUIttSbTEJboieTQsdAAlYp/QAeTJcHwJWNJ9KSZPStuP5FZDSbfNlNGTktLHt8gGlOiKtMPC6bM64x8PK7Dlwumz4pBQ7Bk6gBJhwIDQQeSBLkS9BmD30EGIhKJEV6R9vgiof0qvARgSOoiOGDJoxKaoGkrKGmCv0EF0RNSt2ODQcZSILpT5/hTpiMQlut27d6d79yR2qSh5phPHOl0p/89jL6AxdBAlojflXx1lF2BF6CBKRA98fWWRREpc/4I33HBD6BAkHr5CwKehWUUFlbvvRu9tt8Y6d2bV0qV8vOA5Vi75JEQ43YGvAn8IsfI8GQr0DLXyE04dzdFjDmP7HQfSqVMnrrniJq75/U2hwqnA789ythcBC3IO/b8j2OqL29Jn041Yu2YN773yNg/d/AAfv/lhqJC+HGrFIqElrkRXJE+GhFz5Jnt8iY12HMzaFStoeOddum1SyZbD96Oia7An1+4aasV5sgcBG6Lt/KWd+GzpMt5/N1gi1NwOoQPooF0I2BDtS4fuyarGlbw8/zlWNaxk0J47cszEb9OpS6dQIW0TasUioSUu0b3yyiu58sorQ4ch5S9Y6V9Ft670GTgA1+R4Z94jfPBYLcsXv0VFly5stGOwaonl3qo76EMvfjb+N5w+djwvv/BqyDDSlXv9rr4hVz7twhuY9tMbmHPNvfz157cA0GeTvmyyzWahQuo8ZfSkYFm2SEiJq7rw6KOPAnDuuepDWzokWCLQtW9frFMFq5fX07TSt4db+ckn9Bm4LV37bRQkpmXLVwz49j5nuCArz4MenbvTuEZVOlM6d+rU+4Xr7yjb/bnllr15993lwdb/7stvff5/RWefXzatbaL+k2AxrcH3Ed0QKgCRUHIq0TWzSjObaWb1ZrbYzL5d6MBESlywBwt06u5X7das+XyYW7N2vXFFj6lTed8ccpRtTlcQTU3l/ZyFtWtLY3926d6Fw875JgBP3f0Y9UuDJbprKf9SepF2ybVE94/4rpT64+sm3mtmzzrnni9UYCIlbk3rkxTG2hUrAbDO636+qf9T44qtqak0Eov2isPTEfLJrLw/kYqK8PF379ODYy4ayxY7bk3dnKd5+JYHQoZTAawOGYBIKK0mumbWCxgN7OacWw7MN7O7gBOBnxQ4PpFSFawrqtWfLcOtbaJzz5506taNtStX0q2yHwCrln4aJKY1a8u7BNCsvEuk861zmZfQh77D0GezjRg98QQqt96EJ/72CPNvC/7Y+c6o+zxJqFxKdHcC1jrn/pc27Fn84zLXY2ZnAmcC/PK8HzD2iCPyEmQ+9evXL3QIEg/BKnSuXbmSZa+/Qd/tB7Llgfuz6tPP6LXt1jStXs2nry4ME1OZJ7oVgUswRx13OHsM3Y0v7uoftnfQ1/Znq2368+/7HuHfcx4pejxKdDtm7K9Oofcmffnsw6V07tqZA0/9GgAvPfwc7736ToiQ3PgZE4PdhRIJKZdEtzfQvJjoUzI8Rcg5dy1wLcDC++eU5L3Mq6++OnQIEg+LCfiQhI8X1OFcE7232ZqeW2/Jyo+X8PGzz33eOK3YNtm414O3P3HdQUFWngdDBo2YAvyAQD3R7DF0N7557GGfv//CLjvwhV124J233g+S6NavWPXyLqcf98WirzhP/jl60njg1wSqS997E9/pQ9/N+rHnkeu6sP3g9fdCJbpBOtgWKQW5JLrL2bCrlr7AsvyHI1I2HgIOJ1ADD7e2iY+fruPjp+tCrL65NcC80EF00ONAPYEeA1w94RKqJ1wSYtXZFD+7zq+n8HddgiS6U0ZPCrHaljwTOgCRUHIpvfgf0NnMdkwbtjtQlg3RLrnkEi65pKROKFKeavENNMUniE+EDqKDaklgv+JZ1FP+ie4zBOzrusSsBh4MHYRIKK0e2J1z9cDfgBoz62Vm+wNHA7cUOrhCeOaZZ3jmGV3cSoctoPwfkpAv3fAlaOVsIUp0U5oo8/05fsbEz4CSecxcYA34CzmRRMr1wP49/En9A2AacJa6FpMkGzxmZD3wXug4SkTj4DEj3w8dREcsWDTXAS+EjqNEdCcen4WSO68HZX7hItIROSW6zrklzrmRzrlezrntnHO3FzowkTLwZOgASsSzoQPIk4fwpZlJt3DBorlx6HN1HhCmY+nSsmz8jIkfhQ5CJBTdqhNpv3+jvilXAcE7Cc2TR/H1U5NsLT7hj4MnUD160AW5JFziEt0tttiCLbbYInQYEg/T0UO1mijT+voZ3Iv25wrgutBB5Mmj+PqpSbYM+FPoIERCSlyie/nll3P55ZeHDkNiYPCYkR8A/yDZt7sfGzxm5Ouhg8iHBYvmNgJ/JtmlgG8tWDQ3FiWA42dMbAKmkOxkdyUwO3QQIiElLtEVybNLSe6JdBkQt776rsTfvk+i5cDvQgeRZ1NJ7nmuEbhi/IyJSf0+iwAJPADU1NRQU1MTOgyJj/8AZd3jQAc0Av8KHUQ+LVg091Xg6dBxBGLAHaGDyKfxMyZ+DNxFMu+6GPGphiLSbolLdF988UVefPHF0GFITAweM9LhSzWXh46lyBqAKYPHjIxjAnEJyXvy4yrghgWL5sbx7sRlJK/RaBPwz/EzJn4QOhCR0BKX6IoUwO0krxFTBf62cBzdi2+UlSRrgT+EDqJAngTeDB1EkTXiq1WJJJ4SXZEOGjxm5HJgAsnpmqoemDR4zMhY9s25YNHctcCZJKfudSO+NPeV0IEUwvgZEx1wOsnZnyuB+8fPmFjuj3EWyQsluiL5cQ3wX2BN6EAKrAl4nfg1QlvPgkVzZ+HrHyfhgQOf4C/UYitK+v5MMpLdRnxiLyIkMNEdNGgQgwYNCh2GxExUV3cs8U+MVgJjBo8ZGfeEHuAM4l+3sxE4LqZ1c5u7AJ/Ux1kDME5PQhNZx5xzBVnwwvvnFGbBzQw+5NBirCaUpNX7LHsLp886C5gM9AodSwHUA78ePGbkr0MHUixDBo0YCdwG9AwcSiE0AjcuWDT3e6EDKZYpoyftD8wBeoSOpQBW4hugjQwdiEgpKViiCxQl0Y05JbplZuH0WYZ/ItNQoHPgcPKpCXgRGJKQ0tzPDRk04m/A4UC30LHk2TvAjgkpzf3clNGTrgTGEb9kdymwo0pzRdaXuKoLF154IRdeeGHoMCSmoioMx+O7G4vTxV49cGzSktzIGfhb3nHqSq0BODZpSW7kx/heGFaHDiSPGoHvKMkV2VDiEt1FixaxaNGi0GFIjA0eM3IxcBDx6YWhAThs8JiRL4UOJIQFi+Z+DOyPLzGLw8VLql7uf0IHEsL4GRMbga8CHxCPp+A1AGePnzHx3tCBiJSixCW6IsUweMzIBcBhlH8r70bgmMFjRj4aOpCQFiyauxAYjn+QRDknu43AGQsWzb0ndCAhjZ8x8X1gP8q/pL4BqB4/Y+INoQMRKVVKdEUKZPCYkY/gk91yfWpaAz7JjdVjfttrwaK5zwEH4Et2yzE5SiW5t4UOpBSMnzHxDeDL+JLdcqzG0AhMHD9j4mWhAxEpZUp0RQpo8JiRD+NLAj+lfJIjh0/Ovz54zMh/hg6mlCxYNLcOnxx9RHn1mdwAjFGSu77xMyYuBPbE19ldFTictmgEvjd+xsQpoQMRKXWJS3R33nlndt5559BhSIIMHjPyaWBv4CVKvypDA7AI2G/wmJHzQwdTiqIniO0J1FL69bAbgfeAQ5NeXSGb8TMmvov/fc6l9PfnSnx1i9HjZ0y8MXAsImVB3YuVNnUvFiMLp8/qjO+0/iJ8V1WldKHpgBXAFcDFg8eMLKfSrSCGDBphwP8BlwJdKb3u5BqBW4DxCxbNLfUELrgpoycZcBxwLf732TVsRBtoAGYD3x0/Y+KS0MGIlAsluqVNiW4MLZw+axfgr8BASuNBBA3Au/gnnj0dOphyM2TQiIHANOBLlMaDQhrx9YjHLlg0d17gWMrOlNGT+gM34utjl8L+XIn/jZ48fsbEu0MHI1JuEvdktB/+8IcAXH755YUIJ9+U6MZUVLr7Y+Bn+JLdEA8jWImvN3w58AuV4rZfVLr7XeAy/O82xMMIVuPrDd8MnK9S3PaLSnfH4Et3OxEm4V2L/43eiy/Fjfvji0UKInGJ7tixYwGYNm1aIcLJNyW6Mbdw+qytgbOAs/En1D5FWG3qYRbXAlcNHjPy9SKsMxGGDBqxGf6pW+OB7hRvf1bgE9zfL1g0N5H9HRfClNGTNgJOAiYAG+MT3kIflxvw+/NO4PLxMybqLotIByjRLW1KdBNi4fRZXYCj8XV4dwW6kN86n2vwrcpfBS4B7hw8ZuTKPC5f0gwZNKIT8HV8qf2X8YlLPut8NrGuodlvgWkqwS2cqIR3OD7hHREN7p7HVTh8Q7hPgcnATeNnTFyax+WLJJYS3dKmRDeBojq8/wccAuyAbyRmQO82LKYenwx1BxYCDwLXRA+ykCIaMmjEIPz+PAz4Ar6KgcPvz1x/4w34i5Ue+K6wHgauAR5fsGiu2kMU0ZTRk7bCPxb6KGAX/O+sCV/am2sD0xX4C8+ewDvAo8B1wL/Hz5io/SmSR0p0S5sS3YSL6vLuDAzFP8lpf2BbfIlvF/yJtQmfPK0G3gb+AzyC7/7q+cFjRpZjZ/ixFJX07gTsBXwFGAYMArqaWbfU8bhTRQWdKipYtWbNQuAxYD5+f/53waK5K4IELxuISnq3x+/PL+MfLbwDvs59F3x1JMe63+fHwBPAQ8BTwLPjZ0xUSbxIASUu0b3kkksA+PGPf1yIcPJNia60aOH0WRWDx4wslwdRSAse/sX1rsk5Kmzdz/6An5+uY0AZmzJ6UgXgVEorEk6p9ftYcGWS4IrkREluvKQnuVL+xs+YqN+nSGCl1GG9iIiIiEjeJC7RPeusszjrrLNChyEiIiIiBZa4qgtLly4NHYKIiIiIFEHiSnRFREREJBmU6IqIiIhILCnRFREREZFYSlwd3f322y90CCIiIiJSBIlLdM8999zQIYiIiIhIEeRUdcHMzjGzWjNbaWY3FjgmEREREZEOy7VE9x3gl8DXgR6FC6fwTj31VABuuOGGwJGIiIiISCHllOg65/4GYGZDgW0KGlGBrVixInQIIiIiIlIEee11wczOjKo41E679958LlpEREREpE3y2hjNOXctcC3AwvvnuHwuW0RERESkLdSProiIiIjEUuK6FxsxYkToEERERESkCHJKdM2sczRtJ6CTmXUH1jjn1hQyuEI444wzQocgIiIiIkWQa9WFnwGNwE+A70T//6xQQYmIiIiIdFSu3YtdDFxc0EiKZOzYsQBMmzYtcCQiIiIiUkhqjCYiIiIisaREV0RERERiSYmuiIiIiMSSEl0RERERiaXE9aN7+OGHhw5BRERERIogcYnuiSeeGDoEERERESmCxFVdaGxspLGxMXQYIiIiIlJgiSvRPe200wD1oysiIiISd4kr0RURERGRZFCiKyIiIiKxpERXRERERGKpYHV0Bx9yaKEWLSIiIiLSqsQ1Rhs9enToEERERESkCBKX6B577LGhQxARERGRIkhcHd0lS5awZMmS0GGIiIiISIElrkT37LPPBtSProiIiEjcJa5EV0RERESSQYmuiIiIiMSSEl0RERERiSUluiIiIiISSwVrjLbw/jmFWvTn2vNQihNOOKEAkYiIiIhIqUlcrwtHHnlk6BBEREREpAgSV3XhnXfe4Z133gkdhoiIiIgUWOJKdM8//3xA/eiKiIiIxF3iSnRFREREJBmU6IqIiIhILCnRFREREZFYSlwdXRGR0CaPqukE7AQMBb4A9NmssgdNTQ4zo1OFUdHJeGxUzVnAU0DdhJnVK0LGLCJSjsw5V5AFL7x/TmEWnKY9/eg+8MADABx88MH5DqcQLHQAItJxk0fVDAKGAV+J/u4ErI5G925h1gZgDdATeBN4HHgo+vvMhJnVBT/OioiUs8QlumVGia5ImZo8qqYzcATwY2BPfMLai47/rhuAJuAj4BLg1gkzq5d1cJkiIrGUuER34cKFft7Bg/MdTiEo0RUpM5NH1fQHvgt8H+gK9Cng6urxbS1uB66YMLP6uQKuS0Sk7CQu0R07dixQNv3oKtEVKRNRgnsVcCTggB5FXP0afFWIF4AzJsysfqaI6xYRKVlqjCYi0gGTR9UYMAa4FuiOL8Utts7Ra0/gkcmjai4HfjFhZvWqALGIiJQMdS8mItJOUSnubGAq0JcwSW46w5cknwe8MHlUzR5hwxERCUsluiIibVQipbgt6QkMRqW7IpJwrZbomlk3M5tqZovNbJmZPWNm3yhGcCIipWbyqJoK4DpKpxQ3m/TS3Scmj6rZOGw4IiLFl0vVhc74/huHAxsBE4HpZjawgHEVzNlnn83ZZ58dOgwRKUOTR9V0Af4KjMV3FVYOegJfBJ6cPKpmi9DBiIgUU7t6XTCzOuAXzrkZ2aYp1V4Xyox6XRApEVGSey+wPz55LDergQ+BfSbMrH47dDAiIsXQ5sZoZtYf/1Sf5zOMO9PMas2sdtq99+Yjvrx74YUXeOGFF0KHISJlJHpk718p3yQXoAuwOTB/8qiaTUIHIyJSDG0q0TWzLsA/gNecc99tadpSLdFVP7oi0hZRw7OpwHGUb5KbbhXwKrCvnqgmInGXc4mumVUAt+APkucULCIRkdJyKr6HhTgkueAbzw0Grg8diIhIoeWU6JpZqkSjPzDaObe6oFGJiJSAyaNqtgH+QPk0PMtVd+DIyaNqjggdiIhIIeVaons1sDNwlHOusYDxiIiUhKjKwm2UbvdhHdUTuFndjolInOXSj+4A4LvAEOA9M1sevU4odHAiIgGdCuyFb8QVVz2Ba0IHISJSKO3qXiwXpdoY7amnngJgr732ync4haDGaCIBRFUWXiJ+VRYyaQDGTJhZXZpd5YiIdECbuxcrd3vttVe5JLkiEs4NxLfKQnOpKgxxaWwnIvK5xCW6Tz311OeluiIizU0eVbMjMIx4V1loritwfOggRETyLXGJ7qWXXsqll14aOgwRKV3fJ3nHxt7ABVEDPBGR2EjawVxEJKvo9v2pJKfaQrqtgb1DByEikk9KdEVE1jkOKHhD2hLVAzg/dBAiIvmkRFdEZJ0L8Lfxk6gC+ObkUTWbhA5ERCRflOiKiACTR9XsBWwTOo7AmoDTQgchIpIvnUMHUGwTJ04MHYKIlKYRJKunhUx6AkcBk0MHIiKSD4kr0d1ll13YZZddQochIqXnQJLZCK253dX7gojEReIS3fnz5zN//vzQYYhI6dGTZLyuwFahgxARyYfEVV344x//CMCwYcMCRyIipWLyqJp+QGXoOErEKnzS/3boQEREOqpgie7gQw4t1KJFRPJtT6AB2Ch0ICWgF74/3btCByIi0lGJK9EVEclgL3w/skF84/tHM6BqED369mRV4yref/UdHrp1Lh8sei9EOJ2A4SFWLCKSb4mroysiksHeBGyI1nezjXjz+cU898ACVixrYNCeOzDyJ2NChQPwxZArFxHJF5XoiohA35Arv2PizZ//v/ngLTj5sjPps0lfKjpV0LS2KURI3UOsVEQk3xKX6P7qV78KHYKIlJ5g1RZS9vjG3myy7aYMqBoEQO1dj4VKckH9CYtITCQu0R08eHDoEESk9AQvwdxpv53ZbreBAHz20ae8/dIbIcNRoisisZC4OroPPPAADzzwQOgwRKS0rA0dwB0Tb2bKmF8x8zd/offGffjmhG/Rd7NgnUAEK0oWEcmnxJXoXn/99QAcfPDBgSMRkRLSEGrFnbt2Zu2atbgmx9rVa1n09GusXrGKbr26s1H/jfnsw09DhLU6xEpFRPItcYmuiEgGjaFWvOWOW3Pk+GN48/nFrKxfwda7bEe3Xt2p/7Se9xe+GyqsVaFWLCKSTwVLdBfeP6dQi/6cHkohInnyOuAAK/aKl3+yjCXvfMzAIYPp2r0bDZ/V89Ijz/Of6Q+xqmFlscNJ+TjUikVE8kkluiIi8BhwMtCn2Cv+5J0l63UvViL+EzoAEZF8SFxjNBGRDJ4iQGluiWoA5ocOQkQkHxJXonvZZZeFDkFESs//UJdaKWvwib+ISNlLXInuVlttxVZbbRU6DBEpIRNmVjcBL4eOo0T0AP4bOggRkXxIXKJ7zz33cM8994QOQ0RKz3x8g7Ske2PCzOpgreBERPIpcYnubbfdxm233RY6DBEpPY8Ay0MHEZjDfw4iIrGQuERXRCSLu0lgu4Vm6oGpoYMQEckXJboiIsCEmdXLgNvxjbGS6mPg4dBBiIjkixJdEZF1riC5TwWrByZPmFmtesoiEhtKdEVEIhNmVj+H72osiSqAW0IHISKST4mrj/bHP/4xdAgiUtp+B1xLgKekBbQG+MuEmdWfhQ5ERCSfEleiW1lZSWVlZegwRKR0/Q1oCh1Eka3GV9sQEYmVxCW6d955J3feeWfoMESkRE2YWb0K+CG+zmoSrAD+PmFmdV3oQERE8i1xie6MGTOYMWNG6DBEpLTdCDyJL+mMuwbgrNBBiIgUQk6JrpndambvmtlnZvY/Mzu90IGJiIQS9TzwHSDuTwhrAE6cMLN6aehAREQKIdcS3d8AA51zfYFvAr80s70KF5aISFgTZla/DXyf+FZhWAncNWFm9ezQgYiIFEpOia5z7nnnXKpkw0Wv7QsWlYhIabgRX4Uhjg+RqEdVFkQk5nKuo2tmfzKzBuAl4F1gg1IAMzvTzGrNrHbavffmMUwRkeJLq8KwHH+BHxeNwLdVZUFE4s6cy/3YbWadgK8ABwK/c85lbaix8P45BT8pDD7k0DbP09jYCECPHj3yHU4hWOgARAQmj6rZHf9o3Dj0rdsAnD1hZvWNoQMRESm0NvW64Jxb65ybD2xDmd7y6tGjR7kkuSJSIibMrH4WOAyfJJazBuBnSnJFJCna271YZ8q0ju4tt9zCLbfoKZci0jYTZlY/CnyD8m2c1gBUT5hZfXnoQEREiqXVRNfMNjez482st5l1MrOvA2OBuYUPL/9mz57N7NlqZCwibTdhZvVDwHDgU8qrzm4jcO6EmdWXhQ5ERKSYcinRdfhqCm8BnwCXAuc55/5eyMBERErRhJnVTwH7Aq9S+lUZVuGT8jETZlb/OXQwIiLF1mqi65z70Dk33DnXzznX1zn3JefcdcUITkSkFE2YWf0SsBtwOb60tBRLd+uBfwE7TphZfU/oYEREQugcOgARkXI0YWb1KuBnk0fV3An8FdgK6Bk2KsCX4jYCp0+YWX1n6GBEREJqb2M0EREBJsysXgDsyrrS3azdLhaYY/1SXCW5IpJ4bepHty1KtR/dMqN+dEXKyORRNTsCPwBOwSeevYuw2hX4Y8Uc4NIJM6vnFWGdIiJlQYluaVOiK1KGJo+q6QkcD/wEX6WhB/m/g7YMX3r8B+DaCTOr383z8kVEyl7iEt3rrvPt6M4444x8h1MISnRFytjkUTUG7A2ch3+i5Kb4nhq6A93asKi1+GoJXYGVQC1wFXDPhJnVa/IXsYhIvCQu0R07diwA06ZNy3c4haBEVyRGJo+q2QjYA9gL3x/vUGALoMmMTqnp0g7LnwLPAvOAJ4GnJsysfqeIIYuIlDX1uiAiUiQTZlZ/CjwYvS4DmDyqpgLoNuSLmzc0NTnMjIoKqDDjoF9+t1+wYEVEYkCJrohIQBNmVjcBjQ9OvJZ1ZboiIpIP6l5MRERERGIpcSW63bt3Dx2CiIiIiBRB4hLdG264IXQIIiIiIlIEBUt0E9DHrYiIiIiUsMTV0b3yyiu58sorQ4chIiIiIgWWuET30Ucf5dFHHw0dhoiIiIgUWOISXRERERFJBiW6IiIiIhJLSnRFREREJJYS171Yv379QocgIiIiIkWQuET36quvDh2CiIiIiBSBqi6IiIiISCwVrER34f1zCrXoDrn+bzMBOP2YUQVbhx6WISIiIhJe4qouvLBwYegQRERERKQIVHVBRERERGJJia6IiIiIxJISXRERERGJpcTV0d1s436hQxARERGRIkhcovvTceNChyAiIiIiRaCqCyIiIiISS4lLdP90xx386Y47QochIiIiIgWWuKoLr775VugQRERERKQIEleiKyIiIiLJoERXRERERGJJia6IiIiIxFLi6uhu03/z0CGIiIiISBEkLtEdf+KJoUMQERERkSJoU9UFM9vRzFaY2a2FCkhEREREJB/aWkf3j8CThQikWKbccgtTbrkldBgiIiIiUmA5V10ws+OBpcCjwA6FCqjQ3nr/g9AhiIiIiEgR5FSia2Z9gRrg/FamO9PMas2sdtq99+YjPhERERGRdsm1RHcSMNU596aZZZ3IOXctcC3AwvvnuI6HJyIiIiLSPq0mumY2BDgE2KPg0YiIiIiI5EkuJboHAgOBN6LS3N5AJzPbxTm3Z+FCK4wdtt0mdAgiIiIiUgS5JLrXAn9Je/8jfOJ7ViECKrTvHXdc6BBEREREpAhaTXSdcw1AQ+q9mS0HVjjnPixkYCIiIiIiHdHmJ6M55y4uQBxF85upUwH46bhxgSMRERERkUJK3COAP/xkaegQRERERKQI2vpkNBERERGRsqBEV0RERERiSYmuiIiIiMRS4uro7jJ4cOgQRERERKQIEpfonn7MqNAhiIiIiEgRqOqCiIiIiMRS4hLdi6++houvviZ0GCIiIiJSYImruvBZfX3oEETaZX7N1M2BvYDtgR5A9+i1EmgEVgCLgKeGVY97N1SckpuqAcMr8ftzJ6DHEUOqWLVmLZ07VdClUye6dOrE9wcMHwU8BbxZt3ieCxqwtGhJXW1fYE9gZ6An/jfaDViD/22uAN7F78+FlVVDtT9FiiBxia5IOZhfM3VTYO/odSCwO9ALf7LsBnTC/34NcPiT6VpgFdB1fs3UlcCzwDzgSeCJYdXj3i/uVkhK1YDhGwH7AEOB4fiEaCP8BUpXoPO9C+o+n77CjAozgBuBLsDaqgHD/ws8BDwOPFG3eN7bRdwESbOkrrY3fl/uhd+fQ4FNgQai/Yn/jabumqZ+nyujcbakrvYF/O/zceDJyqqhi4q5DSJJoURXpETMr5lq+KR2AnAQ/qTYi/V/p90yzGr4ZKgLvoSX6O9Xgf2Benzy+yhwCTBnWPW4pgJsgqSpGjDc8Mntj4Aj8fuzJ34/pXTNNG+TczQ5B9A3bfBXgC8Dy4GuVQOGL8Dvz7vrFs9bk+/4ZUNL6mqrgB8Cx+EvKruz/m9yoyyzdo5e6dPuBeyB359dltTVvgr8DrizsmroyjyHLpJY5lzB7p6U5G2ZsWPHAjBt2rTAkeTEQgcghTe/ZupGwMn4BLcfPrkt1L5fhk98LwemDqse93GB1pNYVQOG9wTGAhcAW+FvYReqPcQyYDVwJfD/6hbPU5WVPFtSV9sNOAa/P3dkXYltISyL/l4LXFVZNfT1Aq1HJDESl+heeeWVAJx77rmBI8mJEt0Ym18zdRNgMnA80IRPcIulAZ98zQJ+OKx63HtFXHcsVQ0Y3hv4JXAGfn/2LuLqV+CPF3OA79ctnqfb4B0UJbgXAufhP9s+RVz9Svw59DHgnMqqoc8Xcd0isZK4RLfMKNGNqfk1U4/G179MNVgJZRX+pPpd4C/Dqsfpd9sOVQOGHwRMw1c16BEwlDX4fToBuKZu8TxVUWmHJXW1Q4HpQH98dZNQmvC/z18Dv62sGqoqKiJtpES3tCnRjZmoFPc64OuEPYE2Vw88Apys0t3cRaW4VwDfJmyC21w98DxwvEp3cxeV4k4CzsHXvy2VY3A98AbwLZXuirRN4hLdU089FYAbbrghcCQ5KZWDrORBCZXiZqPS3TYooVLcbFS62wYlVIqbjUp3RdohcYmuGqNJsc2vmVoBXA18h9I8gTZXD9wNnDisepxOps1EvSn8Ajif8tmfjwFH1S2e1xg6mFK0pK72LOAySqsUN5t64GXg4MqqoUsDxyJS8hL3ZDSRYppfM7ULcAdwAuWRFIFvFPdN4J75NVO7tzZxklQNGF4BXAWMp7z2537AQ1UDhvdtbeKkWVJX+zPgUnypfKknueD3567AE0vqavuHDkak1CnRFSmQ+TVTuwL3AodT3B4V8qEnvh/eufNrppbibfmii5Lcm/FdwZXb/uwBfAl4rGrA8I1DB1MKltTV2pK62kuBn1I+Fy0p3YCBwFNL6mq3DhyLSElToitSAPNrpnYC/op/YEO5nURTegBDgHujkunEiqor/AkYSfkluSndgMHAg1UDhpfrNuTTROD/KN/fZxd8feJHltTVbho6GJFSVbA6ugvvn1MydXQHH3Lo5/9fd911AJxxxhmhwmmLcriNJs1ETzi7ETiW8j2JpmvA9886elj1uLWhgwmhasDwXwPfp3yT3HQrgKeAg+sWz0vkE7iW1NWeg38KWRx+n6uA14B9K6uGfhY6GJFSk7gS3TPOOKNcklwpX98DRhOPkyj47TgUf4s3caoGDB8F/IB4JLngG1ztie8WLXGW1NUOwz86OS6/z674kvpbQgciUooSl+iKFNL8mqmD8CfRuCRFKT2BC+fXTN01dCDFVDVg+KbAn4lPUpTSAzipasDwA0IHUkxL6mp74huHxq3eeTfgkCV1taNDByJSahKX6I4dO/bzLsZE8inqRuwvlGYfufnQDfjr/JqpnUMHUkTXE7+kKKUncEfC6uteCsS1MV5PYOqSutrNQgciUkoSl+iKFND/4bv96RQ6kAKpALYDLggdSDFUDRh+DL7KRlwvXAD64ZO/2IuqLJxCfC9cwG/b9aGDECklSnRF8iCqsjCZ+FVZaK4XcFHcqzBEVRamEr8qC80logpDjKssNNcVOFhVGETWUaIrkh+3Eu+Sv3TdgDui3iXi6mrinxSlpKowdA0dSAH9mvhWWWiuF74KQ7/QgYiUAiW6Ih00v2bqnvj+ZuNaZaG5CmAAMDx0IIVQNWD4tsCRJOfCBaA3cEzoIAphSV1tX+BMknPhAr6P3VNDByFSChKX6B5++OEcfvjhocOQePkhyUqKwJcaTQgdRIF8j+T1Yd2H+Na9PgloCh1EkfUEfrSkrjZx53iR5hL3wIgyk7STbdmZXzO1H/Auvm/SpFkJDB5WPe6d0IHkS3T7/gNgo9CxBNAA7Fu3eN5/QweSL0vqag14Hd+IMmmWAcdUVg29P3QgIiEl7mqvsbGRxsbG0GFIfJxC8kqLUhy+p4k4GUUCj4uRrsB5oYPIs68ClaGDCKQ38b3rIpKzxB3QTzvtNE477bTQYUgMRI2xJhD/lvnZdAfOmV8ztUvoQPLoAvxt/CTqDIytGjC8b+hA8mgC8e8JJRsDvrqkrnab0IGIhJS4RFckj0aQ3KQopTNwVOgg8qFqwPBdgS+EjiOwJuDE0EHkw5K62i2Ag0l2FTDD1zkXSSwluiLtdzjJLS1K6QN8M3QQefI1ktNzRja9gLj0wXoQsDp0EIF1w1fHEUksJboi7fdV9BsC+EroAPLkqySv94xMdg8dQJ7si6+nmnTbL6mrTdJju0XWk9NJ2sweNLMVZrY8er1c6MBESllUP3eX0HGUiEHza6bG4WEDe4cOoET0qhowfPPQQeTBASS72kLKCnSskgRry1XeOc65sn+G9ujRcbkrJ4ENCh1ACWkEdgWeCR1Ie1UNGN4HiENylw8rgL2Af4QOpL2i/mO/GDqOEmH4/VkXOhCREBJ3O+PYY48NHYLEw17AmtBBlIhO+M+jbBNd/JPtGkhm/7nN9QKGUsaJLrA9ye32r7newH7ADaEDEQmhLfULf2NmH5nZI2Z2YKYJzOxMM6s1s9pp996blwDzbcmSJSxZsiR0GFL+SqL+36a7DmZY9TiGVY9j0Ne+HCqMXsCwUCvPk6EEfOjH1L9cQd3ieeu9/nZfsLykM74hVzkbSuBEt0vffvTdYWc23m1P+u06hD7bfwHrFKytY7n/PkXaLdcS3QuAF4BVwPHA3WY2xDn3WvpEzrlrgWuhtJ6Mlu7ss88GYNq0aYEjkTK3D4EbonXt05MdDt+PprVNVHQK3iZur9ABdNA+lEBDtFun/vXz/z/84OOAkZR9nc4qAl6Idu1XSe/tBuOamlj12VJc01o69+gFVgGsDRHS4BArFSkFOSW6zrnH097eZGZj8V0rXVmQqERKX/D+c3caOZyVyxpo+OATNtst+HkseOl2B5VElYVLaq4KHUJKj9ABdNDGBGyI1mOLrQFYtugV1tQvCxVGuq5L6mqtsmpoSRZAiRRSe+voOtSaVZItaCKw1Zd3pe+2/Xl26l1s9eVdQ4YCQOdunQcuqast25Po7rsO5tnnF4YOg/l19wDw4nP/44rfXsvzdS+FCqVv1YDhZbs/Lxz/bb52UJibDBVdu9Gpazdc01q6b7YFXQbuQNOa1az46H1WfvxhkJjwxcjd8Q1HRRKl1fudZtbPzL5uZt3NrLOZnYDvb/JfhQ9PpGQFu83dc7ONGXjwUBY/+DT175dGfXPXVLY5EQArV4V9rkBDfSPz7n+Uf949l3fffp8v778X19wymU02qwwaV7nq3i3cU6krOvvyI6voRKeuXVn16SdUdOlKr60H0KVvv1BhpRJdkcTJpUS3C/BLfFcta4GXgJHOOfWlK0kWrKHLJjsPxDp1YqMBW7DRdv3p1d8nQ5t8YTua1qxl8dza4gdV5vd3KirC1nE+d9xPP/+/c5fO3P3vW9l62y3Z5yt78I+7HggYWXlqCnjh1bRmXWcsy99cxNrGBlxTE9033Zyuffux+rOloUILUjlYJLRWE13n3IfEqCP1E044IXQIEg8rQq3YDMyMyh23XW9494370nebMF3BWuBEsaO6BSwB7N69G3369s7Y+GztWvWQ1R6NK1YFW3fT6lU0rV1DRacNT6+uKViu2ZmAxyyRkBLXj+6RRx4ZOgSJh2AnjTfmPcMb89Z1WbvjNw+g/5CdePux51h03+MtzFk4VlHeRbrduoZLdCs33Zi75t7CE/95hnfeeo/d99yVrbfdko8++JgnHn06WFzlbMXKleFW7hwrP/qAHv23ove2g1jTUE/XfpU451j5SbCqRgaErZ8jEkjiEt133nkHgK222ipwJFLm3g8dQClZ3bDq8cqqofuGjqO9Hqs9/xrguyHWvfSTT7n7b/exz357MHTfISz/bDkP/PNhrrr0epZ+8mmIkABer1s8r2yf/rekrvYi4Of4qndF1/j+O2BGt403oWu/jVm7opHG999hbWN9iHAAlqvHBUmqxCW6559/PqB+dKXDHgIOJtCJNN0rdz3MK3c9HDKEJvznUc4eA04gQDdpDfWN/OInk4u92tY8ETqADnqKwE+6a3zvbRrfezvU6pv7b+gAREIp74p1IuHU4k+kAsuJR2KkEi+vEQh65ZQHT1H+fQHnSxwuREXaTYmuSPs8jU6kKZ3xiUU5exF1v5SymjLfn5VVQz/EX4BJPC5ERdpNia5IOwyrHvcxEKwCZYlxwOuhg+iIusXz1gCvho6jRPQEng0dRB480/okidCFMr9wEekIJboi7acTqffCsOpxcbjt/0joAErEO3WL58WhWs481NMA+P5z3wwdhEgoiWuMdvrpp4cOQeLjQeBAoGvYMIJai08o4uAR4HgCNEgrMY+FDiBPnsTXNw7eYDSwOvW4IEmWuBLdgw8+mIMPPjh0GBIP09HThlYCt4UOIk/uJoEX/80sA24IHUSe/Juyf2Zfhy0HrgsdhEhIiUt0Fy5cyMKFC0OHITEwrHrca6ju28Jh1eMWhA4iH+oWz/sY+DsBH+9cAhqA+0IHkQ+VVUNXAtcC4R6TVhruCB2ASEiJS3QvuugiLrrootBhSHxcgi8FS6JlwO9CB5Fnl+FvdydRAzClbvG8OCX6V5HcC5dVwA2VVUOT+n0WAQp4m27wIYcWatEipWQ2/vZ9n9CBBHJn6ADyqW7xvCerBgx/C/hC6FgCqACmhg4inyqrhr6+pK72MXxd+qRZC/whdBAioSWuRFckn4ZVj1sLXEHySgFXAdcNqx63InQgBfA7ktcHaxNwV1R9I26Setfl6cqqoeoyTxJPia5Ix11H8hq9NOFvC8fRHSRvfzYCU0IHUSD/InlPMVwOlNxzpUVCUKIr0kHDqsd9AFxNck6mjcC0YdXjFoUOpBCiPmSrgfrQsRTJKnzXarF8elZl1dAm4DySsz+bgNeAe0IHIlIKEteVztlnnx06BImnC4FjgAGhAymCZcAPQgdRYFcA3wGqgE5hQym4lcDJdYvnxbmv1TuAk4ERxL/f65XAmMqqoUnv+lAEAHOuYMe2OB80iyVpt0/L2vyaqfvgHyLRI3AohdQAfHNY9bgHQgdSaFUDhu+IfxRunPdnPXBm3eJ5t4cOpNCW1NX2B14h3g1H64GLK6uGXho6EJFSkbiqCy+88AIvvPBC6DAkhoZVj3sCuIb4VmFYAdyRhCQXoG7xvFeAnxHfW96pKgvTQgdSDJVVQ98HziS++7MJn8hfHjoQkVKSuER30qRJTJo0KXQYEl8XAh+FDqJAPiP+VRaauwL4H/F8Al4Sqiw0dwfwMPF8iMRK4DhVWRBZX+ISXZFCirrb+hbxK9VtAMYOqx6XqG6aoocnHEf8uo9rAE6vWzzvvdCBFFNl1VAHnIIv1Y1Tgt8AXFhZNfR/oQMRKTUFq6O78P45JXkQGX/pZQBM+dH5gSPZUIaHbKiObpmaXzN1FHAb8ajf2QicNax63E2hAwmlasDwrwL/AHqGjiUPGoCausXz4vZUu5wtqautAuYTj/q69cCfgR9EibyIpFGJrkgBDKseNxP4HuVfstsA/DTJSS5A3eJ5DwFjKP+S3XrgqiQnuQCVVUPrgK8Tj9/nTJTkimSlRFekQIZVj7sR339nuZ5MG4CfD6se9/vQgZSCusXz7gW+Tfkmuw3AtcBPQgdSCiqrhv4HOJzybZxWD8wCTlGSK5Jd4qouPP/aawDsuv32gSPZkKouxNP8mqnfAm6ivKoxNALnDqseNzV0IKWmasDwQ/AJRq/AobRFA/DrusXzfhU6kFKzpK52KHA/0JfyOebWAzcA31eSK9KyxJXo7rr99iWZ5Ep8Dase91fgaGApvmV0KVuNf3zot5XkZla3eN79wMHAB/gu10rZWnyS+30luZlVVg2tBb4CvEHp331x+IvQX6IkVyQniUt0n3/ttc9LdUWKZVj1uDnAjsA/Kd2TaT3wELDTsOpxswLHUtLqFs97HNgB3wdtKe/PZ4CqusXzdNHSgsqqoS8CX8Q/yruR0uyRoR7f1d2+lVVDf6skVyQ3iau6oF4XJLT5NVOPwbeS7g50CxwO+FLclcBZwG3DqseV5G+3VFUNGH4wPuHtg9+noa3F78+f4hueNQWOp6wsqavdB5gObEZp9LLh8HcOLgF+VVk1dHXgeETKSuJKdEVCG1Y97m/40sB/Er4/z+WsK8W9VUlu29UtnvcAsD3rSndDJpbLWVeK+wcluW1XWTX0CdaV7jYQ9mEh6aW4FyvJFWk7JboiAQyrHvfRsOpxI/F1dx/Al9gUq/7uqmh984HjgUOHVY97t0jrjqW6xfOW1S2edxq+7u7d+M+3WL0zrInW9QxwOvDlusXzVD+rAyqrhq6orBr6I3zd3b/g92exqqisxSe4LwPnAl+KukMTkXZQ1YUSoqoLyTW/Zup2+H53z8Lv90J0ZL8sWvZU4Mph1eOUDBVI1YDh/YEz8Y9M7kph9mc9vrDiNuD3dYvnPVeAdQiwpK52Y/wT1X6E35e9yf/xuQG/P2cBUyqrhj6Z5+WLJJIS3RKiRFfm10ztChwDjAeq8PVnwXdl1dbvw3J8tYjuwPPAFcD0YdXjyrUf2LJTNWB4Z3xfrecD++BL65poX6JUH83bHXgV+ANwW93ieYl6LHNIS+pqK4AR+IR3GH4frsHvz7beIW3E/757AouBa4A/V1YNXZK3gEUkeYnuq2++CcAO224bOJINKdGVdPNrpnYCvgDshb+FOgzYCZ/srMUnsanfWUX06gK8AjwCPAo8Bbw4rHrcmqIGLxuoGjC8Al+Xdy9gX+CrwM7R6FQCnGl/LgL+g9+nTwHP1S2et6p4kUsmS+pqDRiA359fBoYDuwKd8clvpv3ZFXgTeBx4GKgF6iqrhpZ6N3UiZStxiW4pU6IrrYmS3/74Ur1Urw2r8KVDK4APlNSWjyj53Rz/MJHUPl3Nuv35oZLa8hElv6neGlL7dDV+X64APlJSK1JciUt0n3rhRQD22mXnVqYsPiW6IiIiIvmTc50iMzvezF40s3oze83MDihkYIVy2+zZ3DZ7dugwRERERKTAOucykZkdCvwOOA54AtiykEGJiIiIiHRUToku8Augxjn3WPT+7QLFIyIiIiKSF61WXTCzTsBQYDMze9XM3jKzq8ysR+HDExERERFpn1zq6PbHd3FzLHAAMATYA/hZ8wnN7EwzqzWz2mn33pvPOEVERERE2qTVXhfMbGNgCXCKc+6maNho4GfOuT2yzVeqvS68+d57AGy7xRaBI9mQel0QERERyZ9W6+g65z4xs7dY1/F1WSvFBFdERERE8i/X7sVuAM41s82jEt7zgHsKFlUB/efZZ/nPs8+GDkNERERECizXXhcmAZsC/8M/3WU68KtCBVVIf51zPwBf2X33wJGIiIiISCHllOg651YD34teIiIiIiIlL+cno4mIiIiIlBMluiIiIiISS0p0RURERCSWcm2MFhs/Oe3U0CGIiIiISBEkLtHdvLIydAgiIiIiUgSJq7rw7yef5N9PPhk6DBEREREpsMSV6N497yEADtp778CRiIiIiEghJa5EV0RERESSQYmuiIiIiMSSEl0RERERiSUluiIiIiISS4lrjPbz7343dAgiIiIiUgTmnCvUsgu24ASx0AGIiIiIlKvEVV248847ufPOO0OHISIiIiIFlrhEd8aMGcyYMSN0GCIiIiJSYIlLdEVEREQkGZToioiIiEgsKdEVERERkVhSoisiIiIisZS47sUaGxsB6NGjR+BIcqLuxURERETaKXGJbplRoisiIiLSTomrunDLLbdwyy23hA5DRERERAoscYnu7NmzmT17dugwRERERKTAEpfoioiIiEgyKNEVERERkVhSoisiIiIisaREV0RERERiqWDdi5nZmc65awuy8A4q1dhKNS4RERGRclTIEt0zC7jsjirV2Eo1LhEREZGyo6oLIiIiIhJLSnRFREREJJYKmeiWcl3TUo2tVOMSERERKTsFa4wmIiIiIhKSqi6IiIiISCwp0RURERGRWGpzomtmh5nZy2b2qpn9JMN4M7M/ROPrzGzPXOftKDPb1sz+bWYvmtnzZvaDDNNMMLMF0es5M1trZpXRuD+b2Qdm9lye43rdzP4brbM2w/gTos+qzsweNbPd08b9MNqW58xsmpl1z2dsIiIiInHVpjq6ZtYJ+B9wKPAW8CQw1jn3Qto0hwPnAocDXwZ+75z7ci7zdpSZbQls6Zx72sz6AE8BI7Otw8yOAn7onBsRvf8qsBy42Tm3Wx7jeh0Y6pz7KMv4/YAXnXOfmNk3gIujz2xrYD6wi3Ou0cymA7OdczfmKzYRERGRuGprie4+wKvOuYXOuVXAX4Cjm01zND5RdM65x4B+UQKay7wd4px71zn3dPT/MuBFYOsWZhkLTEub/yFgST5jyoVz7lHn3CfR28eAbdJGdwZ6mFlnoCfwTrHjExERESlHbU10twbeTHv/FhsmktmmyWXevDGzgcAewONZxvcEDgNmFCqGNA64z8yeMrPWnn42DvgHgHPubeBS4A3gXeBT59x9BY1UREREJCbamuhahmHN6z5kmyaXefPCzHrjE9jznHOfZZnsKOAR51wxSnD3d87tCXwDODuqIrEBMzsIn+heEL3fGF/qPQjYCuhlZt8pQrwiIiIiZa+tie5bwLZp77dhw1vp2abJZd4OM7Mu+CT3Nufc31qY9HjSqi0UknPunejvB8BMfDWO9ZhZFXA9cLRz7uNo8CHAIufch8651cDfgP2KEbOIiIhIuWtrovsksKOZDTKzrvhk8a5m09wFnBT1vrAv/nb7uznO2yFmZsBUfMOuKS1MtxEwHPh7PtefZV29ooZxmFkv4GvAc82m2Q6fxJ7onPtf2qg3gH3NrGe0bQfj6x2LiIiISCs6t2Vi59waMzsH+BfQCfizc+55M/u/aPw1wGx8jwuvAg3AqS3Nm7ct8fYHTgT+a2YLomEXAtulxQcwCrjPOVefPrOZTQMOBDY1s7eAnzvnpnYwpv7ATJ+n0hm43Tn3z2afWTWwCfCnaLo1zrmhzrnHzexO4GlgDfAMekywiIiISE70CGARERERiSU9GU1EREREYkmJroiIiIjEkhJdEREREYklJboiIiIiEktKdEVEREQklpToioiIiEgsKdEVERERkVhSoisiIiIisaREV0RERERiSYmuiIiIiMSSEl0RERERiSUluiIiIiISS0p0RURERCSWlOiKiIiISCwp0RURERGRWFKiKyIiIiKxpERXRERERGJJia50mJntYma17Zz3+2b22xbGDzQzZ2bLzezM9kcpZrZT9DmuNbPTQ8cjIpKi80h5KMfziBLdLMzsdTM7pNmwU8xsfqiYCsHMLjazWzu4mEnApWnLvMLMPjGz/5jZ1mnDTzCz3zeb91rgO2a2eSvr6Oecu7ZZ7IPMrMnM/tTB+AvOzH5oZu+Z2adm9mcz69bCtEPM7Ckza4j+Dkkbd3I07DMze8vMLjGzzmnjHzSzFdGBaLmZvZwa55z7n3OuN/BwobZTpJyY2bfNrDb6rbxrZv8ws2HRuIuj5OhbadN3joYNTBu2n5nNNbNl0e/7bjPbJRp3QtpvsTE6XqXeLy/6BheIziPF0cbzyFFm9lz0XXs09Z3MZVlmtnP0nf7UzF41s1GpceV4HlGiG4iZdWr2vnO2abPM3+r0bV1me5jZlsBBwKzo/T7AXsAWwHzgp9HwjYAfAdXp8zvnVgD/AE5qx+pPAj4Bjm/pBx+amX0d+AlwMDAQGAz8Isu0XYG/A7cCGwM3AX+PhgP0BM4DNgW+HC3zR80Wc45zrnf0+kJeN0YkJsxsPHAF8GugP7Ad8Cfg6LTJlgA1zY/Xacv4CnAf/je7FTAIeBZ4xMwGO+duS/0WgW8A76T9NnvnYRt0HkHnkQzT7gjcBvwf0A+4G7grtS9bWlY0zd+Be4BK4EzgVjPbqSAbVgRKdPPIzL5oZnPMbImZvWxmY9LG3WhmV5vZbDOrBw4yX2p8gZnVAfVRacE3zex5M1salc7tnLaMDabPEIMzs7PN7BXglWjY783sTfOlgE+Z2QHR8MOAC4Hjoqu+Z6PhG5nZVPMlHG+b2S+zHeiBQ4GnowMN+AP9fOfcSuAB/A8I4FfAZOfcpxmW8SBwRE4f8vpOAn4GrAaOimI/3prd/oquXO+K/t8kKnH5zMyejLat0KX0JwNTnXPPO+c+wZdcnJJl2gOBzsAVzrmVzrk/AAaMAHDOXe2ce9g5t8o59zb+YLZ/geMXiZUoYaoBznbO/c05V++cW+2cu9s5NyFt0n8Cq4DvZFnUJcDNzrnfO+eWOeeWOOd+BjwGXNzO2HQe0Xkkk7acR74OPOycm++cWwP8DtgaGJ7Dsr6Iv2i73Dm31jk3F3gEOLEA21QUSnTzxMx6AXOA24HNgbHAn8xs17TJvo3/ofbBX6USTXcE/qprMDANX2K3GTAbuDutNG+96aMvcCYj8aV9qVsVTwJD8FdntwN/NbPuzrl/4ksz7ohKGHaPpr8JWAPsAOwBfA3IVhfnS8DLae+fBw4wsx74q8XnzWwo8AXn3O1ZlvEisHuWcRlFB9ltgL8A01l3JX8X8IXoijbl2/jtBvgjUI8vKTg5euW6zmHRiSPba1iWWXfFl/KkPAv0N7NNskxb55xzacPqouGZfBX/maf7jZl9ZGaPmNmBrW6YSPJ8BegOzGxlOgdMBH5uZl3SR5hZT2A/4K8Z5puOT97aROeRz+k8sqG2nEcsejV/v1sOy0qfL33+3TIMLwtKdFs2K/0LiL+tlc2RwOvOuRucc2ucc08DM4Bj06b5u3PuEedcU9qV6x+cc2865xqB44B7nXNznHOr8fWVeuAPpmSYPpvfRCULjQDOuVudcx9HcV0GdAMy3tI2s/74W2znRaUcHwCXA8dnWVc/YFnqjXPuuWi7H8PfCvwd8Hvg++YbDDxkZreZWb+0ZSwDNmphezI5GfhHdDV6O/ANM9vcOdeAv+0yNtqeHfFXqHdFpQmjgZ875xqccy/gD8Y5ia6O+7XwynZF3xtIL4FI/d8nh2lT028wrZmdCgwlrV4bcAH+RLc1vt7a3Wa2fasbJ5IsmwAftZDkfc45dxfwIRsmaZX4c+i7GWZ7F1+9qK10HkHnkSyztuU8MgcYbmYHRhc4FwJd8VXfWlvWS8AHwAQz62JmX8OXBPekTCnRbdnI9C8g8L0Wph0AfLlZYnwC/oov5c0M86UP2wpYnHrjnGuKxm+dZfps1pvGzM43sxfNVyxfij8YZDsIDwC6AO+mbcf/w5cuZPIJzX5ozrnLnXO7O+eOwx90H8Z/187EX52/iK8flNKHDZO7rKKr/G/hb9vjnPsP8Ab+ihv8AWts9P+3gVnRgWszfLWA9M8nl8+zo5YDfdPep/5flsO0qenXm9bMRgK/Bb7hnPsoNdw593h0C3Wlc+4m/C2nwzsWvkjsfAxsmum2fRY/Ay7ClwKnfAI0AVtmmH5L4KMMw1uj88i6uHUeWV/O5xHn3Ev4JP4q1l10vQC81dqyooujkfgS//eA8/Gl3W9RppTo5s+bwLxmV2a9nXNnpU3jMsyXPuwd/AECADMzYFvg7VaWkXWZ0a2ZC4AxwMZRwv4p625PNF/em8BKYNO07ejrnMt267wOyFhJPbqq/y6+Ltxu+Fvyq/G3wKrSJt2Z9W+jtGYU/of5J/OtRt/DH8RTt53uw5/EhuAPVKnbTR/ib6Vtk7asbXNdqZkdYGktpjO8Dsgy6/Osf0ttd+B959zHWaativZ9ShVp1RPM14m7DjjKOfffVsJ2ZL4VJZJk/wFW4E/orXLOzQFeJa2wwzlXHy3nWxlmGYOvW9pWOo80o/PI59pyHsE5d6dzbjfn3CbAz/HfiSdzWZZzrs45N9w5t4lz7uv4u4RP5LqNpUaJbv7cA+xkZidGxf1dzGxvS2sEkIPpwBFmdnBUH+x8/MHi0Q7E1Qf/o/wQ6Gxm1ax/Jfc+MNDMKgCcc+/if+CXmVlfM6sws+3NbHjzBUfmAHuaWfcM46YQ3d4BFgF7m1lvfIOrhWnTDce3mM3VycCf8fW6hkSv/YEhZval6HbkncBk/O3FOdG2rQX+BlxsZj3N7Iu0oZWu843Aerfwytbdys3AOPP9RG6MLx26Mcu0DwJr8bfoupnZOdHwuQBmNgJfAjHaObfegcfM+pnZ182su/kGKSfg6/D+K9dtFEkC5xszVQN/NLOR0fGgi5l9w8wuyTLbRcCPmw37CXBydDu9j5ltbGa/xNcBztgivhU6j2xI5xGvLecRzGwvM+tkZpvhS9Pvjkp6W12WmVVF55GeZvYj/B2KrOsqdUp088Q5twxf2f54/BX1e/h6RTl3V+KcexnfuvdK/G2vo/Cldqs6ENq/8D/+/+FvZ61g/dssqYYUH5vZ09H/J+Hr87yAv6V0J5lvz+Gcex+fhKV3yYOZHYRv6DAzmu4J4N5o3Qfhb7sTHdgOJ8c6Tub7UzwY3yvBe2mvp/AtpFONAm4HDgH+6tavh3cO/pbbe8At+EYbK9OW/3yUIOaN8401LgH+jd8Hi/FX2Kl1/sPMLoymXYUvZToJWAqchq9Ck/oOTIzin51WApA6uHcBfok/GX0EnBvNm97IQ0QA59wUYDz+JP8h/th0DlEXVxmmf4RmpVpRfcqvA8fgbxEvxje8Guace6UdMek8kkbnkXXach6J/B5/Dnk5+ntGrsvC97DwLr6u7sHAoc73gFGWzLnmdxxE2sZ8R9Q3Afu4Nn6hzOxcYFvnXPOSktT4Afgf6gpggnPuuo7G22z5vwO2cM7l3Gq2XJlvUPEk/uTzPefcjWEjEhHxdB4pD+V4HlGiK4kS3WbqCvwX2Bvf9c7pzrlZIeMSEZHyoPNIeSn4E09ESkwf/G2mrfC3ZS7DdyMjIiKSC51HyohKdEVEREQkltQYTURERERiSYmuiIiIiMRSwero9ulR6dY2rY7ereuvfr2e6y3L8OidbTgIsJantfQxmfvJ/3wOaz4s+5yWOZgMcWZba9q0Wbe7LTE2i9SaD880cYbPlebblmX5zWbMvPwN93Sm/ZJxkK0/ZIPJW1p3s5VkG9/SMv3b1j/EzNvTLO7WvwDZZt9w8pYWmCncnD/w9ElzWX7aNmb+IraykvRB2XZIrvEYz77w4r+cc4dlWYKUoGznhPXetXhszHZMyXxOyDRtS0fn5sfc1s4J64fbwjkhmrDFY721Mn+G+LJN29LvK1NcGZfRjnNC5uW3cE5obf4Mx75s82Y7H6X+yfmcsMFy23IczT5Ri+eFtJG5nBfaf07INkGWt5m+BC1t44ZfxMwrybjeVs4JrcbT8jmhYInu2qbV7LjlXj4Eq6AitSPNqPB9SmOf/8jWH1bx+fCKz6epaHVaPzz9gVIVZp9/gG2ZtsIsw/TpJ/oNx6XHmb5cP379+T6fFttw+2z9eNZfbuZYU/O1Nu268Wmfva0fnxnrbyvNltVsfRXrJv18/vRpzTYc1zzG1Lj0Za//ebPBejMtN9P8G8xbkXn4BjE239aK7NNuEEPFumVnXJdZxjjSx4Nf53rv0+L4fF3rxWCf36MxW/97SPq8zT5vKtKmTV/eetNbxuWs2+a0/9f7LNKHr/89XH+9GdaX/rlUpL03Y5Pd98726FEpUc3PCcAG54Vs5wRIPwdUZDyer1tO8+Pv+ueRTOeE1qZtfk5YN32288v6v7/1jp0tnBNSn0lLx/LWzhnZzgmZpm3tnJB6v94xNcs5IX2dzc8p6dNahmU339bm54TUsrMe11tY7gbxZzr2V2ReVvNzQmrY59NWZJ42Uzzp54UNYkg75rc2Pv19ehyfryfLOWG9YdDyOQE2OC+0dE5Ij/3zcdnOCdGy142z9YZnPCekLzf9s0qbtqVzgqouiIiIiEgsKdEVERERkVhSoisiIiIisaREV0RERERiSYmuiIiIiMSSEl0RERERiSUluiIiIiISS0p0RURERCSWlOiKiIiISCwp0RURERGRWFKiKyIiIiKxpERXRERERGJJia6IiIiIxJISXRERERGJJSW6IiIiIhJLSnRFREREJJbMOVeYBZv9E9i0IAvvuE2Bj0IHUUBx3z6I/zZq+1r2kXPusHwFI4XXwjkh7t/15rS98abtDSPrOaFgiW4pM7Na59zQ0HEUSty3D+K/jdo+SYqkfRe0vfGm7S09qrogIiIiIrGkRFdEREREYimpie61oQMosLhvH8R/G7V9khRJ+y5oe+NN21tiEllHV0RERETiL6kluiIiIiISc2Wf6JrZYWb2spm9amY/yTDezOwP0fg6M9szGr6tmf3bzF40s+fN7Adp80w2s5ei6WeaWb8ibtIGCrGNafP+yMycmQXrCq5Q22dm50bLfd7MLinW9jRXoO/oEDN7zMwWmFmtme1TzG1qFn97t6+7mT1hZs9G2/eLtHkqzWyOmb0S/d24mNsk+ZHDd2NC9B1eYGbPmdnaaN+3euwqRTls70Zmdnfad/7UXOctRR3c3h9Gw54zs2lm1r240bddDtu7sfmcoS46tu2WNq6fmd1pPrd40cy+Utzo28bM/mxmH5jZc1nGZzyup43vZGbPmNk9xYm4Bc65sn0BnYDXgMFAV+BZYJdm0xwO/AMwYF/g8Wj4lsCe0f99gP+l5gW+BnSO/v8d8Lu4bWM0bFvgX8BiYNM4bR9wEHA/0C16v3nMtu8+4Btp8z9YhttnQO/o/y7A48C+0ftLgJ9E//8k5G9Qr8J9N5pNfxQwN/q/xWNXKb5y/C1cmPouA5sBS6Jp2/RZlcKrg9u7NbAI6BGNmw6cEnqb8rC9k4GfR/9/EXggbdxNwOnR/12BfqG3qZXt/SqwJ/BclvEZj+tp48cDtwP3hN6Wci/R3Qd41Tm30Dm3CvgLcHSzaY4GbnbeY0A/M9vSOfeuc+5pAOfcMuBF/I8P59x9zrk10fyPAdsUY2OyKMg2Ri4HfgyErKhdqO07C/itc25lNP6DYmxMBoXaPgf0jf7fCHin0BuSRUe2zznnlkfTdIleLm2em6L/bwJGFnIjpCBy+W6kGwtMA8jh2FWKctleB/QxMwN64xO/NTnOW2o6sr0AnYEeZtYZ6Em4Y1iuctneXYAHAJxzLwEDzay/mfXFJ45To3GrnHNLixZ5OzjnHsLvr2wyHtcBzGwb4Ajg+sJH2rpyT3S3Bt5Me/8WGx4MW53GzAYCe+BLlJo7DX/VEkpBttHMvgm87Zx7Ns/xtlWh9uFOwAFm9riZzTOzvfMZdBsUavvOAyab2ZvApcBP8xZx23Ro+6LbWwuAD4A5zrnU9vV3zr0LPukBNs9/6FJguXw3ADCznsBhwIwM4waS/fhcSnLZ3quAnfFJ3X+BHzjnmnKct9S0e3udc2/jj1tvAO8Cnzrn7it8yB2Sy/Y+CxwDEFUnG4AvKBsMfAjcEN3Ov97MehU+5IJq6fO4Al+I1lTkmDIq90TXMgxrXjrZ4jRm1ht/cD3POffZejOaXYS/+rytg3F2RN63MTqpXARU5y3K9ivUPuwMbIy/pTIBmB6VKhRbobbvLOCHzrltgR8SlRQE0KHtc86tdc4NwZ8M9kmv0yZlL5fvRspRwCPOufVKkFo6PpegXLb368ACYCtgCHBVVNrXls+qVLR7e6M690cDg6JxvczsO4ULNS9y2d7fAhtHF+/nAs/gc4jO+GoAVzvn9gDq8VWyylnGz8PMjgQ+cM49VeyAsin3RPctfD3TlG3Y8PZH1mnMrAv+IHqbc+5v6TOZ2cnAkcAJzrmQB5xCbOP2+APMs2b2ejT902a2Rd6jb12h9uFbwN+i2ypP4K8sQzS4K9T2nQyk3v8Vf1sthA5tX0p0G+9BfKkewPtpt8G2xJf4SnnJ5buRcjxRtYWUlo7PJSqX7T2VdcelV/H1VL+Y47ylpiPbewiwyDn3oXNuNf5Ytl8RYu6IXI5jnznnTo0u3k/C10teFM37VtodqzvxiW85y/Z57A98M8ot/gKMMLNbix9eGlcClZ7b+8JfJS3EJ22pyuG7NpvmCNavMP1ENNyAm4ErMiz3MOAFYLO4bmOz+V8nXGO0Qu3D/wNqov93wt9isRht34vAgdH/BwNPleH+24yoQQbQA3gYODJ6P5n1G6NdEmL79CrsdyOabiN8XcBeacNyOnaV0ivH38LVwMXR//2Bt/EX4Dl9VqX06uD2fhl4Hl831/D18M8NvU152N5+QNfo/zPwdVhT4x4GvhD9fzEwOfQ25bDNA8neGC3jcb3ZNAdSAo3Rgn+QedgRh+Nb5L4GXBQN+z/g/6L/DfhjNP6/wNBo+DD8bYc6/K2VBcDh0bhX8YlRavg1cdvGZst/nUCJbgH3YVfgVuA54GlgRMy2bxjwFP5g+ziwVxluXxX+1l5dtJ+q05a5Cb5RxyvR38pQ26dX4b4b0ftTgL80my+nY1epvXL4LWyF7zHlv9F3/jstzVvqrw5u7y+Al6LhtxD1kFPKrxy29yvRMeslfCn1xmnzDgFqo+/0rPRxpfjC32F5F1iNL70dl8txvdkyDqQEEl09GU1EREREYqnc6+iKiIiIiGSkRFdEREREYkmJroiIiIjEkhJdEREREYklJboiIiIiEktKdEVEREQklpToioiIiEgsKdEVERERkVj6/9clzcBioGyjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x648 with 5 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibmq_jakarta = provider.get_backend(\"ibmq_jakarta\")\n",
    "plot_error_map(ibmq_jakarta)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "208f1129",
   "metadata": {},
   "source": [
    "# 1. Prepare (New) Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e8d0f6",
   "metadata": {},
   "source": [
    "**Dataset Information:**\n",
    "* Total examples: 20,468 -- Dimensions: 112\n",
    "* PCA down to: **4** dimensions\n",
    "* Train set size: **100 (adjustable in cell below)**\n",
    "* Test set size: **2,000 (adjustable in cell below)**\n",
    "\n",
    "**Variables Information:**\n",
    "* `x_train` [np.array] stores the train examples -- shape: (Train set size, 4)\n",
    "* `y_train` [np.array] stores the train labels -- shape: (Train set size,)\n",
    "* `x_test` [np.array] stores the test examples -- shape: (Test set size, 4)\n",
    "* `y_test` [np.array] stores the train labels -- shape: (Test set size,)\n",
    "\n",
    "**Note:** Run all cells in this section to generate the dataset variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9380b23d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set number of training and test example ---------------\n",
    "n_examples = 100\n",
    "n_test = 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "01cce7c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>682</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>540</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 113 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   col_0  col_1  col_2  col_3  col_4  col_5  col_6  col_7  col_8  col_9  ...  \\\n",
       "0      9   1354      0     18      0      1      7      9      0      0  ...   \n",
       "1      0    239      0      1      0      1      0      0      0      0  ...   \n",
       "2      0    260      0      4      0      3      6      0      0      0  ...   \n",
       "3     17    682      0      1      0      0      8     17      0      0  ...   \n",
       "4      1    540      0      2      0      1      7      1      0      0  ...   \n",
       "\n",
       "   col_103  col_104  col_105  col_106  col_107  col_108  col_109  col_110  \\\n",
       "0        0        0        0        1        1        0        0        0   \n",
       "1        0        1        0        0        0        0        0        0   \n",
       "2        0        0        0        1        1        0        0        0   \n",
       "3        0        1        0        1        1        0        0        0   \n",
       "4        0        0        0        1        1        0        0        0   \n",
       "\n",
       "   col_111  targets  \n",
       "0       49        1  \n",
       "1       55        1  \n",
       "2       56        1  \n",
       "3       65        1  \n",
       "4      175        1  \n",
       "\n",
       "[5 rows x 113 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change the directory to the correct one on your machine\n",
    "datafile = pd.read_csv(\"data/fraud_detection_bank_dataset.csv\")\n",
    "data = datafile.to_numpy()\n",
    "datafile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c1ea88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the features in the dataset, prior to PCA\n",
    "X_data = data[:, :-1]\n",
    "X_data = preprocessing.StandardScaler().fit_transform(X_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "886a261d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCA to reduce the number of features\n",
    "pca = PCA(n_components=4, random_state=SEED)\n",
    "X_pca = pca.fit_transform(X_data)\n",
    "# Scale the features in the dataset, post PCA\n",
    "X_pca = preprocessing.StandardScaler().fit_transform(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4aa6b352",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_class_train = int(n_examples / 2)\n",
    "n_class_test = int(n_test / 2)\n",
    "\n",
    "# 0 --> 3000 (pos train) ; 3000 --> 5438 (pos test)\n",
    "# 5438 --> 18000 (neg train) ; 18000 --> 20468 (neg test)\n",
    "rand_pos_ind = random.sample(range(0, 3000), n_class_train)\n",
    "rand_neg_ind = random.sample(range(5438, 18000), n_class_train)\n",
    "x_train = np.concatenate((X_pca[rand_pos_ind], X_pca[rand_neg_ind]))\n",
    "y_train = (\n",
    "    np.concatenate((np.ones((n_class_train, 1)), np.zeros((n_class_train, 1))))\n",
    "    .astype(int)\n",
    "    .reshape(\n",
    "        n_examples,\n",
    "    )\n",
    ")\n",
    "\n",
    "rand_pos_ind = random.sample(range(3000, 5438), n_class_test)\n",
    "rand_neg_ind = random.sample(range(18000, 20468), n_class_test)\n",
    "x_test = np.concatenate((X_pca[rand_pos_ind], X_pca[rand_neg_ind]))\n",
    "y_test = (\n",
    "    np.concatenate((np.ones((n_class_test, 1)), np.zeros((n_class_test, 1))))\n",
    "    .astype(int)\n",
    "    .reshape(\n",
    "        n_test,\n",
    "    )\n",
    ")\n",
    "\n",
    "# compatibility with previous implementation\n",
    "x_train_norm = x_train\n",
    "x_test_norm = x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3ce4be",
   "metadata": {},
   "source": [
    "# 2. VQC from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7d53e2",
   "metadata": {},
   "source": [
    "## 2.1. Prepare encoders and ansatz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e684879c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Fyw9+LN0SesOnuS+m25qwh8W13hj8fb7/Q83rdVFYULTe++ZRbTm4TC3rdyu07vw1E5Ry7qC+/Nse1apaX5KUWLOZBr3bQD+sGq/7ujyvalFxWrh+kuavmaDw4CpKTU/R/DWfq2ndWxQZWs3e1ne/fqy6sc11Y502Zfdhy5ifb4CS4jvkO+ZMH0lS1YhaqhpRSxEhBcezAL8g9Ww9UN8u/0C3tx0sSZW+X39P0EvS/DVfaMPehQ7rFtavjvq0skuoKt3aqHRL6LlzrPY3Sw91lHw8LPkFAAAAAIC7VNyrwkAFsfXgcklSXLVGhZY5cmqHakQnKtA/ON/xhBqNlWPJVvLZ/bJYcjV/zQT9bXx3dUrqo3o1W+jsxRN6+Ys7tOfYeknSn//4L0WEVNXb0/rLkmdRXl6exsx4RAF+wXr+vgmOTl2pxEZKj97kGXfKt0mUejQxOgrP4mqiVpJW7ZynhnEd7AkUSYqNTlTjhM76bedcSVK1yDp6vt8E9evyV63cMUfLNn+tZ//4f3rizjEKC7Zl/LJzr2jJxqnq1rJ/vvbfntbfvuyZo8fICbeV6rMXJzP7sh57r6Ge+X/tlGu5uhfg+j0L1etvPpq78v+KbcOZPnJG1xYP6sipndpx+DdJ9Ks7EvTX9ymku5pLzeoYHYXtJoXBt0gxLJkHAAAAAKjASDQBHuzshROavOBVtWrQo8i7+C9mpCo0qODUlrAg21pv6RmpslgtSr2YoveHLFXPNo8qJDBCL/T7Qo/e9oYOp2yXJAUHhuml/tO19/g6TV30D3398xhtPbhcIx+aqtCgyDL5jN6mWR3pYYOXZWpeR3qoA3fHO8OZRK0kHUnZocQaBTN3CdUb6+gp27SIsxeSNXb2MM1a/r46N/6DurZ4UJ/852n9e/5LSs84L0nadWS1LmWmFVgC7aHuLyuhRhM1TbxZY59ZpbHPrNLbT/wkSXrw1pEa3nd8qT9rUYL8Q/TywzN0MHmLJi14RZJ0/tJpvff1QLVvdLf6dH662Dac6SNn1KvZQsGB4Vq3x/b5K3u/usP1fQpbgmdgZ6lRzcodAwAAAAAA5YFEE+ChMq9c0quT+sjH7KsR908sprRVJhXMOlh1dWlHf98APdLzFYUEReQrc2OdNrqt7SD764Zx7fRor39o+tK3NHnha+rf7e9qWrfwfUMqozaJ0qCbjNkbqV1daaBB5/Y2ziZqJSk9M1WhwQ6StcHRSs+0JTtSUg+qeb2uevepRapTraEaxXXQR3/6RTGRdZR26bQkadfR1TKZTKob2yxfO4k1mujsheNqnNBZSfEdlBTfQf6+gZKk9o3uVo3oBDd84qLVr9VSj985RjOXvaeNexfrvRkD5eNj1oj7v3SqvjN95AwfHx/VjW2mXUdWS6Jf3eH6PoWNr1l6/BapRVz5n9vPLD3RRWpuwLkBAAAAAChv7NEEeKDsnCy9OrG3Us4d1IfDlismsnaR5cOCo3U67WiB45f+d/E3LDg63/Ea0Qn6cNiyQtvr1rK/Ji14RSaTSb3L6Y58b9M8TnouRJq+Skq5UPbn8/eVereUOjVgJpMzSpaotXGYrL1mH74miTcVeN/sY1bvTn+yvz53MVnBAeHy8/XPV+7U+SO6lJmmujWb248dSN4sk8mkxNimRca1ce9ivTihZ7HxN6vbpcj/15J0783DtWHfIo2aeLdyLdka8+QiRYRULbbt3xXXR86KCInRiTN7JdGv7nJtn+IqX7NtVlF8VWn+FinHUvbnrB0t9e/AHnoAAAAAgMqDRBPgYXItOXp9cl/tPrZW7z61uNiLpZIUX72xVm7/j7KyM/Lt03Tk1E75mf1V85o9VYqTl5en9755VDERtZVxJV1jZw/V6Ee/c+mzVHRxVaQRd0gLtklLdkp5Jb/e7pQG1aUHO0hVQsum/YqmpIlaSQoNilJ6RmqB45cyzyvMwbKUA3uNdthOTk6W/HwDChw/kLxFkvLNyNl/YpOqRyUoJDC8yNiSEjrp33/dVWQZSQr0Cy62jMlkUo9WA7Ru94+qV7OFWjXoXmyd35W0j4oS4BekKzmZBY5Xxn51l8L6FJKPj3RrIymplvT1KunQ2bI5j9lHuq2J1L0xs04BAAAAAJULiSbAg+Tl5emd6Q9r0/4levOxH5QU38Gpeh2Temvywte0Yuss9WrzqCTJYsnVsi3fqPUNveTv4AJtYWb8/I62H/pVHw1bobTLZ/TapD7676rxurvjEJc+U0Xna5buaiE1rWO7W373Sfe1XTVM6tZI6lCfWUzOciVRK9n2GTp8akeB40dO7VRc9SSnzx8WUsU+k/Bah05ulb9voGrH3Gg/diB5k+rFNi9Q9nqB/sGKq9bQ6RiKknoxRZ/OG64GtVppf/ImfffLWN1783NO1XVXH0m2feNKMuOnIveru5S0Tyuj6uHSsz2lX/dJP++Szl92T7sm2ZJYdzVnFhMAAAAAoHLifkvAg3zyn6e1Yuss9esyQoH+Idp5ZLX9cSbtuCTbUlG3veirKYv+Ya9Xv1YLdW3+gD6dN1zz13yhjfuW6K1pDyol9ZAG9nrd6fPvOrpGUxaO1oCerykpoaM6Ne6tezr+SeO/f15HThV/578krdj6rVZs/Vb7jm+QJK3b/aNWbP1WWw4sL0FPeJ+4KtLQbtLfe9vunA/2L76OIyaT1KS2ra2X72GpvJK4NlH7+qC5TidqJalj497adXS1Tp47aD+WknpYOw6vVMek3k63ExfTULmWHPv/198dOLlF8TUay+xjlmRLiB09vSvfkm9lzWq16r1vHpWf2V9jnlqke28ari/mv6iDyVudqu+uPrLVO5QvOVScityv7lLSPq2sfHykW26UXuktPdlVSqopBwtCOickQOqeJI3qY2uLJBMAAAAAoLJiRhPgQdbt+VGSNH3JW5q+5K187w3o+ZoG9hotq9WqvDyLrNa8fO+PeGCiJv74d036aZQuZaWpXmxzvfPET2pQu5VT587IStc70/orKaGTHur2sv340Hs+1LZDK/T2tIf0yZ/XFDs76o0p/fK9/n//se2z4sw+JxVBTJjUp5V0RzPpwGnpWKp07Jztz7SMguUDfG37edT536NedSmy+JW64MDvidr+3f9uT9T+Liaitn0JvS0Hlutvn3fXiH5fqmebgZKkO9o/qbkrx+nVSX00+LY3JZNJXy14RTGRdXR3B+dn8zWte4skafextfmW7DuYvEVNE2+2v05NT1GuJafA/mll6dsVH2nTvsV6b8hShQdH6/E7x2jLwWV6e/pD+r/n1ivAL6jI+u7qo0uZaTp+dq/u6zLC6ToVuV8lW4JeUr4EfURojCJCYtS8Xpdi67vSp5Wdj4/UuJbtkXpJOnjm6nh9/LyUnVuwTnSIbZyuHS3VqSLVr2ab1QoAAAAAQGVHognwIFNfPlxsmRrRCVr0fsHNgAL8gjS090ca2vsjl84dHBimyS8dKHDc3y9QE17Y5qCGVRZLrkwmH/n4XJ0c6Si2AjX/lyzLuy5ZVpH4+0qNatoev8vKka7kSrkW2/4dfmYpyJ8ZS+7iTKLWpuC/vyD/EL0/ZKk+nfcXvfv1AFllVcv63TWs98cKCnB+c6wa0QlqWKedVu/8Xjc3vVeSlJWdoZPnDqh3p6ft5SJDYpRQo4m++OFFhQZFqWfrAa59aCftP7FJE398WQ92e8meuPDz9dfL/WfoTx+30mfzntdzfT8tsg139dGaXT/I1+yvm5r80ek6FblfpdIn6F3pU1wVHWp7tEm0vc7LkzJzpByLZMmzjdUBvlKAn7FxAgAAAADgqUg0AXDJog2TtWjD5Osu4DtnycZpevfrsr0A7IkC/WwPlA1nErWS1LxeV4cJ0WpRcXrt0dmljuPujsP0r3nP6dk//p8C/YMV6B+sBe9Z8pUpPIFbNurXaqn5Y64UOF6n2o36/m3HG9VYLLmSyWRflk5yro9+TyRLjpPOSzZO1S3N+ik8pIrzH0AVu19LkqB31K+u9ikc8/GxLYsHAAAAAACcQ6IJQIm9Mfh7ZefaLq5WCa9ZTOmC2jW6U+P+vM7+2mxmKELF0aP1AM1c9p6+/+1f6tfVO5cyO3X+iG4f6efSkpfTl7ylSQtekSRVjaiV7739JzZr84GfNeGF7SWOiX513K+l6VMAAAAAAAB34OougBJLjG1aqvrhwdEKL8c9VIDyZPYx64X7v9T+ExuNDsUlA3uNVp/Oz0iSggPCSlz/9naPq82Nt0uS/Mz++d47n56iEfdPVK2q9UvcLv3quF9L06cAAAAAAADuQKIJAAA3S4rvoKT4DkaH4ZIa0QmqEZ3gcv0q4bGqEh7r8L22DW93uV2JfnXUr6XtUwAAAAAAgNLyMToAAAAAAAAAAAAAeCcSTQAAAAAAAAAAAHAJiSYAAAAAAAAAAAC4hEQTAAAAAAAAAAAAXOJrdADwDh8/bHQEFVOPEUZHAHgP/r8AAAAAAAAAnocZTQAAAAAAAAAAAHAJiSYAAAAAAAAAAAC4hEQTAAAAAAAAAAAAXEKiCQAAAAAAAAAAAC4h0QQAAAAAAAAAAACXkGgCAAAAAAAAAACAS0g0AQAAAAAAAAAAwCUkmgAAAAAAAAAAAOASEk0AAAAAAAAAAABwCYkmAAAAAAAAAAAAuIREEwAAAAAAAAAAAFxCogkAAAAAAAAAAAAuIdEEAAAAAAAAAAAAl5BoAgAAAAAAAAAAgEtINAEAAAAAAAAAAMAlJJoAAAAAAAAAAADgEhJNAAAAAAAAAAAAcAmJJgAAAAAAAAAAALiERBMAAAAAAAAAAABcQqIJAAAAAAAAAAAALiHRBAAAAAAAAAAAAJeQaAIAAAAAAAAAAIBLSDQBAAAAAAAAAADAJSSaAAAAAAAAAAAA4BISTQAAAAAAAAAAAHAJiSYAAAAAAAAAAAC4hEQTAAAAAAAAAAAAXEKiCQAAAAAAAAAAAC7xNToAeIfh04yOwBgfP1y27S/+oGzbrwh6jDA6AgAAAM/D90i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",
      "text/plain": [
       "<Figure size 2219.99x529.76 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_zz_feature_map(feature_dim, feature_map_rep, ent):\n",
    "    return ZZFeatureMap(\n",
    "        feature_dimension=feature_dim, reps=feature_map_rep, entanglement=ent\n",
    "    )\n",
    "\n",
    "\n",
    "feature_dim = 4\n",
    "fmap_zz = get_zz_feature_map(feature_dim, 1, \"linear\")\n",
    "fmap_zz.decompose().draw(\"mpl\", scale=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b2fe8fcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fo7V57xL17/qyvD2r6cDxbx1/C65eV8EBdXXm4nE9Nb2BBnSbpNjukyRJDe9qpc4tH9f7K59TjjVbIUGR+nzr+0pNO6rx/RJK/PoHT2xT/NrJiu3+iqIicu/e+HD7v2rOqhfUvP6DCq9d+BnVktTr/uFa8c1sTZrfW4MfmiqZTPpkzUQFB9TTn9qNuI09gqqAvnYNBD7gQnak/EeStDDxdS1MfD3P325e6tNut8tms8puz3uFsjGPz9O8/7ys+asn6GrmJTUIbalpw1arUd02JXrt65lXNC2hv6IiOqhfl5cc60c+/Lb2Hd2sNxL66b1ntxU5q/LxrKYZIzbo/ZXP681PY2WXXa0bdtWomHfl48UVJY2KvnYNBD7gQha8dKzYMSFBEVo3I//ZwV4ePhoZ845GxrxzW69t8fZT3Pgj+dZ7enhr7ov7CtjCLqs1RyaTm9zcfv12sFZgmF4ZuKzI17r54W6zF31ZVVQN9LVr4Dt8ALdlXVKceo7z0IL1r5V628RdCeo5zkMJ66dUQGXA7avKfc0MH0CpTRm8Slk5NyRJNfzrlHr76KZ/0Oxndzgeu9IdxWBcVb2vXasaAJVCSX5SVRR/S5D8b/kNNeBsVb2vOaQPAIABEPgAABgAgQ8AgAEQ+AAAGACBDwCAAZjsle3+fgAAoNSY4QMAYAAEPgAABkDgAwBgAAQ+AAAGQOADAGAABD4AAAZA4AMAYAAEPgAABkDgAwBgAAQ+AAAGQOADAGAABD4AAAZA4AMAYAAEPgAABkDgAwBgAAQ+AAAGQOADAGAABD4AAAZA4AMAYAAEPgAABkDgAwBgAAQ+AAAGQOADAGAABD4AAAZA4AMAYAAEPgAABkDgAwBgAAQ+AAAG4O7sAlA5PJfg7Aqc490nK/b518+s2OevCrqNqbjnpq8rBn1dtIrs6aIwwwcAwAAIfAAADIDABwDAAAh8AAAMgMAHAMAACHwAAAyAwAcAwAAIfAAADIAL7wAGFLd2suLXvZpvvdnNXX4+gQoPaaYOzXqrV/Qw+Xj5OqFCoPTo66IxwwfgYLXl6NK1c9pzZJPeX/m8Rv6jlU6d+97ZZQFlQl/nYoYPGNxHY75TrYAwSVKONVs/nf9eize9pa/3LdPpC0f0yvze+vCFvTKb+bhA5UFf58cMHzA4Lw+LfLx85ePlKz9LoJqERWvSU0vVplE3SdKJswe1Zf+/nFwlUDr0dX4EPoAC9e86wbG8+/tEJ1YClB8j9zWBD6BAESHNHMvnLp9yYiVA+TFyXxP4AArkZjI7lu2yO7ESoPwYua8JfAAFOnH2oGO5hn8dJ1YClB8j9zWBD6BAn26c7lhuWb+z8woBypGR+5rAh0v4dtlkzRpg0qwBJqWfO1bs+LVzBjnGo/xYrTk6fuagZi4eom8PrJIk1Q4M14Mt+zq5ssqHnnYd9HUu4/wAsRzs2LFD8+bNU2Jiok6dOiUvLy+Fh4frscce0+jRo+Xv76+hQ4fq448/liQtX75cvXv3dnLVQNFip0UW+rcgvxBNHrhcnu5ed7AioOzo6/wI/BLIyMjQyJEjFR8fL7v915M8rl+/rosXLyo5OVkLFy7UunXrdPDgr98PtWrVygnVAmXj4e6l8NpRah8Vo0c6/k3+1Wo4uySgzOhrAr9Y165dU9euXbVt2zZJktlsVvfu3dW6dWtZLBYlJydrxYoV2r9/v8aOHesI/MDAQIWHhzuzdKBEfntFMrObuzw9vJ1cEVB29HV+BH4xBg8e7Aj79u3bKy4uTg0bNswzZvv27erWrVueIwDM7lFZ3LwiGVCV0Nf5cdJeEWbPnq0lS5ZIktq1a6f169fnC3tJio6O1oQJE/Ic7m/duvUdqxMAgOIQ+IW4cuWKXn019zaL3t7eSkhIkMViKXR8nz598jxmhg8AcCUEfiFmz56t8+fPS5Kefvpp1a9fv8jxt35ff+sM/9ixY/q///s/jRw5Um3btpWnp6dMJpMGDRpUrnUDAFAQvsMvxKJFixzLI0aMKHa8zWZzLHt7e6tJkyZ5/v7uu+9q1qxZ5VdgFZaddV1ZmVeLHGOz5tyhaoCyo6fhCgj8Apw4cUL79u2TJIWFhSkqKqrYbc6dO+dYvueee+TunnfX1qxZU3/84x9177336r777tPKlSv14Ycflm/hVcSCsc2KHwRUIvQ0XAGBX4CkpCTH8v3331+ibXbt2uVYLuj7+wkTJuR5/PXXX99ecSVkMpXv1br+Z4GxbjJxU3nvx1utm2HM/VoaFfn/AX1dMejropV1///2BPHSIPALkJqa6lgODQ0t0TZr1qxxLHOGftkM/sdR+QdHFDlm7ZxBOvj1J3emoCroqR6T9VSPyc4uwzDo6TuDvi4aJ+0VIC0tzbHs5lb8LkpPT1d8fLzjMWfoAwBcDYFfAD8/P8fy8ePHix0/btw4paenS8r9B0KLFi0qrDYAAG4HgV+Apk2bOpYTExN19WrhZ9fGxcXpgw8+cDxu2LChfH2df3Unu91erv8ZVXnvR/Zr6bH/yx997VzO2r8EfgE6duyogIAASbmH60eNGiWr1ZpnTE5OjqZOnaohQ4ZwhT0AgMvjpL0CWCwWjRkzxnFm/YIFC7R9+3bFxMQoICBAJ06c0MqVK5WamqqIiAi1bdtWy5Ytk8T39wAA10TgF2LcuHFKTk7W0qVLJUmHDx/WzJkz84zp3LmzPvnkkzxXy2OGDwBwRRzSL4TZbNbixYsVFxenTp06qXr16vL29lZkZKT69u2r5cuXa8OGDQoLC3NcpEdihg8AcE3M8ItgMpkUGxur2NjYQsecPn3acc390NBQ1a5d+06VBwBAiRH4ZbR3717HMrP729euz2S16zO5xON7jJivHiPmV1g9QFnR03A1HNIvo98GPt/fAwBcFYFfRiWd4X/zzTeqWbOm47///d//lSR9+umnedZ/8803FV0yAMCAOKRfRiWd4WdnZ+vChQv51t+4cUM3btzIMw7GtXnvUm3cvUiHT+3UpatnVSswTA/c8xf16/KSLN5+RW6blZ2p+WsmKnHXAl3NuKQGdVpp2B/fVIv6D+Ybm5p2TDM+G6S3R226rToHvBGhMxdzr0IZ2/0Vx/XLz106pc82vanDJ3fqx5/36EZ2huLHH1VIUESe7dcnLdCbn/56bszq6dkym/k4qqroa9fADL8MsrOzlZKSIin3crwNGjQodGznzp1LdAWlzp0736Hq4YqWfDVTbm5mDen1ht4YtloPtx+lVVvf19i53WWz2Yrc9u0lQ/Xltrka+NBrmjLkcwX5h2r83If0w0/JkqTsnCwlrJ+qaxmX82x3+FSS1u4s/U1bopv8QbOe2ape0cMc605f+EFf7VksX0ug7on8XaHb3tekp2Y9s1U9o4eW+nVR+dDXrsF1/ulRCR06dEhZWVmSpBYtWlT4LSdR9U0ZvEoBvsGOxy0bdJKfT5De+myg9vy4Sa0bdilwuyOn92jD7oV68bGP1fO+wbnb1u+kYW830ydrJ2nK4JUymUzyt9TQmDm/V4eo3rqWeVnvLBmuc5dOalDPqaWutXq1YEWFt8uzrnnkg1ryyhlJ0pfbPlLS4bWFbFtT1avV1M6U1aV+XVQ+9LVrYIZfBs2bN3fMzLds2eLsclAF/PZD8aa7690nSTp/+adCt9v63Uq5mz3UueXjjnVms7s6t3xCSSlrlJVzQ+5mDz3cYZRmjNig/x5YoSOnk1XDv46mDV+txvXulSRNXxSrRyYGOA5rStKF9J/16ORgvRbft9j6S3J3SRgPfe0aqsa7AKqwvT9+JUkKq9W00DHHz3ynkKBIeXta8qyPCGmmbGuWTp//QVZrjr7cNld/n9NVHaJ6q0GdVjqf/pNe+qiXUk7ulCQ9++d/qnq1mnojob+sNqtsNpumLxogLw+LXnh0bsW9SRgOfX3ncUgfcGHnL/+kuDWT1KZRN8dspSDp19Pk6xOYb72fT5Ak6cr1NFntVqWlp2rGiA26knFRyUc26sW+Hynl5E4dS92vxvXulcXbT+P7L9Tz/3xAC9a9Jg93L+398SvNHLFRvj4BFfU2YTD0tXMQ+ICLyrhxVZPm95ab2V1jHptXzGi7TMp/Doldv97J0dPdSwO6T5QkXcm46FjfuN69eT50m4RFa2CP1zRvzQSZZFL/Li+ref3CT1QCSoO+dh4O6QMuKCs7U5PmxSj1wo+aPmyNggPqFjnezxKkKxlp+dZf/eUD0M8SlGd9SFBEkT9d6tK6v0wyyWQyKabj6NK/AaAA9LVzEfiAi8mxZuvVuD46dHK7pg79UpGhzYvdJrx2M6WmHVVm1vU864+fOSAPs6fq1GxY4te32Wx667OBCq5eVxYvf81aNrLU7wG4FX3tfAQ+4EJsNpumLXxSu39I1KuDVuT7eVBh2kfFKMearc17lzjWWa052rTnM7W9u4c83b1KXMOijdO0/+gWje+f+3Oob/b/W59vnVPq9wLcRF+7Br7DB1zIe/8erc17l6h/15fl7VlNB45/6/hbcPW6Cg6oqzMXj+up6Q00oNskxXafJElqeFcrdW75uN5f+ZxyrNkKCYrU51vfV2raUY3vl1Di1z94Ypvi105WbPdXFBXRXpL0cPu/as6qF9S8/oMKr134GdU3bd67VJL0/akkSdKOQ/9Rdd9gVa8WrJYNOpW4FlQd9LVrIPABF7Ij5T+SpIWJr2th4ut5/nbzUp92u102m1V2e94rlI15fJ7m/edlzV89QVczL6lBaEtNG7Zajeq2KdFrX8+8omkJ/RUV0UH9urzkWD/y4be17+hmvZHQT+89u63YWdWUW37X/L///qskqUX9Trd9yVNUbvS1ayDwARey4KVjxY4JCYrQuhn2fOu9PHw0MuYdjYx557Ze2+Ltp7jxR/Kt9/Tw1twX9xWwhV1Wa45MJrc8FyYpqLZ8W/7y4W6zF31ZVVQN9LVr4Dt8ALdlXVKceo7z0IL1r5V628RdCeo5zkMJ66dUQGXA7avKfc0MH0CpTRm8Slk5uXd5rOFfp9TbRzf9g2Y/u8Px2JXuKAbjqup97VrVAKgUSvKTqqL4W4Lkf8tvqAFnq+p9zSF9AAAMgMAHAMAACHwAAAyAwAcAwAAIfAAADMBkt9uLv5oAAACo1JjhAwBgAAQ+AAAGQOADAGAABD4AAAZA4AMAYAAEPgAABkDgAwBgAAQ+AAAGQOADAGAABD4AAAZA4AMAYAAEPgAABkDgAwBgAAQ+AAAGQOADAGAABD4AAAZA4AMAYAAEPgAABkDgAwBgAAQ+AAAGQOADAGAABD4AAAZA4AMAYAAEPgAABkDgAwBgAAQ+AAAGQOADAGAABD4AAAbg7uwCUDk8l+DsCpzj3Scr9vnXz6zY568Kuo2puOemrysGfV20iuzpojDDBwDAAAh8AAAMgMAHAMAACHwAAAyAwAcAwAAIfAAADIDABwDAAAh8AAAMgAvvAAYUt3ay4te9mm+92c1dfj6BCg9ppg7NeqtX9DD5ePk6oUKg9OjrojHDB+BgteXo0rVz2nNkk95f+bxG/qOVTp373tllAWVCX+dihg8Y3EdjvlOtgDBJUo41Wz+d/16LN72lr/ct0+kLR/TK/N768IW9Mpv5uEDlQV/nxwwfMDgvD4t8vHzl4+UrP0ugmoRFa9JTS9WmUTdJ0omzB7Vl/7+cXCVQOvR1fgQ+gAL17zrBsbz7+0QnVgKUHyP3NYEPoEARIc0cy+cun3JiJUD5MXJfE/gACuRmMjuW7bI7sRKg/Bi5rwl8AAU6cfagY7mGfx0nVgKUHyP3NYEPoECfbpzuWG5Zv7PzCgHKkZH7msCHS/h22WTNGmDSrAEmpZ87Vuz4tXMGOcaj/FitOTp+5qBmLh6ibw+skiTVDgzXgy37Ormyyoeedh30dS7j/ACxHOzYsUPz5s1TYmKiTp06JS8vL4WHh+uxxx7T6NGj5e/vr6FDh+rjjz+WJC1fvly9e/d2ctVA0WKnRRb6tyC/EE0euFye7l53sCKg7Ojr/Aj8EsjIyNDIkSMVHx8vu/3XkzyuX7+uixcvKjk5WQsXLtS6det08OCv3w+1atXKCdUCZePh7qXw2lFqHxWjRzr+Tf7Vaji7JKDM6GsCv1jXrl1T165dtW3bNkmS2WxW9+7d1bp1a1ksFiUnJ2vFihXav3+/xo4d6wj8wMBAhYeHO7N0oER+e0Uys5u7PD28nVwRUHb0dX4EfjEGDx7sCPv27dsrLi5ODRs2zDNm+/bt6tatW54jAMzuUVncvCIZUJXQ1/lx0l4RZs+erSVLlkiS2rVrp/Xr1+cLe0mKjo7WhAkT8hzub9269R2rEwCA4hD4hbhy5YpefTX3Nove3t5KSEiQxWIpdHyfPn3yPGaGDwBwJQR+IWbPnq3z589Lkp5++mnVr1+/yPG3fl//2xm+3W7Xf//7X40bN04PPPCAatSoIQ8PDwUHB6tHjx5KSEjIc3QAAIDyxnf4hVi0aJFjecSIEcWOt9lsjmVvb281adLE8XjDhg3q1q2b43H9+vUVGRmpo0ePat26dVq3bp0WLVqkZcuWycvLWD8TKUh21nVlZV4tcozNmnOHqgHKjp6GKyDwC3DixAnt27dPkhQWFqaoqKhitzl37pxj+Z577pG7+6+71m63KzIyUs8995yeeOIJ1apVy/G3+Ph4DR8+XF988YUmTZqkN998sxzfSeW0YGyz4gcBlQg9DVfAIf0CJCUlOZbvv//+Em2za9cux/Kt399HR0crJSVFzz77bJ6wl6TY2FhNmjRJkvTRRx/lOVJQFiaTqVz/M6ry3o/s19Jj/5c/+tq5nLV/meEXIDU11bEcGhpaom3WrFnjWL71DH1/f/8it+3Vq5defvllpaWl6dy5c6pdu3Ypqq16Bv/jqPyDI4ocs3bOIB38+pM7U1AV9FSPyXqqx2Rnl2EY9PSdQV8XjRl+AdLS0hzLbm7F76L09HTFx8c7Hpf2DP2MjAzHso+PT6m2BQCgJAj8Avj5+TmWjx8/Xuz4cePGKT09XVLuPxBatGhRqte7eYJgy5Ytiz0aAADA7SDwC9C0aVPHcmJioq5eLfzs2ri4OH3wwQeOxw0bNpSvb8mv7pSUlOTYfty4cbdRbcHsdnu5/mdU5b0f2a+lx/4vf/S1czlr/xL4BejYsaMCAgIk5R6uHzVqlKxWa54xOTk5mjp1qoYMGXLbV9g7c+aM/vKXvygnJ0d//vOf9cQTT5RL/QAA3IqT9gpgsVg0ZswYTZgwQZK0YMECbd++XTExMQoICNCJEye0cuVKpaamKiIiQm3bttWyZcsklfz7+8uXL6tXr146ceKE2rZtq/nz51fQuwEAgMAv1Lhx45ScnKylS5dKkg4fPqyZM2fmGdO5c2d98sknGjRokGNdSWb4V69eVc+ePbV79241a9ZMa9as4bt7AECF4pB+IcxmsxYvXqy4uDh16tRJ1atXl7e3tyIjI9W3b18tX75cGzZsUFhYmOMiPVLxM/zr16/rj3/8o7799ls1atRI69evV40axrsvMwDgzmKGXwSTyaTY2FjFxsYWOub06dOOa+6HhoYW+Rv6zMxM9e7dW5s3b1Z4eLgSExMVEhJS7nUDAHArAr+M9u7d61guanafnZ2tPn36aP369brrrru0YcMG1atX7w5UWDm06zNZ7fpMLvH4HiPmq8eI+RVWD1BW9DRcDYf0y+i3gV/Y9/dWq1VPPvmkvvzyS4WEhGjDhg3F3n0PAIDyxAy/jEoyw1+8eLGWLFkiKfdOekOGDCn0+ZYuXcphfgBAuSPwy6gkM/wbN244lo8dO6Zjx44V+nyZmZnlVhsqn817l2rj7kU6fGqnLl09q1qBYXrgnr+oX5eXZPH2K3LbrOxMzV8zUYm7FuhqxiU1qNNKw/74plrUfzDf2NS0Y5rx2SC9PWrTbdU54I0InbmYexXK2O6vOK5fXtL61yct0Juf/npuzOrp2TKb+Tiqquhr18Ah/TLIzs5WSkqKpNzL8TZo0KDAcYMGDSrxFZQiIiLu4DuAq1ny1Uy5uZk1pNcbemPYaj3cfpRWbX1fY+d2L/ZOim8vGaovt83VwIde05QhnyvIP1Tj5z6kH35KliRl52QpYf1UXcu4nGe7w6eStHZn6W/aEt3kD5r1zFb1ih5W6vrva9JTs57Zqp7RQ0v9uqh86GvX4Dr/9KiEDh06pKysLElSixYtuC0kymzK4FUK8A12PG7ZoJP8fIL01mcDtefHTWrdsEuB2x05vUcbdi/Ui499rJ73Dc7dtn4nDXu7mT5ZO0lTBq+UyWSSv6WGxsz5vTpE9da1zMt6Z8lwnbt0UoN6Ti11rdWrBSsqvN1t1V+9Wk1Vr1ZTO1NWl/p1UfnQ166BGX4ZNG/e3DEz37Jli7PLQRXw2w+Vm+6ud58k6fzlnwrdbut3K+Vu9lDnlo871pnN7urc8gklpaxRVs4NuZs99HCHUZoxYoP+e2CFjpxOVg3/Opo2fLUa17tXkjR9UawemRjgOKwpSRfSf9ajk4P1WnzfCqsfVRt97RoIfMDF7f3xK0lSWK2mhY45fuY7hQRFytvTkmd9REgzZVuzdPr8D7Jac/Tltrn6+5yu6hDVWw3qtNL59J/00ke9lHJypyTp2T//U9Wr1dQbCf1ltVlls9k0fdEAeXlY9MKjcyusfhgPfX3ncUgfcGHnL/+kuDWT1KZRN8dspSDp19Pk6xOYb72fT5Ak6cr1NFntVqWlp2rGiA26knFRyUc26sW+Hynl5E4dS92vxvXulcXbT+P7L9Tz/3xAC9a9Jg93L+398SvNHLFRvj4BFVY/jIW+dg4CH3BRGTeuatL83nIzu2vMY/OKGW2XSfnPIbHr1zs5erp7aUD3iZKkKxkXHesb17s3z4dWk7BoDezxmuatmSCTTOrf5WU1r/+7Cq4fRkFfOw+H9AEXlJWdqUnzYpR64UdNH7ZGwQF1ixzvZwnSlYy0fOuv/vIB6GcJyrM+JCiiyJ8udWndXyaZZDKZFNNxdIXXD2Ogr52LwAdcTI41W6/G9dGhk9s1deiXigxtXuw24bWbKTXtqDKzrudZf/zMAXmYPVWnZsMSv77NZtNbnw1UcPW6snj5a9aykRVeP6o++tr5CHzAhdhsNk1b+KR2/5CoVwetyPfzoMK0j4pRjjVbm/cucayzWnO0ac9nant3D3m6e5W4hkUbp2n/0S0a3z/351Df7P+3Pt86p0LrR9VGX7sGvsMHXMh7/x6tzXuXqH/Xl+XtWU0Hjn/r+Ftw9boKDqirMxeP66npDTSg2yTFdp8kSWp4Vyt1bvm43l/5nHKs2QoJitTnW99XatpRje+XUOLXP3him+LXTlZs91cUFdFekvRw+79qzqoX1Lz+gwqvXfQZySWpH8ZDX7sGAh9wITtS/iNJWpj4uhYmvp7nbzcv9Wm322WzWWW3571C2ZjH52nef17W/NUTdDXzkhqEttS0YavVqG6bEr329cwrmpbQX1ERHdSvy0uO9SMfflv7jm7WGwn99N6z24qcVZWkfhgPfe0aCHzAhSx46VixY0KCIrRuhj3fei8PH42MeUcjY965rde2ePspbvyRfOs9Pbw198V9BWxhl9WaI5PJTW5uud8OlqR+SY4Pd5u96Muqomqgr10D3+EDuC3rkuLUc5yHFqx/rdTbJu5KUM9xHkpYP6UCKgNuX1Xua2b4AEptyuBVysrJvQtkDf86pd4+uukfNPvZHY7HrnRHMRhXVe9r16oGQKVQ1p8k+VuC5H/Lb6gBZ6vqfc0hfQAADIDABwDAAAh8AAAMgMAHAMAACHwAAAzAZLfb81/pAAAAVCnM8AEAMAACHwAAAyDwAQAwAAIfAAADIPABADAAAh8AAAMg8AEAMAACHwAAAyDwAQAwAAIfAAADIPABADAAAh8AAAMg8AEAMAACHwAAAyDwAQAwAAIfAAADIPABADAAAh8AAAMg8AEAMAACHwAAAyDwAQAwAAIfAAADIPABADAAAh8AAAMg8AEAMAACHwAAAyDwAQAwAAIfAAADcHd2AagcnktwdgXO8e6TFfv862dW7PNXBd3GVNxz09cVg74uWkX2dFGY4QMAYAAEPgAABkDgAwBgAAQ+AAAGQOADAGAABD4AAAZA4AMAYAAEPgAABsCFdwADils7WfHrXs233uzmLj+fQIWHNFOHZr3VK3qYfLx8nVAhUHr0ddGY4QNwsNpydOnaOe05sknvr3xeI//RSqfOfe/ssoAyoa9zMcMHDO6jMd+pVkCYJCnHmq2fzn+vxZve0tf7lun0hSN6ZX5vffjCXpnNfFyg8qCv82OGDxicl4dFPl6+8vHylZ8lUE3CojXpqaVq06ibJOnE2YPasv9fTq4SKB36Oj8CH0CB+ned4Fje/X2iEysByo+R+5rAB1CgiJBmjuVzl085sRKg/Bi5rwl8AAVyM5kdy3bZnVgJUH6M3NcEPoACnTh70LFcw7+OEysByo+R+5rAB1CgTzdOdyy3rN/ZeYUA5cjIfU3gwyV8u2yyZg0wadYAk9LPHSt2/No5gxzjUX6s1hwdP3NQMxcP0bcHVkmSageG68GWfZ1cWeVDT7sO+jqXcX6AWEY7duzQvHnzlJiYqFOnTsnLy0vh4eF67LHHNHr0aPn7+2vo0KH6+OOPJUnLly9X7969nVw1ULzYaZGF/i3IL0STBy6Xp7vXHawIKDv6Oj8CvxgZGRkaOXKk4uPjZbf/eoLH9evXdfHiRSUnJ2vhwoVat26dDh789buhVq1aOaFaoOw83L0UXjtK7aNi9EjHv8m/Wg1nlwSUGX1N4Bfp2rVr6tq1q7Zt2yZJMpvN6t69u1q3bi2LxaLk5GStWLFC+/fv19ixYx2BHxgYqPDwcGeWDpTYb69IZnZzl6eHt5MrAsqOvs6PwC/C4MGDHWHfvn17xcXFqWHDhnnGbN++Xd26dctzBIDZPSqTm1ckA6oS+jo/TtorxOzZs7VkyRJJUrt27bR+/fp8YS9J0dHRmjBhQp7D/a1bt75jdQIAUBIEfgGuXLmiV1/NvcWit7e3EhISZLFYCh3fp0+fPI+Z4QMAXA2BX4DZs2fr/PnzkqSnn35a9evXL3L8rd/X3zrDX7JkiZ5++mnde++9qlOnjjw9PeXn56c2bdpo4sSJunDhQvm+AQAAbsF3+AVYtGiRY3nEiBHFjrfZbI5lb29vNWnSJM/fX3/9de3Zs0deXl4KDQ1Vy5YtdfbsWe3evVu7d+/Whx9+qLVr16ply5bl9yYqseys68rKvFrkGJs15w5VA5QdPQ1XQODf4sSJE9q3b58kKSwsTFFRUcVuc+7cOcfyPffcI3f3vLt19OjRaty4sdq3by8PDw/H+n379ql///7av3+/+vfvr++++66c3kXltmBss+IHAZUIPQ1XwCH9WyQlJTmW77///hJts2vXLsdyQd/fDx8+XA8++GCesJek5s2bOy7Uc+DAgTy/4y8rk8lUrv8ZVXnvR/Zr6bH/yx997VzO2r/M8G+RmprqWA4NDS3RNmvWrHEsl/YM/d8e/r9+/Xqptq2qBv/jqPyDI4ocs3bOIB38+pM7U1AV9FSPyXqqx2Rnl2EY9PSdQV8XjRn+LdLS0hzLbm7F75709HTFx8c7Hpf2DP0tW7ZIknx9fdW4ceNSbQsAQEkxw7+Fn5+fY/n48ePFjh83bpzS09Ml5f4DoUWLFsVuY7PZlJqaqrVr12rs2LGSpOnTp8vXl4tEAAAqBjP8WzRt2tSxnJiYqKtXCz+zNi4uTh988IHjccOGDYsM7XfffVcmk0lms1l33XWXBg8erPDwcH3xxRcaPXp0+byBX9jt9nL9z6jKez+yX0uP/V/+6Gvnctb+JfBv0bFjRwUEBEjKPVw/atQoWa3WPGNycnI0depUDRkypFRX2LvrrrvUsWNH3X///QoNDZXJZNKePXsUHx+vS5culfdbAQDAgUP6t7BYLBozZowmTJggSVqwYIG2b9+umJgYBQQE6MSJE1q5cqVSU1MVERGhtm3batmyZZKK//6+b9++6tv31/sv7927V88884w+/fRTHTp0SDt37pTZbK6w9wYAMC5m+AUYN26cHn30Ucfjw4cPa+bMmZowYYI+/PBDpaamqnPnzvrqq6/ynORX2jP0W7RooS+++EI1a9ZUcnKyPv3003J7DwAA/BaBXwCz2azFixcrLi5OnTp1UvXq1eXt7a3IyEj17dtXy5cv14YNGxQWFua4SI90e9fQ9/PzU6dOnSTlvQYAAADliUP6hTCZTIqNjVVsbGyhY06fPu245n5oaKhq1659W6+Vk5OT538BAChvBH4Z7N2717F8u3fIS0tL06ZNmyQZ+7a67fpMVrs+k0s8vseI+eoxYn6F1QOUFT0NV8Mh/TL4beAXFtZfffWVpk6dqmPHjuX7265du/TQQw/p8uXLuuuuu/Kc0AcAQHlihl8GJZnhX7x4URMnTtTEiRMVEhKiunXrys3NTSdPntTPP/8sKffnep9//jkX3gEAVBgCvwxKMsPv0KGD3nnnHW3atEnfffedUlJSlJmZqcDAQP3+97/Xww8/rGHDhuW5wh+Ma/Pepdq4e5EOn9qpS1fPqlZgmB645y/q1+UlWbyL7pGs7EzNXzNRibsW6GrGJTWo00rD/vimWtR/MN/Y1LRjmvHZIL09atNt1TngjQiduZh7JcrY7q84rl++I2WNFm98U8fPHNDVjIuq7husqPAOeqrHZIXX/vXOk+uTFujNT389P2b19GyZzXwcVVX0tWtwnUoqmezsbKWkpEjKPdO+QYMGBY6rVauWnn/+eT3//PN3sjxUUku+mqlaAWEa0usN1axeV0dO71bc2slKPrJRs0b/t8j7O7y9ZKi2HfxCT/9phkKC6mvlf/9/jZ/7kGY9s1UN72ql7JwsLd70lh7p+Lc82x0+laRjqfvV496Bpao1uskf9GS3iQquXtex7sr1NDWq21YPd/irqlcL1tlLJ/TZxul69r12+vDFfaodGC5Juq9JT816Zqv+s/0jrd7+f6V6XVQ+9LVrIPBv06FDh5SVlSUp9/f03BIS5WHK4FUK8A12PG7ZoJP8fIL01mcDtefHTWrdsEuB2x05vUcbdi/Ui499rJ73Dc7dtn4nDXu7mT5ZO0lTBq+UyWSSv6WGxsz5vTpE9da1zMt6Z8lwnbt0UoN6Ti11rdWrBSsqvF2edV1a91OX1v3yrGtSL1pDZjTR5r1L1bfTi79sW1PVq9XUzpTVpX5dVD70tWvgpL3b1Lx5c8d1jW/e8Q4oq99+KN50d737JEnnL/9U6HZbv1spd7OHOrd83LHObHZX55ZPKClljbJybsjd7KGHO4zSjBEb9N8DK3TkdLJq+NfRtOGr1bjevZKk6Yti9cjEAMdhTUm6kP6zHp0crNfib++kUv9qNSRJ7maP29oelR997RoIfMDF7f3xK0lSWK2mhY45fuY7hQRFytvTkmd9REgzZVuzdPr8D7Jac/Tltrn6+5yu6hDVWw3qtNL59J/00ke9lHJypyTp2T//U9Wr1dQbCf1ltVlls9k0fdEAeXlY9MKjc0tcs9VmVXZOlk6d+17vLh2hIL8QdW71xG28e1RV9PWdxyF9wIWdv/yT4tZMUptG3RyzlYKkX0+Tr09gvvV+PkGScr+DtNqtSktP1YwRG3Ql46KSj2zUi30/UsrJnTqWul+N690ri7efxvdfqOf/+YAWrHtNHu5e2vvjV5o5YqN8fQJKXPff3rtf35/KvXJknZoN9daIDQr0rVW6N48qi752DgIfcFEZN65q0vzecjO7a8xj84oZbZdJ+c8jsevXuzl6untpQPeJkqQrGRcd6xvXuzfPh26TsGgN7PGa5q2ZIJNM6t/lZTWv/7tS1T72iXhdv5Guny/8qKVfzdS4ud31j79uUUhQRKmeB1UPfe08HNIHXFBWdqYmzYtR6oUfNX3YGgUH1C1yvJ8lSFcy0vKtv/rLB6CfJSjP+pCgiCJ/utSldX+ZZJLJZFJMx9Glrj+8dlM1DbtfXVr301sjEpVx46o+3Ti91M+DqoW+di4CH3AxOdZsvRrXR4dObtfUoV8qMrR5sduE126m1LSjysy6nmf98TMH5GH2VJ2aDUv8+jabTW99NlDB1evK4uWvWctGlvo9/JavT4Dq1Gyo0+d/KNPzoHKjr52PwAdciM1m07SFT2r3D4l6ddCKfD8PKkz7qBjlWLO1ee8SxzqrNUeb9nymtnf3kKe7V4lrWLRxmvYf3aLx/XN/DvXN/n/r861zSv1ebrp45YxOnj2kOjUKvlYFqj762jXwHT7gQt7792ht3rtE/bu+LG/Pajpw/FvH34Kr11VwQF2duXhcT01voAHdJim2+yRJUsO7Wqlzy8f1/srnlGPNVkhQpD7f+r5S045qfL+EEr/+wRPbFL92smK7v6KoiPaSpIfb/1VzVr2g5vUfVHjtws+olqTJ8/+shne1Uf3QFrJ4++vUucP619f/kNnNXY/+8ltlGA997RoIfMCF7Ej5jyRpYeLrWpj4ep6/3bzUp91ul81mld1uy/P3MY/P07z/vKz5qyfoauYlNQhtqWnDVqtR3TYleu3rmVc0LaG/oiI6qF+XlxzrRz78tvYd3aw3EvrpvWe3FTmrahLeTpv3LNbSzW8rx5ql4IB6alm/s57oMr7SnNiE8kdfuwYCH3AhC146VuyYkKAIrZthz7fey8NHI2Pe0ciYd27rtS3efoobfyTfek8Pb819cV8BW9hltebIZHJzXBr1id+P1RO/H1vsa938cLfd8uGOqom+dg18hw/gtqxLilPPcR5asP61Um+buCtBPcd5KGH9lAqoDLh9VbmvmeEDKLUpg1cpK+eGJKmGf51Sbx/d9A+a/ewOx2NXuqMYjKuq97VrVQOgUijJT6qK4m8Jkv8tv6EGnK2q9zWH9AEAMAACHwAAAyDwAQAwAAIfAAADIPABADAAk91uz3+lAwAAUKUwwwcAwAAIfAAADIDABwDAAAh8AAAMgMAHAMAACHwAAAyAwAcAwAAIfAAADIDABwDAAAh8AAAMgMAHAMAACHwAAAyAwAcAwAAIfAAADIDABwDAAAh8AAAMgMAHAMAACHwAAAyAwAcAwAAIfAAADIDABwDAAAh8AAAMgMAHAMAACHwAAAyAwAcAwAAIfAAADIDABwDAAAh8AAAMgMAHAMAA/h+z58QXDXXXAQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 654.795x529.76 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_z_feature_map(feature_dim, feature_map_rep):\n",
    "    return ZFeatureMap(feature_dimension=feature_dim, reps=feature_map_rep)\n",
    "\n",
    "\n",
    "fmap_z = get_z_feature_map(feature_dim, 2)\n",
    "fmap_z.decompose().draw(\"mpl\", scale=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8e1c7ad1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x, ['x[0]', 'x[1]', 'x[2]', 'x[3]']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 654.795x529.76 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define angle embedding with weights\n",
    "def get_angle_embedding(nb_features, repeat=1):\n",
    "    x_params = ParameterVector(\"x\", nb_features)\n",
    "    weight_params = ParameterVector(\"w\", 2 * nb_features * repeat)\n",
    "    rotation_params = ParameterVector(\"m\", 2 * nb_features * repeat)\n",
    "\n",
    "    model_params = list(rotation_params) + list(weight_params)\n",
    "\n",
    "    qc = QuantumCircuit(nb_features)\n",
    "    for j in range(repeat):\n",
    "        for i in range(nb_features):\n",
    "            qc.rx(\n",
    "                x_params[i] * weight_params[2 * i + 2 * nb_features * j]\n",
    "                + rotation_params[2 * i + 2 * nb_features * j],\n",
    "                i,\n",
    "            )\n",
    "            qc.ry(\n",
    "                x_params[i] * weight_params[2 * i + 2 * nb_features * j + 1]\n",
    "                + rotation_params[2 * i + 2 * nb_features * j + 1],\n",
    "                i,\n",
    "            )\n",
    "\n",
    "    return qc, x_params, model_params\n",
    "\n",
    "\n",
    "repeat = 1\n",
    "angle_embedding, x_params, m_params = get_angle_embedding(feature_dim, repeat)\n",
    "print(x_params)\n",
    "angle_embedding.draw(\"mpl\", scale=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "42f2b4ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function(s) to generate the encoder circuit\n",
    "# TODO: implement different designs for data encoding (one function for each)\n",
    "def encoder_circuit_1(data_x, _):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        data_x [np.array] : one data example\n",
    "        (...)\n",
    "    Returns:\n",
    "        encoder_circuit [QuantumCircuit] : encoder circuit for input data example\n",
    "    \"\"\"\n",
    "    encoder_circuit = None\n",
    "    return encoder_circuit\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c3d26d",
   "metadata": {},
   "source": [
    "## 2.2. Prepare Ansatz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a0ca1961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1015.99x529.76 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_two_locals(feature_dim, rotations, var_form_rep, ent):\n",
    "    return TwoLocal(\n",
    "        num_qubits=feature_dim,\n",
    "        rotation_blocks=rotations,\n",
    "        entanglement_blocks=\"cx\",\n",
    "        entanglement=ent,\n",
    "        reps=var_form_rep,\n",
    "    )\n",
    "\n",
    "\n",
    "ansatz_tl = get_two_locals(feature_dim, [\"ry\", \"rz\"], 1, \"linear\")\n",
    "ansatz_tl.decompose().draw(\"mpl\", scale=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "579832c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function(s) to generate the ansatz\n",
    "# TODO: implement different ansatz designs (one function for each)\n",
    "def ansatz_1(n_qubits, _):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        n_qubits [int] : number of qubits in the circuit\n",
    "        (...other necessary args -- e.g., n_layers)\n",
    "    Returns:\n",
    "        ansatz [QuantumCircuit] : parameterized ansatz circuit\n",
    "        ansatz_params [ParameterVector] : parameter vector of the ansatz\n",
    "    \"\"\"\n",
    "    ansatz = None\n",
    "    ansatz_params = None\n",
    "    return ansatz, ansatz_params\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "efd50395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1738.39x529.76 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_circuit = angle_embedding.compose(ansatz_tl)\n",
    "var_circuit.draw(\"mpl\", scale=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8c3823ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define function to generate all data encoder circuits (one for each example in the dataset)\n",
    "# Preparing and cache all encoder circuits to concatenate with ansatz later\n",
    "def get_all_encoders(data_mat, encoder_func, _):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        data_mat [np.array] : of shape (n_samples,n_features) -- matrix containing all examples\n",
    "        encoder_func [function] : callable to generate encoder circuit (e.g., encoder_circuit_1)\n",
    "        (...)\n",
    "    Returns:\n",
    "        all_encoders [list(QuantumCircuit)] : list of all encoder circuits (one for each example)\n",
    "    \"\"\"\n",
    "    all_encoders = []\n",
    "    # pseudo-code: for 'example' in 'data_mat': all_encoders.append(encoder_func('example'))\n",
    "    return all_encoders\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a41509c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define function to generate all VQC circuits (one for each example)\n",
    "def get_all_circuits(all_encoders, ansatz_func, _):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        all_encoders [list(QuantumCircuit)] : list of all encoder circuits (one for each example)\n",
    "        ansatz_func [function] : callable to generate ansatz circuit (e.g., ansatz_1)\n",
    "        (...)\n",
    "    Returns:\n",
    "        all_circuits [list(QuantumCircuit)] : list of all VQC circuits (one for each example)\n",
    "    \"\"\"\n",
    "    all_circuits = []\n",
    "    return all_circuits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8de76423",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a quantum instance to wrap the execution of circuits\n",
    "qinstVQC = QuantumInstance(Aer.get_backend(\"statevector_simulator\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5c99842",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define function to calculate target values (can be expectation value or sampling probability)\n",
    "def get_all_target_vals(all_results, _):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        all_results [Result] : output wrapper of all results from executing the VQC circuits\n",
    "        (...)\n",
    "    Returns:\n",
    "        target_val [list(float)] : target values for classification (e.g., expectation value), one for each example\n",
    "    \"\"\"\n",
    "    target_vals = []\n",
    "    return target_vals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6f012781",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define function to calculate cost function given all target vals (there can be multiple designs for cost function)\n",
    "def calculate_cost_function(target_vals, true_labels, _):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        target_vals [list(float)] : target values for classification (e.g., expectation value), one for each example\n",
    "        true_labels [np.array] : vector containing true labels for each example (0 or 1)\n",
    "        (...)\n",
    "    Returns:\n",
    "        cost [float] : total cost for current instance\n",
    "    \"\"\"\n",
    "    cost = 0\n",
    "    return cost\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04033f08",
   "metadata": {},
   "source": [
    "## 2.3 Prepare measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "43889ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for multi-class problem this works\n",
    "def parity(x, num_classes):\n",
    "    return f\"{x:b}\".count(\"1\") % num_classes\n",
    "\n",
    "\n",
    "# for multi-class problem this needs to be changed\n",
    "def one_qubit_binary(x):\n",
    "    return x % 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bafce6",
   "metadata": {},
   "source": [
    "# 3. VQC using Qiskit QNN Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6d2e048b",
   "metadata": {},
   "outputs": [],
   "source": [
    "qasm_sim = Aer.get_backend(\"qasm_simulator\")\n",
    "qinst_qasm = QuantumInstance(qasm_sim, 2048)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29193868",
   "metadata": {},
   "source": [
    "## 3.1. Circuit QNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9386a207",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 2\n",
    "qnn = CircuitQNN(\n",
    "    circuit=var_circuit,\n",
    "    # input_params=fmap_z.parameters,\n",
    "    # input_params=fmap_zz.parameters,  # if your embedding strategy is not a parametrized circuit\n",
    "    input_params=x_params,  # (e.g. amplitude encoding) you will have to do some extra work!\n",
    "    # weight_params=ansatz_tl.parameters,  # if you train your embedding as well, the trainable\n",
    "    weight_params=list(ansatz_tl.parameters)\n",
    "    + list(m_params),  # parameters of the embedding must be added\n",
    "    interpret=one_qubit_binary,\n",
    "    output_shape=num_classes,\n",
    "    gradient=None,\n",
    "    quantum_instance=qinst_qasm,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "090d4a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to run on hardware\n",
    "phys_qubits = [3, 4, 5, 6]\n",
    "N_shots = 2048\n",
    "qinst_hardware = QuantumInstance(\n",
    "    ibmq_jakarta, shots=N_shots, initial_layout=phys_qubits\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3f17d4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "qnn_hardware = CircuitQNN(\n",
    "    circuit=var_circuit,\n",
    "    # input_params=fmap_z.parameters,\n",
    "    # input_params=fmap_zz.parameters,  # if your embedding strategy is not a parametrized circuit\n",
    "    input_params=x_params,  # (e.g. amplitude encoding) you will have to do some extra work!\n",
    "    weight_params=list(ansatz_tl.parameters)\n",
    "    + list(m_params),  # if you train your embedding as well, the trainable\n",
    "    # parameters of the embedding must be added\n",
    "    interpret=one_qubit_binary,\n",
    "    output_shape=num_classes,\n",
    "    gradient=None,\n",
    "    quantum_instance=qinst_hardware,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb530ffe",
   "metadata": {},
   "source": [
    "### Forward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "94b3e417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.56933594, 0.43066406]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The result of the neural network of the shape (output_shape).\n",
    "weights = np.random.random(len(list(ansatz_tl.parameters) + list(m_params)))\n",
    "\n",
    "output = qnn.forward(x_train[1], weights)\n",
    "print(output.shape)\n",
    "output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d2709e",
   "metadata": {},
   "source": [
    "### Backward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2c8f535e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-0.26660156 -0.02856445  0.01513672  0.00195313 -0.13745117\n",
      "   -0.08520508  0.01123047 -0.0012207  -0.25854492 -0.00341797\n",
      "    0.00439453  0.00512695 -0.00170898 -0.00585937  0.01098633\n",
      "    0.00683594  0.07373047 -0.26538086  0.06420898 -0.03076172\n",
      "   -0.00097656  0.00585937 -0.01147461 -0.00268555 -0.01176617\n",
      "    0.04727631  0.05263531 -0.01551357  0.00290943 -0.00187496\n",
      "    0.00081786  0.00056621]\n",
      "  [ 0.26660156  0.02856445 -0.01513672 -0.00195312  0.13745117\n",
      "    0.08520508 -0.01123047  0.0012207   0.25854492  0.00341797\n",
      "   -0.00439453 -0.00512695  0.00170898  0.00585938 -0.01098633\n",
      "   -0.00683594 -0.07373047  0.26538086 -0.06420898  0.03076172\n",
      "    0.00097656 -0.00585938  0.01147461  0.00268555  0.01176617\n",
      "   -0.04727631 -0.05263531  0.01551357 -0.00290943  0.00187496\n",
      "   -0.00081786 -0.00056621]]]\n",
      "(1, 2, 32)\n"
     ]
    }
   ],
   "source": [
    "_, update = qnn.backward(x_train[0], weights)\n",
    "print(update)\n",
    "print(update.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f93622d",
   "metadata": {},
   "source": [
    "### 3.1.1. Prepare encoder, ansatz, and interpreter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e56a3258",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_one_hot_encoding(y):\n",
    "    unique_labels = np.unique(y, axis=0)\n",
    "    y_one_hot = [\n",
    "        (np.eye(len(unique_labels))[np.where(unique_labels == y_i)]).reshape(\n",
    "            len(unique_labels)\n",
    "        )\n",
    "        for y_i in y\n",
    "    ]\n",
    "\n",
    "    return np.array(y_one_hot)\n",
    "\n",
    "\n",
    "y_train_1h = get_one_hot_encoding(y_train)\n",
    "y_test_1h = get_one_hot_encoding(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a60e68f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def callback(nfev, params, fval, stepsize, accepted=None):\n",
    "    \"\"\"\n",
    "    Can be used for SPSA and GradientDescent optimizers\n",
    "    nfev: the number of function evals\n",
    "    params: the current parameters\n",
    "    fval: the current function value\n",
    "    stepsize: size of the update step\n",
    "    accepted: whether the step was accepted (not used for )\n",
    "    \"\"\"\n",
    "    global loss_recorder\n",
    "\n",
    "    loss_recorder.append(fval)\n",
    "    print(f\"{nfev} - {fval}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3509e97",
   "metadata": {},
   "source": [
    "### 3.1.2. Training Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8acb5c32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 - 120.9675367684064\n",
      "6 - 110.41099343447249\n",
      "9 - 104.32639069281649\n",
      "12 - 103.38875634756047\n",
      "15 - 103.20447086647533\n",
      "18 - 102.7086822360657\n",
      "21 - 100.12887927395667\n",
      "24 - 99.70427550646863\n",
      "27 - 98.54615044688762\n",
      "30 - 99.18955569291809\n",
      "33 - 98.03722983278921\n",
      "36 - 98.74356558897485\n",
      "39 - 98.4993566281412\n",
      "42 - 98.75725112122332\n",
      "45 - 98.92299851933859\n",
      "48 - 98.11345082433431\n",
      "51 - 98.21914376188953\n",
      "54 - 98.31074068996598\n",
      "57 - 98.54156444410944\n",
      "60 - 98.16944624559784\n",
      "63 - 98.09346694503775\n",
      "66 - 98.11671747563342\n",
      "69 - 99.112236710191\n",
      "72 - 98.20524459594965\n",
      "75 - 98.2062916880382\n",
      "78 - 99.06799648349386\n",
      "81 - 97.94350566214148\n",
      "84 - 98.51413793553579\n",
      "87 - 97.80320285536989\n",
      "90 - 97.81361556992749\n",
      "93 - 98.60455662591988\n",
      "96 - 98.11264723889698\n",
      "99 - 98.21588786390699\n",
      "102 - 97.62394185169356\n",
      "105 - 98.86300023490145\n",
      "108 - 98.35614444286038\n",
      "111 - 98.33380922693485\n",
      "114 - 98.12644568997065\n",
      "117 - 97.86932178229902\n",
      "120 - 98.73294704474003\n",
      "123 - 97.83115612239523\n",
      "126 - 97.91148820159388\n",
      "129 - 98.1438858515204\n",
      "132 - 97.90041696586741\n",
      "135 - 98.24099540208903\n",
      "138 - 98.7673890788059\n",
      "141 - 97.56510754978228\n",
      "144 - 98.05051903397772\n",
      "147 - 98.32792255886942\n",
      "150 - 98.38892733763132\n"
     ]
    }
   ],
   "source": [
    "from qiskit.algorithms.optimizers import ADAM, COBYLA, SLSQP, SPSA\n",
    "\n",
    "max_itr = 50\n",
    "spsa_opt = SPSA(maxiter=max_itr, callback=callback)\n",
    "loss_recorder = []\n",
    "initial_point = np.random.random((len(list(ansatz_tl.parameters) + list(m_params)),))\n",
    "vqc = NeuralNetworkClassifier(\n",
    "    neural_network=qnn,\n",
    "    loss=CrossEntropyLoss(),  # log of ...\n",
    "    one_hot=True,\n",
    "    optimizer=spsa_opt,\n",
    "    initial_point=initial_point,\n",
    ")\n",
    "\n",
    "x_train_norm = np.array(\n",
    "    [x / np.linalg.norm(x) for x in x_train]\n",
    ")  # normalizing or not ... can depend on the data set you can try both\n",
    "vqc = vqc.fit(x_train_norm, y_train_1h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93a840ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nate/opt/anaconda3/lib/python3.9/site-packages/qiskit/utils/run_circuits.py:727: UserWarning: max_credits is not a recognized runtime option and may be ignored by the backend.\n",
      "  return backend.run(circuits, **run_kwargs)\n"
     ]
    }
   ],
   "source": [
    "# to run on hardware\n",
    "max_itr = 50\n",
    "spsa_opt = SPSA(maxiter=max_itr, callback=callback)\n",
    "loss_recorder = []\n",
    "initial_point = np.random.random((len(list(ansatz_tl.parameters) + list(m_params)),))\n",
    "vqc = NeuralNetworkClassifier(\n",
    "    neural_network=qnn_hardware,\n",
    "    loss=CrossEntropyLoss(),  # log of ...\n",
    "    one_hot=True,\n",
    "    optimizer=spsa_opt,\n",
    "    initial_point=initial_point,\n",
    ")\n",
    "\n",
    "x_train_norm = np.array(\n",
    "    [x / np.linalg.norm(x) for x in x_train]\n",
    ")  # normalizing or not ... can depend on the data set you can try both\n",
    "vqc = vqc.fit(x_train_norm, y_train_1h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f03ad8ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_train_norm = vqc.score(x_train_norm, y_train_1h)\n",
    "print(f\"Score on the normalized train set {score_train_norm}\")\n",
    "\n",
    "x_test_norm = np.array(\n",
    "    [x / np.linalg.norm(x) for x in x_test]\n",
    ")  # normalizing or not ... can depend on the data set you can try both\n",
    "score_test_norm = vqc.score(x_test_norm, y_test_1h)\n",
    "print(f\"Score on the normalized test set {score_test_norm}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f592aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(loss_recorder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8c5646",
   "metadata": {},
   "source": [
    "### 3.1.3. Results Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118209d7",
   "metadata": {},
   "source": [
    "## 3.2. Opflow QNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc3f111b",
   "metadata": {},
   "source": [
    "### 3.2.1. Define measurement operator and circuit state function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fdf5a5b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.opflow import StateFn, Gradient, I, Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b06f0d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PauliOp(Pauli('IIIZ'), coeff=1.0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_parity = Z ^ feature_dim\n",
    "obs_one_qubit = I ^ (feature_dim - 1) ^ Z\n",
    "obs_one_qubit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d39e1f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1497.59x529.76 with 1 Axes>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymap_op = get_my_map(feature_dim, 2)\n",
    "var_circuit_op = mymap_op.compose(ansatz_tl)\n",
    "var_circuit_op.draw(\"mpl\", scale=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b7b258c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ComposedOp([OperatorStateFn(PauliOp(Pauli('IIIZ'), coeff=1.0), coeff=1.0, is_measurement=True), CircuitStateFn(<qiskit.circuit.quantumcircuit.QuantumCircuit object at 0x000002034FA53610>, coeff=1.0, is_measurement=False)], coeff=1.0, abelian=False)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operator = ~StateFn(obs_one_qubit) @ StateFn(var_circuit_op)\n",
    "operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9eeddd5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient = Gradient(grad_method=\"param_shift\")\n",
    "op_qnn = OpflowQNN(\n",
    "    operator,\n",
    "    # input_params=fmap_z_op.parameters,\n",
    "    input_params=mymap_op.parameters,\n",
    "    weight_params=ansatz_tl.parameters,\n",
    "    gradient=gradient,\n",
    "    quantum_instance=qinst_qasm,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6463ed1d",
   "metadata": {},
   "source": [
    "### Forward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7eea1d1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1)\n"
     ]
    }
   ],
   "source": [
    "weights = np.random.random(len(ansatz_tl.parameters))\n",
    "output = op_qnn.forward(x_train[0], weights)\n",
    "print(output.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328c4d1d",
   "metadata": {},
   "source": [
    "### Backward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "06a1a8d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 16)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[-0.46582031, -0.00732422, -0.00390625,  0.01757813,\n",
       "          0.04003906,  0.00878906, -0.01855469,  0.00830078,\n",
       "         -0.83789062,  0.02880859,  0.00732422,  0.01318359,\n",
       "         -0.00683594, -0.01318359, -0.00244141, -0.01220703]]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, update = op_qnn.backward(x_train[0], weights)\n",
    "print(update.shape)\n",
    "update"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca7c675",
   "metadata": {},
   "source": [
    "### 3.1.2. Training Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f8719976",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.algorithms.optimizers import GradientDescent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2820a2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pm = np.array([-1 if y==0 else y for y in y_train])\n",
    "y_test_pm = np.array([-1 if y==0 else y for y in y_test])\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8d907638",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 - 109.27603054046631\n",
      "2 - 108.25804901123047\n",
      "3 - 106.33079814910889\n",
      "4 - 104.87362766265869\n",
      "5 - 101.9281587600708\n",
      "6 - 97.71057415008545\n",
      "7 - 90.23495197296143\n",
      "8 - 83.41075992584229\n",
      "9 - 80.60181045532227\n",
      "10 - 79.26840400695801\n",
      "11 - 79.95791816711426\n",
      "12 - 80.30300807952881\n",
      "13 - 80.20854091644287\n",
      "14 - 79.80421257019043\n",
      "15 - 79.60912609100342\n",
      "16 - 79.73513221740723\n",
      "17 - 79.49081230163574\n",
      "18 - 79.88297939300537\n",
      "19 - 79.32025814056396\n",
      "20 - 80.02000522613525\n",
      "21 - 79.58462333679199\n",
      "22 - 78.97389793395996\n",
      "23 - 79.08813285827637\n",
      "24 - 79.29561614990234\n",
      "25 - 79.15980339050293\n",
      "26 - 79.86717987060547\n",
      "27 - 78.74774742126465\n",
      "28 - 79.38510417938232\n",
      "29 - 78.82346248626709\n",
      "30 - 79.52055835723877\n"
     ]
    }
   ],
   "source": [
    "max_itr = 30\n",
    "gd_opt = GradientDescent(maxiter=max_itr, callback=callback)\n",
    "loss_recorder = []\n",
    "initial_point = np.random.random((len(ansatz_tl.parameters),))\n",
    "op_vqc = NeuralNetworkClassifier(\n",
    "    neural_network=op_qnn,\n",
    "    loss=L2Loss(),\n",
    "    one_hot=False,\n",
    "    optimizer=gd_opt,\n",
    "    initial_point=initial_point,\n",
    ")\n",
    "\n",
    "op_vqc = op_vqc.fit(x_train, y_train_pm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b0bf540d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on the train set 0.5125\n",
      "Score on the test set 0.65\n"
     ]
    }
   ],
   "source": [
    "score_train = op_vqc.score(x_train, y_train_pm)\n",
    "score_test = op_vqc.score(x_test, y_test_pm)\n",
    "print(f\"Score on the train set {score_train}\")\n",
    "print(f\"Score on the test set {score_test}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d046cfd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16a09e28ac0>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_recorder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5fbade",
   "metadata": {},
   "source": [
    "### 3.1.3. Results Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c304add",
   "metadata": {},
   "source": [
    "# 4. Quantum Support Vector Machine (if have time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e272c73",
   "metadata": {},
   "source": [
    "## 4.1. Define feature map(s) (kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f7d09e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "qinst_kernel = QuantumInstance(backend=Aer.get_backend(\"statevector_simulator\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed3a4bfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ZZ-featureMap\n",
    "def ZZ_kernel(\n",
    "    feature_dim, reps, quantum_instance, entanglement=\"linear\", insert_barriers=False\n",
    "):\n",
    "    ZZ_map = ZZFeatureMap(\n",
    "        feature_dimension=feature_dim,\n",
    "        reps=reps,\n",
    "        entanglement=entanglement,\n",
    "        insert_barriers=insert_barriers,\n",
    "    )\n",
    "    ZZ_kernel = QuantumKernel(ZZ_map, quantum_instance=quantum_instance)\n",
    "    return ZZ_kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d396fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = ZZ_kernel(feature_dim, reps=1, quantum_instance=qinst_kernel)\n",
    "kernel.feature_map.decompose().draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14b3bc45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom kernel inspired by Xiaoran's design, with linear entanglement\n",
    "def custom_kernel(feature_dim, reps, quantum_instance, insert_barriers=False):\n",
    "    qc = QuantumCircuit(feature_dim)\n",
    "    data_params = ParameterVector(\"x\", feature_dim)\n",
    "    for layer in range(reps):\n",
    "        for i in range(feature_dim):\n",
    "            qc.ry(data_params[i], i)\n",
    "            qc.rz(data_params[i], i)\n",
    "        for i in range(feature_dim - 1):\n",
    "            qc.cx(i, i + 1)\n",
    "        if insert_barriers:\n",
    "            qc.barrier()\n",
    "    kernel = QuantumKernel(feature_map=qc, quantum_instance=quantum_instance)\n",
    "    return kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efa3f130",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_kernel = custom_kernel(\n",
    "    feature_dim=feature_dim, reps=2, quantum_instance=qinst_kernel, insert_barriers=True\n",
    ")\n",
    "my_kernel.feature_map.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e771ca",
   "metadata": {},
   "source": [
    "## 4.2. Training Implementation (using sci-kit's SVC class)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6f8ca6",
   "metadata": {},
   "source": [
    "### 4.2.1. Training with ZZ-FeatureMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6304df5d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "kernel_ZZ = ZZ_kernel(\n",
    "    feature_dim=feature_dim, reps=1, quantum_instance=qinst_kernel, insert_barriers=True\n",
    ")\n",
    "display(kernel_ZZ.feature_map.decompose().draw(\"mpl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c5bff38",
   "metadata": {},
   "outputs": [],
   "source": [
    "qclf_ZZ = svm.SVC(kernel=kernel_ZZ.evaluate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8304f555",
   "metadata": {},
   "outputs": [],
   "source": [
    "qclf_ZZ.fit(x_train_norm, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03ab1a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Training accuracy: {qclf_ZZ.score(x_train_norm,y_train)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b6304f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Test accuracy: {qclf_ZZ.score(x_test_norm,y_test):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99e99ee",
   "metadata": {},
   "source": [
    "### 4.2.2. Training with custom feature map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3d686cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "kernel_custom = custom_kernel(\n",
    "    feature_dim=feature_dim, reps=1, quantum_instance=qinst_kernel, insert_barriers=True\n",
    ")\n",
    "display(kernel_custom.feature_map.draw(\"mpl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c68dcf2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "qclf_custom = svm.SVC(kernel=kernel_custom.evaluate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6894bb21",
   "metadata": {},
   "outputs": [],
   "source": [
    "qclf_custom.fit(x_train_norm, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e874b98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Training accuracy: {qclf_custom.score(x_train_norm,y_train):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0aebf20",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Test accuracy: {qclf_custom.score(x_test_norm,y_test):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c546e3f3",
   "metadata": {},
   "source": [
    "## 4.3. Results Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef11e93",
   "metadata": {},
   "source": [
    "# 5. Classical Methods (if have time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d1a89d",
   "metadata": {},
   "source": [
    "## 5.1. Classical SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c98ed503",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_linear = svm.SVC(kernel=\"linear\")\n",
    "clf_linear.fit(x_train_norm, y_train)\n",
    "print(\n",
    "    f\"Training accuracy: {clf_linear.score(x_train_norm,y_train):.3f}\\n\"\n",
    "    + f\"Testing accuracy: {clf_linear.score(x_test_norm,y_test):.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e953b613",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_poly = svm.SVC(kernel=\"poly\", degree=3)\n",
    "clf_poly.fit(x_train_norm, y_train)\n",
    "print(\n",
    "    f\"Training accuracy: {clf_poly.score(x_train_norm,y_train):.3f}\\n\"\n",
    "    + f\"Testing accuracy: {clf_poly.score(x_test_norm,y_test):.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25c37276",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_rbf = svm.SVC(kernel=\"rbf\")\n",
    "clf_rbf.fit(x_train_norm, y_train)\n",
    "print(\n",
    "    f\"Training accuracy: {clf_rbf.score(x_train_norm,y_train):.3f}\\n\"\n",
    "    + f\"Testing accuracy: {clf_rbf.score(x_test_norm,y_test):.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78756e99",
   "metadata": {},
   "source": [
    "## 5.2. Classical Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "528f9900",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5393c74",
   "metadata": {},
   "source": [
    "### 5.2.1. ANN w/ 1 Hidden Layer (20) -- ReLU activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a026c018",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clf_nn = MLPClassifier(\n",
    "    solver=\"adam\",\n",
    "    hidden_layer_sizes=(20,),\n",
    "    learning_rate=\"adaptive\",\n",
    "    random_state=1,\n",
    "    max_iter=10000,\n",
    ")\n",
    "clf_nn.fit(x_train_norm, y_train)\n",
    "print(f\"Converged after {clf_nn.n_iter_} iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2b61561",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\n",
    "    f\"Training accuracy: {clf_nn.score(x_train_norm,y_train):.3f}\\n\"\n",
    "    + f\"Testing accuracy: {clf_nn.score(x_test_norm,y_test):.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6075a95",
   "metadata": {},
   "source": [
    "### 5.2.2. ANN w/ 2 Hidden Layers (20,20) -- ReLU activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf424efc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clf_nn_2 = MLPClassifier(\n",
    "    solver=\"adam\",\n",
    "    hidden_layer_sizes=(20, 20),\n",
    "    learning_rate=\"adaptive\",\n",
    "    random_state=1,\n",
    "    max_iter=10000,\n",
    ")\n",
    "clf_nn_2.fit(x_train_norm, y_train)\n",
    "print(f\"Converged after {clf_nn_2.n_iter_} iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21039138",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\n",
    "    f\"Training accuracy: {clf_nn_2.score(x_train_norm,y_train):.3f}\\n\"\n",
    "    + f\"Testing accuracy: {clf_nn_2.score(x_test_norm,y_test):.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ba5063",
   "metadata": {},
   "source": [
    "### 5.2.3. ANN w/ 1 Hidden Layer (20) -- Sigmoid activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cea6da4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clf_nn_sig = MLPClassifier(\n",
    "    solver=\"adam\",\n",
    "    activation=\"logistic\",\n",
    "    hidden_layer_sizes=(20,),\n",
    "    learning_rate=\"adaptive\",\n",
    "    random_state=1,\n",
    "    max_iter=10000,\n",
    ")\n",
    "clf_nn_sig.fit(x_train_norm, y_train)\n",
    "print(f\"Converged after {clf_nn_sig.n_iter_} iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "773662b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\n",
    "    f\"Training accuracy: {clf_nn_sig.score(x_train_norm,y_train):.3f}\\n\"\n",
    "    + f\"Testing accuracy: {clf_nn_sig.score(x_test_norm,y_test):.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63112785",
   "metadata": {},
   "source": [
    "### 5.2.2. ANN w/ 2 Hidden Layers (20,20) -- Sigmoid activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e58913d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "clf_nn_sig_2 = MLPClassifier(\n",
    "    solver=\"adam\",\n",
    "    activation=\"logistic\",\n",
    "    hidden_layer_sizes=(20, 20),\n",
    "    learning_rate=\"adaptive\",\n",
    "    random_state=1,\n",
    "    max_iter=10000,\n",
    ")\n",
    "clf_nn_sig_2.fit(x_train_norm, y_train)\n",
    "print(f\"Converged after {clf_nn_sig_2.n_iter_} iterations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b688b9ef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\n",
    "    f\"Training accuracy: {clf_nn_sig_2.score(x_train_norm,y_train):.3f}\\n\"\n",
    "    + f\"Testing accuracy: {clf_nn_sig_2.score(x_test_norm,y_test):.3f}\"\n",
    ")"
   ]
  }
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