Quantum_Neural_Networks_IRIS.ipynb

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   "execution_count": 1,
   "id": "f933cf56",
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   "source": [
    "import qiskit\n",
    "from qiskit import QuantumCircuit, Aer, IBMQ, QuantumRegister, ClassicalRegister\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\n",
    "from sklearn import model_selection, datasets, svm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ae906df",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = datasets.load_iris()\n",
    "X = d.data\n",
    "Y = d.target\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X,Y,test_size=0.2,random_state=42)"
   ]
  },
  {
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   "execution_count": 3,
   "id": "2bf8bb39",
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   "outputs": [
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "pd.DataFrame(X)"
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   "execution_count": 4,
   "id": "9872d69c",
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf18bc32",
   "metadata": {},
   "outputs": [
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      "[0 0 1 0 0 2 1 0 0 0 2 1 1 0 0 1 2 2 1 2 1 2 1 0 2 1 0 0 0 1 2 0 0 0 1 0 1\n",
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      " 1 2 2 0 1 2 0 1 2]\n"
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   "source": [
    "print(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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      " [7.  3.2 4.7 1.4]\n",
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      " [5.9 3.  4.2 1.5]\n",
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      " [7.1 3.  5.9 2.1]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e434971",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "278bc8ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_map(X):\n",
    "    q = QuantumRegister(N)\n",
    "    c = ClassicalRegister(1)\n",
    "    qc = QuantumCircuit(q,c)\n",
    "    \n",
    "    for i, x in enumerate(X):\n",
    "        qc.rx(x,i)\n",
    "    return qc, c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "416f2812",
   "metadata": {},
   "outputs": [],
   "source": [
    "def variational_circuit(qc, theta):\n",
    "    for i in range(N-1):\n",
    "        qc.cnot(i, i+1)\n",
    "    qc.cnot(N-1, 0)\n",
    "    for i in range(N):\n",
    "        qc.ry(theta[i],i)\n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb7efd55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': 5319, '1': 4681}\n",
      "0.5319\n"
     ]
    },
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8be/k6Q4vxFm+EkJpV65fxLuSsfH6eFYhv8D8XrNl1YuV2z7h0aiepp+/3f4pA6c0wM87CA8355mhJzVEGZpo5BWJj2cVosOMd6xrGt6R7HP7zda//PQs/vP6AVLXO/95rGHDhqkdQYh7VjsI3nzSeNmhtYfHG4fCW90gprZ9s93J19OfazcuA3D1xmV8vfzN1pdWL/Zn/8TPB1bRp8ObpmVPth7Mf944SO6lHA6fdJ5bVEsNUYY0cgdrVOdRjpz+DYDfT+2mRmBd07qbRTcA8HD3Mv0V7szscWMDIRzB0x16t4J3e8KTMRBRDbw8bq13d4WHg6FjIxjb3XhI3t+7zN3ZTaM6bdh1yDifZteh74msfWueUGn1IvfSSeZ88yqvPzsf1z8PrZds5+riiqeHDx7udrqo3gZSQ5ShmcluFUV4SFMquXvx6uwEqvgE80zcaGYsH8mInh8za8UrnDh3gKLim/SWzyEXwu4qe0GnxsYvgwFGpxqXf5BsvM+A2iJCY3F392T0rDjCasTQsHYri/Viwbq/c+HKWd6b/zQAk15czdqd89m0exHF+iKa1utA7aoN1XxJwg6kkatgSHfzzwge0fNjAEY984kacYQQwO2fZeEMTbzE7ZecgeV6MarX3Zfhdm8zlO5thtonnHAKTvR2FVqzf//+8jcSQogySA1RhozIFVK5avnbVLTn/vrrr53mFotCaJ1av8dq1i61akjTpk3v+TFHsk8DEFa7htn39n5ea+gM1n68l3igWPPJRZGRkeX+Rd2woXOdjyu5Rri8z6MWDyZ5fyinotWQtz74FID33xxs9r0zkEPrQgghhIZJIxdCCCE0TBq5sNmsWbPUjiCE0DCpIcqQRi5sFhUVpXYEIYSGSQ1RhjRyYbP4+Hi1IwghNExqiDKkkQshhBAaJo1c2Kxly5ZqRxBCaJjUEGVIIxc227Fjh9oRhBAaJjVEGdLIhRBCCA2TRi6EEEJomDRyYbPFixerHUEIoWFSQ5QhjVwIIYTQMGnkwma9evVSO4IQQsOkhihDPsZUIQc3QP45dZ67clVo0FGd5xYVm1rva3lPC2E9aeQKyT8HF3PUTiGEsuR9LYTzk0PrwmYpKSlqRxBCaJjUEGVIIxc2GzFihNoRhBAaJjVEGdLIhc3at2+vdgSrGQxwueDWz0XF6mURQhhpqYY4MzlHLmz2xx9/qB3BomI97D0JPx+BY7lw5fqtdW9+DTWqQKMQeDQC/L3VyynEg8rZa4hWSCN3oFdnJ7D/+DZcXd1xcXGlekBd+j02lviY3mpHq3CyzsB/f4LzV0pfX6yHnAvGr+/3QtsI6NYMKslvxD2T97UQ6pKy5WD9E9+hf+LbFBcXsWLrDCan9iM8pBkhweFqR7tnjRo1UjvCXfQGSP8VNh24t8dszoJ9p+ClBKhexW7xKqyK9L4WjuOMNUSL5By5Slxd3XjikZco1hfx+6ndasexyZIlS9SOYMZggLSf762J3+78Ffh4HZy5pGyuB0lFeF8Lx3G2GqJV0shVUlh0k5VbZwMQGlxf5TS2GTdunNoRzGw9DNsOW97m3/2NX2W5egP+8wPcLFI224OiIryvheM4Ww3RKs038kuXLjF06FCqVq2Kt7c3bdu2ZcuWLWrHKlPq+on0fMefbn/z4os1bzOm91zCakYDcDL3MCM+akVRceGf204idf0kNeNalJaWpnYEkwtXjYfUlXDuMny3R5l9KcVgUDuBZZbe1xO/6suPmctN246f15NdhzeolFQ4E2eqIVqm6UZuMBhISkpi2bJlTJ06lfT0dIKDg+nUqRO7du1SO16p+j02luUTLrL43VxaNezK7tsKWkhwOI9G9eS/m6ZwMvcwW/cuJznhdRXTasfG/XBDwVH0DweNo3M1/X7WeHTgtYUwJhXeXwlbsqDQCS+ds/S+Hp40nS/Xjufa9Xw271lKZe9AmoXL/VeFUIqmG/nKlSvJyMhg3rx5PP/88yQmJpKWlkZoaChjx45VO55Flb0DGNN7Lj8fWMXWzBWm5ckJr7Nt7wreT+3P8B7TcXN1VzGlNtwoMl5ipqSiYuX3eS827YePv4c9OVCkBwNw9hIs3gGzvocbhepls6S093WAb1WejhvNzBUvk7r+HwzuNlXllEJULE7byPV6PVOnTiUiIgJPT09iYmLIyMigQYMGDB48GIAVK1YQFBREly5dTI/z8PDg2WefZd26dVy9elWt+Fbx8w7kmbgx/Oe7v6HX6wFwc3Wncd04rt3IJ7JOa5UTWpaRkaF2BACO58J1OzS2/aeU36c1fj8Hy/88TXD7IfWSb4/mwrJfHB7LaqW9rzu3fIGTuYfo2fZl/LwDVU4onIWz1BCtc9pGPnDgQCZMmMCQIUNYvXo1ycnJ9O3blyNHjtC8eXMAMjMziYqKQqfTmT22cePGFBUVceCAjdOXHeipuFfIu3yadb98CcCxM3vZd2wrMfUSWLPjC5XTWbZ37161IwBw4rx99puTp8656R8OgK6cbXYcVf/QvyV3vq8BagaFy+Vowoyz1BCtc8rryFNTU5k/fz6bNm0iPj4egA4dOvDrr7+ydOlSUyPPy8sr9TrEwMBA03qA8ePHk5aWxoEDB/j666/v6TNw7/wjoSxTh24kpl6CxW3+NWzTXct8PP1Y+ndjToPBwEdLhzHyqZnUDA5n9Mx2tG7UHX/fhyzuNyNjEy37drAqp7VGjx5d7jbTpk0rd7tp06YpFalMHV6YSXTicLNllmamW1o/6qtb31+7CZ4+ftwsyL/PhPdm+OdXcK/kY3GbYj00jXuGwzuW2jWLEu9rW9jjPW2NVxYY/3Kz9vdelE1LNcQab74/BzC+N27/3p4MVo4knHJEPnnyZLp06WJq4iXCw8Nxd3enSZMmgPFFlvYPeeeyiIgIpk+fTqtWrewXWgHpW2dRv1ZLIkJj8fH04y+dxvHJN2PUjuX0dC6u9tu3zn77LouLlfMiXNw87JxECKEFOoO1Ld9BcnJyqFWrFp9//jkDBw40W9e3b18OHDhgmpHeunVrKlWqdNd5lq+//po+ffqwc+dO0+gdICEhgREjRtzTiNxaOxep97nN/qHQ4lll92nNaYnIyEj2799vcZuGDRsqFalMK3cbb7NqjZKR+O0j77K4usCUPsb/OtKUb+H0xVvnxMvyRleoGWDfLGq9r+3xnrZGyfuivCM6onxaqiHWeOuDTwF4/83BZt87A6cbkefkGKtG9erVzZYXFBSQkZFh1pijoqLYt2/fXYcfMjMzcXNzc5o3QEX13nvvqR0BgFA7NbMa/o5v4gDt6ltu4jqgTrD9m7gQ9uYsNUTrnK6RBwcHA5CVlWW2fMqUKZw+fZrY2FjTsqSkJHJzc1mzZo1pWWFhIYsWLSIxMREfH8vnGcX9SU5OVjsCAA8/VP7kMFvUDbbDTq3QKgzqVS19nQ5wc4VeLRwaSQi7cJYaonVON9ktLCyM6OhoJk2aRGBgICEhISxevJhVq1YBmI3Iu3fvTlxcHAMGDGDKlCnUqFGDGTNmkJ2dzcKFC9V6CQ8Maw6LOYK/NzSsqfzlYq1VmmDt5gpDOhgvQfv5d+N15CXqBMMzLaBWkDrZhFCSs9QQrXO6EbmLiwtpaWlERUUxbNgwBgwYQHBwMCkpKbi5uREdHW3aVqfTkZ6eTo8ePRgzZgzdu3fn3LlzrF271qzhO5P92T/xyoxHGT0rjtnp5rM1v1z7LkM+jOHV2QkszvhQpYTalKDwWZSIahCi4qFrDzdIbgV/f/rWsjefhFGdnaOJz04fzehZccxc8cpd6wwGA0M+jGHVT3MBOH52H6/MaMsrM9oy77t3AFizYx6vzk7g1dkJPDUugMMndzsyvhAVitONyAHq16/Pxo0bzZY999xzREZG4uXlZbbc39+fOXPmMGfOnDL3V1hYSHFxMXq9nsLCQq5fv06lSpVUucSkmn8d/jlkAx7unkxO7c/R03uoW6OJaf2Qbv8itn6iw3NpXYMa0Pxh+OXY/e/LzRV6O8kFDt6Vbn1fw1+1GGYO5fzK9ZtXmTZ8M9OXDOPgiR00qNXStH7b3nT8fW+dG1i57RMGdZ1MdFh73vy0E1cKLtK55Qt0bvkCxcVFDJ/egno1Y9R4KUJUCE43Ii/LnTPQ78VLL72El5cXmzdvpl+/fnh5eXH8+HGFE1on0K86Hu6eALi6uOFyx6VTc1e9yRtzEjUxQklISFA7gpmnW8BDlS1vM+qr8mesP9MCqvopl6ui2Xd8G7ERxj82YyMS2X98u9n6jbsXEh/Tx/RznWpRXL1+iWK98Sbx7m63/jr57egPRIe1l+u2H1DOVkO0ShON/MqVK2RlZZlNdLsX8+bNw2AwmH09/PDDyoa8R0dO/calq7nUqXbrhjY9273MrFG/8PLTs5m5YqSK6awze/ZstSOY8akEwx+7vyb8VHNoIzcfs+jK9Yt4VzL+I/t4ViG/4IJp3Y6Da4gOi8fV5dbBvqbhHfl05WsMnNKAyDptqOR+66jalj1Ladv4KceFF07F2WqIVmmikfv6+lJcXMzIkc7f3Kxx+VoeM5aP4NXen5stL7kHdehDEWrEumfDhg1TO8JdAnxgdGdoXe/eHzf8MYiXKxbL5evpz7UblwG4euMyvl7+pnWrf55L55YDzLaft+Zt3uq7gC/eyOLY6T2cyTsGGM+lZx7dQpOw9o6KLpyMM9YQLXLKc+QVWXFxEe8v/AsvdfsngX7m18pfvX4ZH08/Ll3NpbhYwc/ktJNNmzapHaFUXh7wbGvjZVw/HITfToC+jAuzH6oMbSOMM9Q95YPmrNKoThu+3T6H+Jhkdh36nsdbvGBad/KPQ4yf15PcyyfBYKBx3XYYDAYqewXi4uKCt2cVCm4Yb3l78MQOIkJjcbXjnfmEc3PWGqI10sgdLOO3NLJO7GDut28CMOiJyWzYncqInh/z2crXOXomE4NBz6Cu76ucVPvCqhq/rt00fgDKyQtQcBNcXCDYF2oFwkN+4CKnZ+9JRGgs7u6ejJ4VR1iNGBrWbsWM5SMZ0fNj5ozZDRhnpRfri6hdtSF9OrzJB4uew8XFldpVI02TO3/MXEa7xk9beCYhhDWkkTtYx2Z96disr9myRg+3AWBUr7Jn3gvbeXtA/erGL6GMlKTpZj+P6Pmx2c+dW75g+r5+aHOmj9h61z4GdZ1sl2xCPGg0cY5cOCe5kYMQ4n5IDVGGNHJhs6+//lrtCEIIDZMaogw5tK6QymXcG7siP/f48ePlXskVnFrvLTV/n4TjSA1RhjRyhTToqHYCIZQn72shnJ8cWhdCCCE0TBq5sNmsWbPUjiCE0DCpIcqQRi5sFhUVpXYEIYSGSQ1RhjRyYbP4+Hi1IwghNExqiDKkkQshhBAaJo1cCCGE0DBp5MJmLVu2VDuCEELDpIYoQxq5sNmOHTvUjiCE0DCpIcqQRi6EEEJomDRyIYQQQsOkkQubLV68WO0IQggNkxqiDGnkQgghhIZJIxc269Wrl9oRhBAaJjVEGfLpZwo5uAHyz6nz3JWryqdUCaF1atUQqR/aJ41cIfnn4GKO2imEEFolNUTYSg6tC5ulpKSoHUEIoWFSQ5QhjVzYbMSIEWpHEEJomNQQZUgjFzZr37692hEqrEvX4OcjsGTnrWULfoSN++FYLhgM6mUTQilSQ5Qh58iFzf744w+1I1Q4OXmwLhP25ID+jma985jxC6B6FUhoCK3qgYvO0SmFUIbUEGVII3egV2cnsP/4Nlxd3XFxcaV6QF36PTaW+JjeakcTKivWw5o98P3euxt4ac5cgkU/wY6j0K8NBPnaP6NQn9QQURo5tO5g/RPf4ZuJV1j67nk6t3yByan9OJl7WO1YNmnUqJHaESqEomL4YjOszbSuid/u93Mw7Ts4dcE+2YTzkRoi7iSNXCWurm488chLFOuL+P3UbrXj2GTJkiVqR6gQ/vszZN7HZUdXbsDsDXCpQLlMwvlJDRElNN/IL126xNChQ6latSre3t60bduWLVu2qB2rXIVFN1m5dTYAocH1VU5jm3HjxqkdQfP+lw07jlje5t/9jV+W5F+HtJ9lEtyDRGqIKKHpRm4wGEhKSmLZsmVMnTqV9PR0goOD6dSpE7t27VI7XqlS10+k5zv+dPubF1+seZsxvecSVjMagJO5hxnxUSuKigv/3HYSqesnqRnXorS0NLUjaFpRsfms9PuVmQP7Tim3P1tdvQGbD0L6r8bTBWcvqZ2oYrFUQyZ+1ZcfM5ebth0/rye7Dm9QKWn5pIYoQ9ONfOXKlWRkZDBv3jyef/55EhMTSUtLIzQ0lLFjx6odr1T9HhvL8gkXWfxuLq0admX3bb9kIcHhPBrVk/9umsLJ3MNs3buc5ITXVUwr7Om3E3BZ4cPhmw8qu797YTAYJ+yNW2r8A2XDflj1P5i8EuZmwPVC9bJVJJZqyPCk6Xy5djzXruezec9SKnsH0ixc7r9a0TltI9fr9UydOpWIiAg8PT2JiYkhIyODBg0aMHjwYABWrFhBUFAQXbp0MT3Ow8ODZ599lnXr1nH16lW14persncAY3rP5ecDq9iaucK0PDnhdbbtXcH7qf0Z3mM6bq7uKqYU9rTjqPL7PHAa8lU6V74uE1b/ZpyBf6fMHPh0I+hLWSdsU1oNCfCtytNxo5m54mVS1/+Dwd2mqpxSOILTNvKBAwcyYcIEhgwZwurVq0lOTqZv374cOXKE5s2bA5CZmUlUVBQ6nfmFtI0bN6aoqIgDBw6oEd1qft6BPBM3hv989zf0f1Y4N1d3GteN49qNfCLrtFY5oWUZGRlqR9AsgwGyz9tn39l59tmvJVdvwJpMy9sc+cM5Dv1XJKXVkM4tX+Bk7iF6tn0ZP+9AlRNaJjVEGU7ZyFNTU5k/fz7p6em89tprdOjQgbFjx9KmTRuKiopMjTwvL4+AgIC7Hh8YGGhaf+PGDV544QVCQkLw9/enY8eO7N+/36Gvx5Kn4l4h7/Jp1v3yJQDHzuxl37GtxNRLYM2OL1ROZ9nevXvVjqBZl68bm589qHEp2q7jpY/Eb6cDtmvzKimndmcNAagZFE5IcLiKqawjNUQZTnlDmMmTJ9OlSxfi4+PNloeHh+Pu7k6TJk0A42S3O0fjgNmyoqIiwsPDmThxItWrV+eDDz6gT58+/Pbbb1ZlKW3/pZk6dCMx9RIsbvOvYZvuWubj6cfSvxuHUAaDgY+WDmPkUzOpGRzO6JntaN2oO/6+D1ncb0bGJlr27WBVTmuNHj263G2mTZtW7nbTpk1TKlKF4l8tnP/3r0Nmy8qbmV7W+lFfmf88/u8TeTzt7ftId+8eTZ5Ei+5votOVPTYwABu37ualDs0cF+wevbLAOO3f2t97JSlRQ2xhj/oBFa+GvPn+HMD43rj9e3syWHkZitONyHNycsjMzKR377vvVJSdnU1UVBSVKlUCICgoiLy8u9/AJcsCAwPx8fHh7bffJiQkBFdXV0aOHMmePXu4fv26fV+IDdK3zqJ+rZZEhMbi4+nHXzqN45NvxqgdS9hBcdFNu+1bb8d9l+VmwSWLTRxAry/mxlW5c40QStMZrG35DrJ9+3batGnDt99+S9euXU3LCwoKqFevHl27dmXu3LkADBo0iPT0dM6dO2f2l9G4ceOYPHkyFy9exMfHx2z/a9asYejQoRw9quxMo52L1PssYf9QaPGssvu0Zn5BZGRkuacpGjZsqFSkCqVYD299DYXF5W9bMhK/c+RdlufbQuzDNkezyR/5MDG9/O16tYR2TnzJc8m/cXlHR+xBrRpij/oBFa+GvPXBpwC8/+Zgs++dgdONyIODgwHIysoyWz5lyhROnz5NbGysaVlSUhK5ubmsWbPGtKywsJBFixaRmJh4VxO/cOECKSkpTJw40Y6v4MHx3nvvqR1Bs1xdIOTu6R2KqKXC/KaHKkNMrbLX63RQ2RNa1HVcJuH8pIYow+nOkYeFhREdHc2kSZMIDAwkJCSExYsXs2rVKgDTRDeA7t27ExcXx4ABA5gyZQo1atRgxowZZGdns3DhQrP9FhQU0KNHD/r06UO/fv0c+poqquTkZLUjaFpMbeNHkiqphj8EV1Z2n9bq1wau3YRDZ40T224/1OdbCYY/Bp5yNaW4jdQQZTjdiNzFxYW0tDSioqIYNmwYAwYMIDg4mJSUFNzc3IiOjjZtq9PpSE9Pp0ePHowZM4bu3btz7tw51q5da9bwi4qKSE5OJiIiQkbjCoqMjFQ7gqa1CgM3V2X32TbCOPpVQyV3GNYRBidAk9tG571bwds9jH9kCHE7qSHKcLoROUD9+vXZuHGj2bLnnnuOyMhIvLy8zJb7+/szZ84c5syZU+b+XnzxRfR6PZ9++qld8t6PM3nHGPnxI9SuGombqwcfDF6rdiThID6VoFOU8SYqSqheBVrXU2ZftnJxgUYhxq+S881tI9TNpHWz00eTlbOT8JBYUpKmm5b/7/cM5n77Buh0PN7iBbq3Gcq5C9n8878vUKwvIqntCOJjbo14Zy5/mfxrebzVb4EaL0PYkdONyMuyc+dOs1G2tY4fP878+fPZsGED/v7++Pr64uvrS3Z2th1S2qZ5RCf+NWyTNPEHUGKUMue0XXTGQ9tKj/CFug7l/Mr1m1eZNnwzRUU3OXhih2nd4h/+xTvPpTE9ZStr/7znxKKNHzCgy0T+OXQjq36aS3FxEQAX8s9y5sIxNV6CcABNNPIrV66QlZVlNtHNWnXq1MFgMFBQUMCVK1dMX7Vr17ZDUtvs/n0jo2fFseQH57he0loJCQlqR9A8VxcYFA/BvmVvM+oryzPWdUD/R6F2kOLxhMr2Hd9GbEQiALERiew/vt207uFqUVy9fonC4ht4ehgn9p7JO0LdmtG4urgSULkaJ88b78CzZPM0erYd6fgXUA6pIcpwykPrd/L19aW42IrrdDQo0K8GX7yZhYdrJcbNS6JZ+GOmTzJydrNnz1Y7QoXg7w0jH4cvt8Dv5+7tsT4e0LcNNA61TzahrivXL1IzyHi+xMezCsfO3roT2qONe/L2f7oB8JdOxo8DDX2oAb/9nkFMvQT2Z2/nyrULXL6Wx6UrfxAS7HznOKSGKEMTI/KKzMOtEl4ePri6utE6shvHzpZzw2onMmzYMLUjVBhVvCAlEZ5pYbxMqzwuOmjxMLzVTZp4Rebr6c+1G5cBuHrjMr5e/qZ1n618nWkpW5j35iHW/fIl129eo2/Hv7Lqp0+Z8H+9qf1QQwIqV2PZ5ukktR2h0iuwTGqIMjQxIq/Irl3Px9vTeL3Q3mM/ktTO+Q5/lWXTpk1qR6hQXHQQ1wDahMOeHNh/Ck7kQd4V44eseFcyXnte9yFoWReqeKudWNhbozpt+Hb7HOJjktl16Hseb/GCaZ2Liyu+nv64u3ngonOhuLiQgMrVeO+F5dwoLOCDhc9RPbAuZ/KO8vmqv3KjqICTuYfI+N/XZpPg1CQ1RBnSyFW25+hm5q95B3e3SjR+uB2RtR9RO5JQmZsrNKtj/BIPtojQWNzdPRk9K46wGjE0rN2KGctHMqLnx/RJeJM3P01Ep3OhZcMn8PGqwk/7vyUtYyouOldefPID433B+xo/TOVM3jHmffe20zRxoRxp5Cp7JLIrj0R2LX9DIcQD6fZLzgBG9PwYgJYNu9CyYRezdY9EPskjkU+Wup/qgQ/LpWcVlJwjFzZzpo+DFUJoj9QQZciIXCGVqz54z/3111/LLRaFUIhav8dq1i6pIcqQRq6QBh3VTuB448ePl19CIRQiNUTYSg6tCyGEEBomjVwIIYTQMGnkwmazZs1SO4IQQsOkhihDGrmwWVRUlNoRhBAaJjVEGdLIhc3i4+PVjiCE0DCpIcqQRi6EEEJomDRyYbOWLVuqHUEIoWFSQ5QhjVzYbMeOHWpHEEJomNQQZUgjF0IIITRMGrkQQgihYdLIhc0WL16sdgQhhIZJDVGGNHIhhBBCw6SRC5v16tVL7QhCCA2TGqIM+fQzhRzcAPnn1HnuylUfzE9OEqIiUauGSP3QPmnkCsk/Bxdz1E4hhNAqqSHCVnJoXdgsJSVF7QhCCA2TGqIMaeTCZiNGjFA7ghBCw6SGKEMaubBZ+/bt1Y4gnNDNIsg+f+vnK9fVyyKcm9QQZcg5cmGzP/74Q+0IwklcvQE/H4EdR+D0JTAYbq17ewn4e0N0LWhbH6r5qZdTOBepIcqQRu5Ar85OYP/xbbi6uuPi4kr1gLr0e2ws8TG91Y4mhE30BvgxC77ZbRyJl+XiNfjhoPGrdT3o2Rw83R0Ws8KQGiJKI43cwfonvkP/xLcpLi5ixdYZTE7tR3hIM0KCw9WOds8aNWqkdgShouuFMG8zHDh9b4/b/jscPA1DOkL1KvbJVpFJDRF3knPkKnF1deOJR16iWF/E76d2qx3HJkuWLFE7glDJzSL4dOO9N/ESF67Bx+vg7CVlcz1IpIaIEppv5JcuXWLo0KFUrVoVb29v2rZty5YtW9SOVa7Copus3DobgNDg+iqnsc24cePUjiBUsnI3HCnn9Oa/+xu/ynL1BszbAkXFikZ7YEgNESU03cgNBgNJSUksW7aMqVOnkp6eTnBwMJ06dWLXrl1qxytV6vqJ9HzHn25/8+KLNW8zpvdcwmpGA3Ay9zAjPmpFUXHhn9tOInX9JDXjWpSWlqZ2BKGCI+eM57qVcPoirNurzL7u1+0T9PSGsrdTm6UaMvGrvvyYudy07fh5Pdl1eINKScsnNUQZmm7kK1euJCMjg3nz5vH888+TmJhIWloaoaGhjB07Vu14per32FiWT7jI4ndzadWwK7tv+yULCQ7n0aie/HfTFE7mHmbr3uUkJ7yuYloh7va9wo034wDcsDBRzt70Bth+GKasurXs78thXablCXxqsVRDhidN58u147l2PZ/Ne5ZS2TuQZuFy/9WKzmkbuV6vZ+rUqURERODp6UlMTAwZGRk0aNCAwYMHA7BixQqCgoLo0qWL6XEeHh48++yzrFu3jqtXr6oVv1yVvQMY03suPx9YxdbMFablyQmvs23vCt5P7c/wHtNxc5WpvcJ5nL8C+08pu8/rhfDrMWX3aS29ARZug0U/wZmLt5ZfvAbf/g9mfq/uHxmWlFZDAnyr8nTcaGaueJnU9f9gcLepKqcUjuC0jXzgwIFMmDCBIUOGsHr1apKTk+nbty9HjhyhefPmAGRmZhIVFYVOpzN7bOPGjSkqKuLAgQNqRLean3cgz8SN4T/f/Q29Xg+Am6s7jevGce1GPpF1Wquc0LKMjAy1IwgHO3wW7HHUOeuMHXZqhZ+PwI6jxu9Le13Hz8Oq/zk00j0prYZ0bvkCJ3MP0bPty/h5B6qc0DKpIcpwykaemprK/PnzSU9P57XXXqNDhw6MHTuWNm3aUFRUZGrkeXl5BAQE3PX4wMBA03qA/v37U61aNapUqUKrVq3Ytm2b415MOZ6Ke4W8y6dZ98uXABw7s5d9x7YSUy+BNTu+UDmdZXv3OsnJTeEwJ/K0tV9LDAb44QDoytlu22HnHZXD3TUEoGZQuCYuR5MaogynvI588uTJdOnShfj4eLPl4eHhuLu706RJE8A42e3O0Thw17KxY8fyxRdf4OHhwbfffsszzzzDqVPWHR8sbf+lmTp0IzH1Eixu869hm+5a5uPpx9K/G6uYwWDgo6XDGPnUTGoGhzN6ZjtaN+qOv+9DFvebkbGJln07WJXTWqNHjy53m2nTppW73bRp05SKJJxAt1HLqNeip9kySzPTLa0f9dWt70/nXkOn87m/cPfIw8uPYZ+Vf/3bzSIIi2rHqawf7ZpHiRpiC3vUD6h4NeTN9+cAxp5w+/f2ZDBYd/zL6UbkOTk5ZGZm0rv33Xcqys7OJioqikqVKgEQFBRkGnXfrmRZyci8UaNGeHh4YDAYcHd358yZM1y/7nw3gE7fOov6tVoSERqLj6cff+k0jk++GaN2LCFusVPhsndBLPU5XVztsq0QjuZ0I/KcHOMH8lavXt1seUFBARkZGXTt2tW0LCoqivT09LtG5pmZmbi5udGwYUPTsv79+7NkyRJu3LhBSkoKnp6eVuWx9i+inYvu/7OEk9qaf6Rf++hetI/uVe7j4uMTMMxW9sylNfMLpk2bZpp4WJYPP/xQqUjCCSzabrwz2+1uH1nfrmQkXtb62wX7e1n9u6YUvQEmrIAL5cyJddHBgV0Z+FpXMmymRA0p8caz86ze1h71AypeDXnrg08BY0+4/Xtn4HQj8uDgYACysrLMlk+ZMoXTp08TGxtrWpaUlERubi5r1qwxLSssLGTRokUkJibi43PrUN1XX31Ffn4+y5cvp3Vr555EphXvvfee2hGEg4Xaae5ULRXmZLnoIK6c+6jogNiHsXsTf1BJDVGG043Iw8LCiI6OZtKkSQQGBhISEsLixYtZtcp4kWfJRDeA7t27ExcXx4ABA5gyZQo1atRgxowZZGdns3Dhwrv27e7uTlJSEjExMbRq1Yr69bV5NyRnkZycrHYE4WD1qtpnv2F22m954hpAZk7pd6nTAf4+0KOZw2M9MKSGKMPpRuQuLi6kpaURFRXFsGHDGDBgAMHBwaSkpODm5kZ0dLRpW51OR3p6Oj169GDMmDF0796dc+fOsXbtWrOGf6ebN29y7NgxB7yaii0yMlLtCMLBavhD3WBl9+nmAi3rKrtPa7m7wtCO0CHS/NPYXF2gRV0Y3Rn8vNTJ9iCQGqIMpxuRA9SvX5+NGzeaLXvuueeIjIzEy8v8t8rf3585c+YwZ86cUvd1/vx5NmzYwJNPPombmxufffYZp06dMjtE70i5l07xzhfdOH52H9/84wqurrf+Fxw+uZuPl6Xg4uLCwC6TaBIWp0pGISzp0AiO/qDc/lrVU/fQtYcbJMXCE9Fw8oLxsrRqVcCnknqZbjc7fTRZOTsJD4klJWm62TqDwcDQaU1JajuSro+8yLXr+UxO7cfla3l0az2ETi2eZ82OeazdOQ+AI6f/xz+HbCQ8pKnjX4iwG6cbkZdl586dFkfZlnz88cfUqFGDatWqsWjRIr799lvTuXhH8/MOZMrg9UTWvvs8/fy143j7L/9l8otrSF0/UYV0QpSvSSjE1FJmX/7e0L2pMvu6Xx5uUPch42F+Z2nih3J+5frNq0wbvpmiopscPLHDbP22ven4+946L7Hqp8/o0KwvHw7/gdU/z6Ww6CadW77Av4ZtYsrg76nqX4d6NWMc/TKEnTnliPxOV65cISsri+HDh9/zY4OCgvjhBwWHD/fJw90TD/fShx9XCi7wkH8oANcLr3KjsIBK7s57XC8hIUHtCEIFOh30bgWnLsIf+WVvV95sdTcXeO5R8PJQNF6Fsu/4NmIjEgGIjUhk//HtNKjV0rR+4+6FxMf0ubV99jZGPjUTVxdXwmrGkPPHQerWMN5347ejPxAd1l6VS/3KIjVEGZpo5L6+vhQXV/zPOqzi8xBHz2QS4FuNY2cyuVJw0akb+ezZs9WOIFTi6wkpiTB7PZy9fO+P93CDQe2hXjXls1UkV65fpGZQPQB8PKtw7OytO6HtOLiG6LB4XF3cKNYbbz13teAiPpX8TNvnF1wwbb9lz1LimjzjwPTlkxqiDM0cWn8QvNj1fT5JH8P0JUOpWyOaKj7qHP631rBhw9SOIFTk7w1jupR/Cded6lWFN7pCgxr2yVWR+Hr6c+2G8S+lqzcu4+vlb1q3+ue5dG45wGx7Hy9/rv65/bXrl/H1NG5vMBjIPLqFJmHtHZLbWlJDlKGJEfmDIvSh+nwweC2XruYyO32003/y2aZNm9SOIFRWyR2eaQmP1IMfD8Evx0r/6E8dxsbdrj40CjFewy3K16hOG77dPof4mGR2Hfqex1u8YFp38o9DjJ/Xk9zLJ8FgoHHddjSq3YZdh9YTH5PM76d2E1q1AQAHT+wgIjQWVye7Q53UEGVII3ewouJC/jb3CY6c/h9vze3MwCcmsf7XBYzo+TGrf/6c9b8uoJK7NyOemqF2VCGsFhoIfR6BXi3hzCU4dcH4QSNuLvCQH4QEmF/eJawTERqLu7sno2fFEVYjhoa1WzFj+UhG9PyYOWN2A7BmxzyK9UXUrtqQIL+aTE7tx4ofP6brI4PxcDPO2vsxcxntGj+t4isR9iSN3MHcXN2ZMuR7s2WRtR8B4IlWg3ii1SA1YgmhCFcXY9MOuftDCYWN7rzkbETPj81+7tzyBdP3Pp5+/GPgyrv2MajrZLtkE85BzpELm+3fv1/tCEIIDZMaogxp5MJmX3/9tdoRhBAaJjVEGXJoXSGVVbpXtJrPPX78eLlXshAKUev3WM3aJTVEGdLIFdKgo9oJhBBaJjVE2EoOrQshhBAaJo1c2GzWrFlqRxBCaJjUEGVIIxc2i4qKUjuCEELDpIYoQxq5sFl8fLzaEYQQGiY1RBnSyIUQQggNk0YuhBBCaJhcfiZK1bBhw3K3GT9+vFXbCSEePFJDHEdG5MJm7777rtoRhBAaJjVEGdLIhRBCCA2TRi6EEEJomDRyIYQQQsOkkQshhBAaJo1cCCGE0DBp5EIIIYSGSSMXQgghNEwa+X0YOXIkoaGhuLnJfXWEEEKUbtOmTURFRREeHs6LL75IcXGxovuXRn4f+vTpwy+//KJ2DCGEEE5Kr9fz4osvkpaWxuHDh7l8+TILFixQ9Dmkkd+Hdu3aUa1aNbVjCCGEcFI7duygZs2aNGrUCIBBgwaxZMkSRZ9DjgkLIYQQdzh3/iIL09fftXz6F0vu+t7N1ZXnn3mcyj7ed22fk5NDrVq1TD/Xrl2bEydOKJpVRuRCCCHEHaoG+RNWuwanz53n9LnzpuV3fn/63HmiI8NKbeIABoMBnU5n9rPSpJELIYQQpejSvhVVg/wtbhNWuyZtWzQpc32tWrXIzs42/XzixAlCQ0OVighIIxdCCCFK5e7uRp9uHXFx0ZW6vpKHO8lPJuCiK309QIsWLTh58iT79u0D4PPPP+fpp59WNGeFb+SnTp2ib9++BAQE4OvrS+fOndm7d68i+x4yZAihoaEUFxcTGhpKSkqKaZ09Dp8IIYRwrJDqwSS2bV7qup6Pt8Pfz9fi411dXfnss8/o1asX9erVw9fXl+eee07RjDpDBe44BQUFNGvWDL1ez6RJk/D29mbSpEkcOHCA3bt3K35443aLvtlAQBVfOrdvZbfnEEIIYX/Fej1zvvqG7FNnTcuaNKhLv6REs/PfaqnQI/K5c+eSlZXFsmXL6NWrF127duWbb76hqKiISZMm2e15T587z+59h9HpKvQ/rxBCPBBcXVxI7paAh7vxQq/Kvt707BznFE0cKkAj37NnD8888wzBwcF4enoSERHB2LFjAVixYgXNmjUjKirKtH1AQADdu3dn2bJldsu0/sdfqeThTrsWje32HEIIIRwnOKAKT3ZsDUCvLu3x8fJUOdEtmr6O/JdffqF9+/bUqVOHqVOnUrt2bY4ePcrWrVsByMzMpHPnznc9rnHjxixYsIDz588TFBRk8Tne+uBTm/P9/aMvbX6sEEII5/TF4u8c8jzvvznYqu003chfffVVKleuzPbt2/Hz8zMtHzRoEAB5eXkEBATc9bjAwEDT+vIauRBCCOHMNNvIr127xubNmxk5cqRZE79Taecw7uW8hrV/EYHx3Pj0L5bQ8dFYHo9rYfXjhBBCCFtptpFfuHABvV5PSEhImdsEBgaSl5d31/KSZSUjc0tsObS+YeuvbNj66z0/TgghhChh7UBSs5PdAgICcHFx4eTJk2VuExUVVeo145mZmVSvXl0OqwshhNA8TV9H3qFDB/bv309WVlaph9c/+ugjRo0axd69e4mMjATg4sWL1K1bl2effZbZs2crlmXBsnUcOpbDm0P74u1EsxmFEEJUbJpu5LfPWn/jjTeoU6cO2dnZbN68mblz53Lt2jWaNm2KTqczuyHMvn372L17t9kn0twPOTcuhBBCLZo9tA7QvHlztm3bRkREBKNHj6Zr165MnDiRmjVrAuDt7c3GjRtp2rQpgwYNolevXnh5ebFp0ybFmjhA3sV8AqtUluvGhRBCOJymR+TORK/X4+Ki6b+LhBBCaJA0ciGEEELDZAgphBBCaJg0ciGEEELDpJELIYQQGiaNXAghhNAwaeRCCCGEhkkjF0IIITRMGrkQQgihYdLIhRBCCA2TRi6EEEJomDRyIYQQQsOkkQshhBAaJo1cCCGE0DBp5EIIIYSGSSMXQgghNEwauRBCCKFh0siFEEIIDZNGLoQQQmiYNHIhhBBCw6SRCyGEEBomjVwIIYTQMGnkQgghhIZJIxdCCCE0TBq5EEIIoWHSyIUQQggNk0YuhBBCaJg0ciGEEELDpJELIYQQGvb/AQ3TJsQV//u8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 641.378x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc, c = feature_map(X_train[5])\n",
    "qc.barrier()\n",
    "qc = variational_circuit(qc, np.random.rand(N))\n",
    "qc.barrier()\n",
    "qc.measure(0, c)\n",
    "\n",
    "shots = 1E4\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "job = qiskit.execute(qc, backend, shots = shots)\n",
    "result = job.result()\n",
    "counts = result.get_counts(qc)\n",
    "print(counts)\n",
    "print(counts['0']/shots)\n",
    "\n",
    "qc.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b89b57f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def quantum_nn(X, theta, simulator=True):\n",
    "\n",
    "    qc, c = feature_map(X)\n",
    "    qc = variational_circuit(qc, theta)\n",
    "    qc.measure(0, c)\n",
    "    shots = 1E4\n",
    "    backend = Aer.get_backend('qasm_simulator')\n",
    "    \n",
    "    if simulator == False:\n",
    "        \n",
    "        shots = 5000\n",
    "        provider = IBMQ.load_account()\n",
    "        backend = provider.get_backend('ibmq_qasm_simulator')\n",
    "    \n",
    "    job = qiskit.execute(qc, backend, shots=shots)\n",
    "    result = job.result()\n",
    "    counts = result.get_counts(qc)\n",
    "    \n",
    "    return counts['1']/shots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33ce773e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss(prediction, target):\n",
    "    return (prediction - target)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a4380d23",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient(X, Y, theta):\n",
    "    delta = 0.01\n",
    "    grad = []\n",
    "    for i in range(len(theta)):\n",
    "        dtheta = copy.copy(theta)\n",
    "        dtheta[i] += delta\n",
    "        pred1 = quantum_nn(X, dtheta)\n",
    "        pred2 = quantum_nn(X, theta)\n",
    "        \n",
    "        grad.append((loss(pred1,Y) - loss(pred2,Y)) / delta)\n",
    "    \n",
    "    return np.array(grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d9b82b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(X, Y, theta):\n",
    "    \n",
    "    counter = 0\n",
    "    for X_i, Y_i in zip(X, Y): \n",
    "        \n",
    "        prediction = quantum_nn(X_i, theta)\n",
    "        if prediction < 0.5 and Y_i == 0:\n",
    "            counter += 1\n",
    "        elif prediction >= 0.5 and Y_i == 1:\n",
    "            counter += 1\n",
    "            \n",
    "    return counter/len(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5f0f5df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch \t Loss (in %) \t Training Accuracy (in %)\n",
      "0 \t 94.610 \t 56.667\n",
      "1 \t 83.339 \t 55.833\n",
      "2 \t 84.539 \t 56.667\n",
      "3 \t 76.006 \t 56.667\n",
      "4 \t 76.838 \t 55.000\n",
      "5 \t 76.094 \t 57.500\n",
      "6 \t 75.027 \t 56.667\n",
      "7 \t 75.115 \t 55.000\n",
      "8 \t 75.917 \t 55.833\n",
      "9 \t 75.646 \t 55.833\n",
      "10 \t 76.160 \t 55.000\n",
      "11 \t 76.675 \t 55.000\n",
      "12 \t 76.200 \t 56.667\n",
      "13 \t 77.776 \t 54.167\n",
      "14 \t 84.928 \t 58.333\n",
      "15 \t 78.709 \t 57.500\n",
      "16 \t 75.468 \t 55.833\n",
      "17 \t 75.440 \t 56.667\n",
      "18 \t 75.907 \t 55.833\n",
      "19 \t 76.146 \t 57.500\n"
     ]
    }
   ],
   "source": [
    "eta = 0.05\n",
    "loss_list = []\n",
    "theta = np.ones(N)\n",
    "\n",
    "print('Epoch \\t Loss (in %) \\t Training Accuracy (in %)')\n",
    "\n",
    "for i in range(20):\n",
    "    \n",
    "    loss_tmp = []\n",
    "\n",
    "    for X_i, Y_i in zip(X_train, Y_train):\n",
    "        prediction = quantum_nn(X_i, theta)\n",
    "        loss_tmp.append(loss(prediction, Y_i))\n",
    "\n",
    "        #update theta\n",
    "        theta = theta - eta * gradient(X_i, Y_i, theta)\n",
    "    \n",
    "    loss_list.append(np.mean(loss_tmp))\n",
    "    acc = accuracy(X_train, Y_train, theta)\n",
    "    \n",
    "    print(f'{i} \\t {loss_list[-1]*100:.3f} \\t {acc*100:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "64ed4034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_list)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "577ac932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43333333333333335"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy(X_test, Y_test, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "37e328ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.SVC()\n",
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fcf95ddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0]\n",
      "[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(X_test))\n",
    "print(Y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}