vqc_notebook_seb_trying_to_copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from qiskit.algorithms.optimizers import ADAM, SPSA\n",
    "from qiskit.circuit.library import TwoLocal, ZZFeatureMap\n",
    "from qiskit.primitives import BackendEstimator, BackendSampler\n",
    "from qiskit.quantum_info import Pauli\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit_algorithms.gradients import ParamShiftEstimatorGradient, ParamShiftSamplerGradient\n",
    "\n",
    "from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier\n",
    "from qiskit_machine_learning.neural_networks import EstimatorQNN, SamplerQNN\n",
    "from qiskit_machine_learning.utils.loss_functions import CrossEntropyLoss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 123\n",
    "qasm_sim = AerSimulator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b52baa2970>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = datasets.make_moons(n_samples=100, noise=0.1, random_state=SEED)\n",
    "\n",
    "# Normalizatin\n",
    "x[:,0] = x[:,0] / (np.max(x[:,0]) - np.min(x[:,0]))\n",
    "x[:,1] = x[:,1] / (np.max(x[:,1]) - np.min(x[:,1]))\n",
    "\n",
    "num_classes = 2\n",
    "num_features = 2\n",
    "x_blue = x[y==0]\n",
    "x_red = x[y==1]\n",
    "\n",
    "plt.scatter(x_blue[:,0], x_blue[:,1], c='b', label=\"class 0\")\n",
    "plt.scatter(x_red[:,0], x_red[:,1], c='r', label=\"class 1\")\n",
    "plt.title(\"Moon dataset\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 0 1 0 0 1 1 0 0 1 1 1 0 0 1 0 1 1 0 1 0 0 0 1 1 0 1 0 0 1 0 1 1 1 1\n",
      " 1 1 0 0 1 0 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 1 1 0 0 0 0 1\n",
      " 0 0 1 1 1 0 0 1 1 0 1 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.28536195  0.22539022]\n",
      " [ 0.25848741 -0.31429953]\n",
      " [ 0.38856686 -0.156283  ]\n",
      " [-0.08169527  0.53213333]\n",
      " [ 0.07581807 -0.22166999]\n",
      " [ 0.34980911 -0.03809214]\n",
      " [ 0.34658529  0.21888567]\n",
      " [-0.00978258  0.21643643]\n",
      " [ 0.4138374  -0.29620469]\n",
      " [ 0.36549889  0.1493555 ]\n",
      " [ 0.06559166  0.48033086]\n",
      " [ 0.13861126 -0.11191268]\n",
      " [ 0.33022836 -0.21691082]\n",
      " [ 0.02801198 -0.12964721]\n",
      " [ 0.02175317  0.53171507]\n",
      " [-0.26006333  0.13055256]\n",
      " [ 0.38253682 -0.12571767]\n",
      " [-0.23742029  0.36033644]\n",
      " [ 0.02829437 -0.03398784]\n",
      " [ 0.33400276 -0.36497283]\n",
      " [-0.20707074  0.50105301]\n",
      " [ 0.52460135 -0.05424793]\n",
      " [ 0.13881393  0.5343549 ]\n",
      " [-0.13103888  0.51172631]\n",
      " [-0.06490748  0.3936612 ]\n",
      " [ 0.57302841 -0.02539684]\n",
      " [ 0.50832812 -0.11446952]\n",
      " [ 0.31047264  0.28671951]\n",
      " [ 0.03097866  0.19841284]\n",
      " [-0.25606045  0.2401672 ]\n",
      " [ 0.03816235  0.47827365]\n",
      " [ 0.54776197 -0.00739108]\n",
      " [-0.13746466  0.49075192]\n",
      " [ 0.18639706 -0.14259422]\n",
      " [ 0.53581458 -0.1966195 ]\n",
      " [ 0.50846539 -0.10100542]\n",
      " [ 0.65081605  0.15462151]\n",
      " [ 0.63633919  0.19404409]\n",
      " [ 0.10171685 -0.02279776]\n",
      " [ 0.21581181  0.39261348]\n",
      " [ 0.09631878  0.54580013]\n",
      " [ 0.51599243 -0.15803635]\n",
      " [-0.25495118  0.39397569]\n",
      " [ 0.06753536 -0.05452379]\n",
      " [ 0.35296289  0.02751153]\n",
      " [ 0.30146663 -0.28692462]\n",
      " [ 0.24082043  0.39740354]\n",
      " [ 0.41991528 -0.30330766]\n",
      " [ 0.24113357  0.37177523]\n",
      " [ 0.012627    0.12104752]\n",
      " [ 0.1285124   0.46871126]\n",
      " [-0.05286789  0.55444269]\n",
      " [ 0.01316635  0.32244725]\n",
      " [-0.29107869  0.10646375]\n",
      " [ 0.27591129  0.21525035]\n",
      " [ 0.62385964  0.32176488]\n",
      " [-0.06397122  0.49909352]\n",
      " [-0.31201505  0.03284773]\n",
      " [ 0.24413334 -0.32347844]\n",
      " [-0.29911053  0.22916332]\n",
      " [ 0.18331907  0.45187054]\n",
      " [ 0.61196311  0.09526505]\n",
      " [ 0.13192863  0.32697295]\n",
      " [ 0.20057937  0.40321916]\n",
      " [ 0.03899682  0.53382662]\n",
      " [ 0.27093539  0.29766179]\n",
      " [ 0.10839083  0.04319339]\n",
      " [ 0.24144193 -0.27404689]\n",
      " [ 0.11432906 -0.20645673]\n",
      " [ 0.30210652  0.12111455]\n",
      " [-0.24246308  0.33371734]\n",
      " [-0.1863551   0.37522308]\n",
      " [-0.07039862  0.63502717]\n",
      " [ 0.03040084  0.22621013]\n",
      " [ 0.25635741  0.24012468]\n",
      " [-0.34918395  0.00490842]\n",
      " [ 0.06652098 -0.10196279]\n",
      " [ 0.62861032 -0.00947762]\n",
      " [ 0.59469612  0.20046756]\n",
      " [-0.28753327  0.33759488]\n",
      " [ 0.25850393  0.11239818]\n",
      " [ 0.15937039 -0.20997265]\n",
      " [ 0.02585889  0.07823099]\n",
      " [-0.27870761 -0.0107465 ]\n",
      " [ 0.2034549  -0.24016648]\n",
      " [ 0.42717535 -0.22699388]\n",
      " [-0.20117994  0.35547288]\n",
      " [ 0.39350239 -0.28025641]\n",
      " [ 0.63545748  0.05175324]\n",
      " [-0.05549686  0.57277957]\n",
      " [-0.2471662   0.15188023]\n",
      " [-0.16496574  0.39669478]\n",
      " [ 0.30403554 -0.27838397]\n",
      " [-0.28988621  0.0770874 ]\n",
      " [ 0.52066695  0.03073638]\n",
      " [ 0.03058804  0.02775434]\n",
      " [ 0.46813687 -0.22864962]\n",
      " [ 0.1745284   0.47069817]\n",
      " [ 0.61057981  0.05229766]\n",
      " [ 0.1875677  -0.17314748]]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set size: 80\n",
      "Test set size:  20\n"
     ]
    }
   ],
   "source": [
    "test_ratio = 0.2\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_ratio, random_state=SEED, stratify=y)\n",
    "\n",
    "print(f\"Train set size: {len(x_train)}\")\n",
    "print(f\"Test set size:  {len(x_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data embedding circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sebts\\AppData\\Roaming\\Python\\Python39\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
      "  self._style, def_font_ratio = load_style(self._style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 903.439x140.467 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_zz_feature_map(feature_dim, feature_map_rep, ent):\n",
    "    return ZZFeatureMap(feature_dimension=feature_dim, reps=feature_map_rep, entanglement=ent)\n",
    "\n",
    "fmap_zz = get_zz_feature_map(num_features, 2, 'linear')\n",
    "input_params = fmap_zz.parameters\n",
    "fmap_zz.decompose().draw('mpl', scale=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 669.328x140.467 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmap_zz.assign_parameters({k:v for (k,v) in zip(fmap_zz.parameters, x[0])}).decompose().draw('mpl', scale=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anstaz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 552.273x140.467 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_two_locals(feature_dim, rotations, var_form_rep, ent):\n",
    "    return TwoLocal(num_qubits=feature_dim, rotation_blocks=rotations, entanglement_blocks='cx', entanglement=ent, reps=var_form_rep)\n",
    "\n",
    "ansatz_tl = get_two_locals(num_features, ['ry', 'rz'], 2, 'linear')\n",
    "weights_params = ansatz_tl.parameters\n",
    "ansatz_tl.decompose().draw('mpl', scale=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 552.273x140.467 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = np.random.random(len(ansatz_tl.parameters))\n",
    "ansatz_tl.assign_parameters({k:v for (k,v) in zip(ansatz_tl.parameters, weights)}).decompose().draw('mpl', scale=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 669.328x140.467 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_circuit = fmap_zz.compose(ansatz_tl)\n",
    "var_circuit.draw('mpl', scale=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primitives\n",
    "\n",
    "For info, see https://docs.quantum.ibm.com/run/primitives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for qiskit_ibm_runtime.Options, see https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.options.Options\n",
    "options = {}\n",
    "sampler = BackendSampler(backend=qasm_sim, options=options)\n",
    "estimator = BackendEstimator(backend=qasm_sim, options=options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantum Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SamplerQNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parity(x, num_classes):\n",
    "    return f\"{x:b}\".count(\"1\") % num_classes\n",
    "\n",
    "def one_qubit_binary(x):\n",
    "    return x % 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_circuit_with_meas = var_circuit.copy()\n",
    "var_circuit_with_meas.measure_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "paramShiftSampGrad = ParamShiftSamplerGradient(sampler=sampler)\n",
    "\n",
    "sampler_qnn = SamplerQNN(sampler=sampler,\n",
    "                         circuit=var_circuit_with_meas,\n",
    "                         input_params=input_params,     # if your embedding strategy is not a parametrized circuit\n",
    "                                                        # (e.g. amplitude encoding) you will have to do some extra work!\n",
    "                         weight_params=weights_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=2,\n",
    "                         gradient=paramShiftSampGrad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 100 samples: (100, 2)\n",
      "Output of the forward pass for first sample: [0.43359375 0.56640625]\n"
     ]
    }
   ],
   "source": [
    "# The result of the neural network on a forward pass.\n",
    "output = sampler_qnn.forward(x, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {output.shape}\")\n",
    "print(f\"Output of the forward pass for first sample: {output[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 100 samples: (100, 2, 12)\n",
      "Output of the backward pass for first sample: [[ 0.10058594 -0.06933594 -0.0625     -0.0234375   0.20263672  0.11425781\n",
      "  -0.05078125 -0.05859375  0.20458984 -0.00097656  0.01367188  0.00488281]\n",
      " [-0.10058594  0.06933594  0.0625      0.0234375  -0.20263672 -0.11425781\n",
      "   0.05078125  0.05859375 -0.20458984  0.00097656 -0.01367188 -0.00488281]]\n"
     ]
    }
   ],
   "source": [
    "# The result of the neural network on a backward pass\n",
    "_, weights_grad = sampler_qnn.backward(x, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {weights_grad.shape}\")\n",
    "print(f\"Output of the backward pass for first sample: {weights_grad[0]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EstimatorQNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parity observable: ZZ\n",
      "First qubit observable: IZ\n"
     ]
    }
   ],
   "source": [
    "obs_parity = Pauli('Z' * num_features)\n",
    "obs_one_qubit = Pauli('I' * (num_features - 1) + 'Z')\n",
    "print(f\"Parity observable: {obs_parity}\")\n",
    "print(f\"First qubit observable: {obs_one_qubit}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "paramShiftEstGrad = ParamShiftEstimatorGradient(estimator=estimator)\n",
    "\n",
    "estimator_qnn = EstimatorQNN(estimator=estimator,\n",
    "                             circuit=var_circuit,\n",
    "                             observables=obs_one_qubit,\n",
    "                             input_params=input_params,     # if your embedding strategy is not a parametrized circuit\n",
    "                                                            # (e.g. amplitude encoding) you will have to do some extra work!\n",
    "                             weight_params=weights_params,  # if you train your embedding as well, the trainable\n",
    "                                                            # parameters of the embedding must be added\n",
    "                             gradient=paramShiftEstGrad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 100 samples: (100, 1)\n",
      "Output of the forward pass for first sample: [-0.1015625]\n"
     ]
    }
   ],
   "source": [
    "# The result of the neural network on a forward pass.\n",
    "output = estimator_qnn.forward(x, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {output.shape}\")\n",
    "print(f\"Output of the forward pass for first sample: {output[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 100 samples: (100, 1, 12)\n",
      "Output of the backward pass for first sample: [[ 0.20507812 -0.15429688 -0.16113281 -0.05957031  0.37695312  0.18164062\n",
      "  -0.11230469 -0.12304688  0.44335938 -0.02636719  0.00390625  0.00585938]]\n"
     ]
    }
   ],
   "source": [
    "# The result of the neural network on a backward pass\n",
    "_, weights_grad = estimator_qnn.backward(x, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {weights_grad.shape}\")\n",
    "print(f\"Output of the backward pass for first sample: {weights_grad[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a Neural Network Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 0 converted to [1 0]\n",
      "Label 1 converted to [0 1]\n"
     ]
    }
   ],
   "source": [
    "def get_one_hot_encoding(y):\n",
    "    unique_labels = np.unique(y, axis=0)\n",
    "    y_one_hot = [(np.eye(len(unique_labels), dtype=int)[np.where(unique_labels == y_i)]).reshape(len(unique_labels)) for y_i in y]\n",
    "\n",
    "    return np.array(y_one_hot)\n",
    "\n",
    "y_train_1h = get_one_hot_encoding(y_train)\n",
    "y_test_1h = get_one_hot_encoding(y_test)\n",
    "\n",
    "print(f\"Label {y_train[2]} converted to {y_train_1h[2]}\")\n",
    "print(f\"Label {y_train[0]} converted to {y_train_1h[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spsa_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 spsa_loss_recorder\n",
    "\n",
    "    if (nfev % 3) == 0:\n",
    "        spsa_loss_recorder.append(fval)\n",
    "        # print(f\"SPSA Epoch {len(spsa_loss_recorder)}: {fval:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_itr = 10\n",
    "spsa_opt = SPSA(maxiter=max_itr, callback=spsa_callback)\n",
    "spsa_loss_recorder = []\n",
    "initial_point = np.random.random((len(ansatz_tl.parameters),))\n",
    "vqc = NeuralNetworkClassifier(neural_network=sampler_qnn,\n",
    "                              loss=CrossEntropyLoss(),\n",
    "                              one_hot=True,\n",
    "                              optimizer=spsa_opt,\n",
    "                              initial_point=initial_point)\n",
    "\n",
    "vqc = vqc.fit(x_train, y_train_1h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training loss')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(spsa_loss_recorder)\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on the train set 0.75\n",
      "Score on the test set 0.55\n"
     ]
    }
   ],
   "source": [
    "score_train = vqc.score(x_train, y_train_1h)\n",
    "score_test = vqc.score(x_test, y_test_1h)\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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = vqc.predict(x_train)\n",
    "y_test_pred = vqc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred_acc = np.all(y_train_pred == y_train_1h, axis=1)\n",
    "test_pred_acc = np.all(y_test_pred == y_test_1h, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_all = np.concatenate((x_train, x_test))\n",
    "y_all= np.concatenate((y_train_1h, y_test_1h))\n",
    "y_pred_acc_all = np.concatenate((train_pred_acc, test_pred_acc))\n",
    "\n",
    "x_b = x_all[np.all(y_all == [1, 0], axis=1)]\n",
    "x_b_good = x_b[(y_pred_acc_all[np.all(y_all == [1, 0], axis=1)])]\n",
    "x_b_bad = x_b[np.logical_not(y_pred_acc_all[np.all(y_all == [1, 0], axis=1)])]\n",
    "\n",
    "x_r = x_all[np.all(y_all == [0, 1], axis=1)]\n",
    "x_r_good = x_r[(y_pred_acc_all[np.all(y_all == [0, 1], axis=1)])]\n",
    "x_r_bad = x_r[np.logical_not(y_pred_acc_all[np.all(y_all == [0, 1], axis=1)])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b52fa7cb20>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ER0eLRYsWiTt37ug9T0ZGhhg2bJho2rSp8PT0FI888ogYNmyY+PLLL/XKJSUliRYtWgg3NzezRkJlZWWJoUOHCqVSKby9vUWbNm30RtgYG21krdf2yy+/iBdeeEG0adNG+Pr6CqVSKWJjY0VKSopeHR9ktJE5r/H69esiMTFRBAcHi0aNGonevXuLAwcOGD2vPv/8c9G2bVvh6emp93prjjYSQhoR9t5774moqCjh6ekpgoKCxH/913+JwsJCvXJ9+/YVjz76qMFnM2HCBBEWFmawnchSCiGEcFDcRERERGQx5rwQERGRrDB4ISIiIllh8EJERESywuCFiIiIZIXBCxEREckKgxciIiKSFZebpK6qqgqXLl2Cv7+/wRo0RERE5JyEELh16xZatGgBN7fa21ZcLni5dOkSQkNDHV0NIiIiqofCwsI6Fx91ueBFO214YWEhAgICHFwbIiIiMsfNmzcRGhpqcvmP6lwueNF2FQUEBDB4ISIikhlzUj6YsEtERESywuCFiIiIZIXBCxEREcmKy+W8EBGR4wghUFFRgcrKSkdXhZyQu7s7PDw8HngqEwYvRERkFeXl5SgqKsIff/zh6KqQE2vUqBFCQkLg5eVV72MweCEiogdWVVWFCxcuwN3dHS1atICXlxcnCiU9QgiUl5fj8uXLuHDhAiIjI+ucjM4UBi9ERPTAysvLUVVVhdDQUDRq1MjR1SEn5evrC09PT1y8eBHl5eXw8fGp13GYsEtERFZT31/S1HBY4xzhWUZERESywuCFiIiIZIXBC1Ed1GogLU26JyKyNoVCgZ07d9brsfn5+VAoFMjJybFqnZwdgxeiWiQnA2FhwJNPSvfJyY6uERFZW3FxMV5++WVERETAx8cHzZo1Q+/evbF27VoO+65DWVkZ/vKXvyAoKAh+fn4YOXIk1Hb4pcfghcgEtRqYNg2oqpL+rqoCpk9nCwyRKzl//jw6d+6MPXv24J133sGJEyfw/fffY86cOfjnP/+J77//3tFVdGqzZ89GamoqvvjiC/zwww+4ffs2hg8fbvNJChm8EJlw9uz9wEWrshL49VfH1IeIrG/mzJnw8PDA0aNHMXr0aMTExKBDhw549tln8d1332HEiBG6sgUFBXj66afRuHFjBAQEYPTo0fj999/1jrdmzRq0adMGXl5eiI6OxubNm/X2nz17Fk888QR8fHzQrl077N27t846VlVV4b333kNERAS8vb3RsmVLLF261GjZyspKJCYmonXr1vD19UV0dDRWrlypVyY9PR2xsbHw8/NDkyZN0KtXL1y8eBEAcPLkSfTv3x/+/v4ICAhA165dcfToUaPPpdFokJycjL///e8YOHAgOnfujC1btuDUqVM2D/o4zwuRCZGRgJubfgDj7g5ERDiuTkQNgVot/XiIjARUKts9z9WrV3UtLn5+fkbLaCfaE0Jg1KhR8PPzQ0ZGBioqKjBz5kyMGTMG6enpAIDU1FS8/PLLWLFiBQYOHIhvv/0WkyZNgkqlQv/+/VFVVYVnnnkGQUFByM7Oxs2bNzF79uw665mUlIQNGzZg+fLl6N27N4qKivDLL78YLVtVVQWVSoXt27cjKCgImZmZmDZtGkJCQjB69GhUVFRg1KhRmDp1Kj7//HOUl5fj8OHDutf54osvonPnzlizZg3c3d2Rk5MDT09Po8917Ngx3Lt3D4MHD9Zta9GiBdq3b4/MzEwMGTKkztdWb8LFaDQaAUBoNBpHV4VcwMaNQri7CwFI9xs3OrpGRM6ptLRU5ObmitLS0gc6zsaNQri5Sf/n3Nxs+38uOztbABA7duzQ2x4YGCj8/PyEn5+feP3114UQQuzZs0e4u7uLgoICXbmff/5ZABCHDx8WQgjRs2dPMXXqVL1jPf/88+Kpp54SQgixe/du4e7uLgoLC3X7//3vfwsAIjU11Wgdb968Kby9vcWGDRuM7r9w4YIAIE6cOGHydc6cOVM8++yzQgghrl69KgCI9PR0o2X9/f1FSkqKyWNVt3XrVuHl5WWwfdCgQWLatGkmH2fqXLHk+s1uI6JaJCYC+fnSaKP8fOlvIrINR+WZ1VzG4PDhw8jJycGjjz6KsrIyAEBeXh5CQ0MRGhqqK9euXTs0adIEeXl5ujK9evXSO1avXr309rds2RKqas1JcXFxtdYtLy8PZWVlGDBggNmvZ+3atejWrRsefvhhNG7cGBs2bEBBQQEAoGnTppg4cSKGDBmCESNGYOXKlSgqKtI9du7cuZgyZQoGDhyId999F+fOnTP7ebWEEDZfGoLBC1EdVCqgXz/bNl8Tkf3zzCIiIqBQKAy6YMLDwxEREQFfX1/dNlMX5Jrba5apvl8IYfD4ui7y1etgju3bt2POnDmYPHky9uzZg5ycHEyaNAnl5eW6Mps2bUJWVhZ69uyJbdu2ISoqCtnZ2QCAxYsX4+eff8awYcOwf/9+tGvXDqmpqUafq3nz5igvL8f169f1tpeUlKBZs2YW1dtSDF6IiMgpaPPMqrNlnllgYCAGDRqEjz/+GHfu3Km1bLt27VBQUIDCwkLdttzcXGg0GsTExAAAYmJi8MMPP+g9LjMzU7dfe4xLly7p9mdlZdX6vJGRkfD19cW+ffvMek0HDhxAz549MXPmTHTu3BkRERFGW086d+6MpKQkZGZmon379vjss890+6KiojBnzhzs2bMHzzzzDDZt2mT0ubp27QpPT0+9pOOioiL89NNP6Nmzp1n1rS8GL0RE5BRUKmD9eilgAaT7dets2+q5evVqVFRUoFu3bti2bRvy8vJw+vRpbNmyBb/88gvc/1OZgQMHomPHjnjxxRdx/PhxHD58GOPHj0ffvn3RrVs3AMBrr72GlJQUrF27FmfPnsWHH36IHTt24NVXX9UdIzo6GuPHj8fJkydx4MABLFiwoNb6+fj4YN68eXj99dfx6aef4ty5c8jOzkayiUmnIiIicPToUezevRtnzpzBwoULceTIEd3+CxcuICkpCVlZWbh48SL27NmDM2fOICYmBqWlpZg1axbS09Nx8eJFHDx4EEeOHNEFXzUplUokJibilVdewb59+3DixAn813/9Fzp06ICBAwda/FlYxKysHBlhwi4Rkf1ZK2FXCCEKC4VIS5Pu7eHSpUti1qxZonXr1sLT01M0btxYxMbGig8++EDcuXNHV+7ixYti5MiRws/PT/j7+4vnn39eFBcX6x1r9erVIjw8XHh6eoqoqCjx6aef6u0/ffq06N27t/Dy8hJRUVFi165dtSbsCiFEZWWlePvtt0VYWJjw9PQULVu2FO+8844QwjBh9+7du2LixIlCqVSKJk2aiD//+c9i/vz54rHHHhNCCFFcXCxGjRolQkJChJeXlwgLCxN//etfRWVlpSgrKxNjx44VoaGhwsvLS7Ro0ULMmjWr1s+0tLRUzJo1SzRt2lT4+vqK4cOH6yU1m3rMgybsKoQw0glnZatXr8YHH3yAoqIiPProo1ixYgX69OljsnxZWRneeustbNmyBcXFxVCpVFiwYAEmT55c53PdvHkTSqUSGo0GAQEB1nwZRERkwt27d3HhwgW0bt0aPj4+jq4OOTFT54ol12+bz/Oybds2zJ49G6tXr0avXr2wbt06DB06FLm5uWjZsqXRx2gn/klOTkZERARKSkpQUVFh66oSERGRDNg8ePnwww+RmJiIKVOmAABWrFiB3bt3Y82aNVi2bJlB+V27diEjIwPnz59H06ZNAQCtWrWydTWJiIhIJmyasFteXo5jx47pzb4HAIMHD0ZmZqbRx3zzzTfo1q0b3n//fTzyyCOIiorCq6++itLSUqPly8rKcPPmTb0bERERuS6btrxcuXIFlZWVBuO9mzVrhuLiYqOPOX/+PH744Qf4+PggNTUVV65cwcyZM3Ht2jV88sknBuWXLVuGJUuW2KT+RERE5HzsMlS6tkl7aqqqqoJCocDWrVsRGxuLp556Ch9++CFSUlKMtr4kJSVBo9HobtXH4BMREZHrsWnLS1BQENzd3Q1aWWqbfS8kJASPPPIIlEqlbltMTAyEEFCr1YiMjNQr7+3tDW9vb+tXnoiIiJySTVtevLy80LVrV4Mlv/fu3Wty9r1evXrh0qVLuH37tm7bmTNn4ObmprceBBERETVMNu82mjt3LjZu3IhPPvkEeXl5mDNnDgoKCjBjxgwAUrfP+PHjdeXHjRuHwMBATJo0Cbm5ufi///s/vPbaa5g8ebLFazwQERGR67H5UOkxY8bg6tWreOutt1BUVIT27dvjX//6F8LCwgBI6yBoV7sEgMaNG2Pv3r34y1/+gm7duiEwMBCjR4/G22+/beuqEhERkQzYZYZde+IMu2QvarW0Cm5kJFecJuIMu4YWL16MnTt3Iicnp16PnzhxIm7cuIGdO3datV6OZo0ZdrkwI1E9JCcDYWHAk09K9ybWSCMiM2k00g8CY9Rqab8tTJw4EQqFQncLDAxEfHw8fvzxR9s8oYxkZGSga9eu8PHxQXh4ONauXevoKukweCGykFoNTJsGVFVJf1dVAdOnm/7iJaLaaTRAfDzQty9Qc7aLwkJpe3y87QKY+Ph4FBUVoaioCPv27YOHhweGDx9umyeTiQsXLuCpp55Cnz59cOLECbzxxht46aWX8PXXXzu6agAYvBBZ7OzZ+4GLVmUl8OuvjqkPkdzdugWUlADnzwP9+t0PYAoLpb/Pn5f237plm+f39vZG8+bN0bx5c3Tq1Anz5s1DYWEhLl++rCszb948REVFoVGjRggPD8fChQtx7949veO8++67aNasGfz9/ZGYmIi7d+/W+dw///wzhg0bhoCAAPj7+6NPnz44d+6c0bK7du1C79690aRJEwQGBmL48OF6ZcvLyzFr1iyEhITAx8cHrVq10luGZ/HixWjZsiW8vb3RokULvPTSSybrtXbtWrRs2RIrVqxATEwMpkyZgsmTJ+Nvf/tbna/JHhi8EFkoMhJwq/E/x90diIhwTH2I5E6lAtLTgfDw+wFMZub9wCU8XNpvj9yy27dvY+vWrYiIiEBgYKBuu7+/P1JSUpCbm4uVK1diw4YNWL58uW7/9u3bsWjRIixduhRHjx5FSEgIVq9eXetz/fbbb3jiiSfg4+OD/fv349ixY5g8ebLJhYjv3LmDuXPn4siRI9i3bx/c3Nzwpz/9CVX/+TX1j3/8A9988w22b9+O06dPY8uWLbq1Ab/66issX74c69atw9mzZ7Fz50506NDBZN2ysrIMlvYZMmQIjh49ahC0OYRwMRqNRgAQGo3G0VUhF7ZxoxDu7kIA0v3GjY6uEZFjlZaWitzcXFFaWlrvYxQUCBEeLv2/0t7Cw6XttjJhwgTh7u4u/Pz8hJ+fnwAgQkJCxLFjx2p93Pvvvy+6du2q+zsuLk7MmDFDr0z37t3FY489ZvIYSUlJonXr1qK8vNxk3Z5++mmTjy8pKREAxKlTp4QQQvzlL38RTz75pKiqqjIo+/e//11ERUWZfK6aIiMjxdKlS/W2HTx4UAAQly5dMusYppg6Vyy5frPlhageEhOB/HwgLU26T0x0dI2I5C80FNi8WX/b5s3Sdlvq378/cnJykJOTg0OHDmHw4MEYOnQoLl68qCvz1VdfoXfv3mjevDkaN26MhQsX6k3zkZeXh7i4OL3j1vy7ppycHPTp0weenp5m1fPcuXMYN24cwsPDERAQgNatWwOArh4TJ05ETk4OoqOj8dJLL2HPnj26xz7//PMoLS1FeHg4pk6ditTUVJMtPFrGlvYxtt0RGLwQ1ZNKJTVrA1IQw4RdogdTWAgkJOhvS0gwTOK1Nj8/P0RERCAiIgKxsbFITk7GnTt3sGHDBgBAdnY2xo4di6FDh+Lbb7/FiRMnsGDBApSXlz/Q81o68eqIESNw9epVbNiwAYcOHcKhQ4cAQFePLl264MKFC/if//kflJaWYvTo0XjuuecAAKGhoTh9+jRWrVoFX19fzJw5E0888YTJLqDmzZsbXdrHw8NDrzvNURi8ED0ADpkmso7qybnh4cDBg/o5MPZcc1ehUMDNzU23GPDBgwcRFhaGBQsWoFu3boiMjNRrlQGkNfiys7P1ttX8u6aOHTviwIEDZuWQXL16FXl5eXjzzTcxYMAAxMTE4Pr16wblAgICMGbMGGzYsAHbtm3D119/jWvXrgGQgqWRI0fiH//4B9LT05GVlYVTp04Zfb64uDiDpX327NmDbt26md1SZEs2n2GXyFWZGjI9ZEjDnbSOE/dRfajVhsm5oaHSvXZ7v35ARoZtzquysjJdK8P169fx8ccf4/bt2xgxYgQAICIiAgUFBfjiiy/w+OOP47vvvkNqaqreMV5++WVMmDAB3bp1Q+/evbF161b8/PPPCA8PN/m8s2bNwkcffYSxY8ciKSkJSqUS2dnZiI2NRXR0tF7Zhx56CIGBgVi/fj1CQkJQUFCA+fPn65VZvnw5QkJC0KlTJ7i5ueHLL79E8+bN0aRJE6SkpKCyshLdu3dHo0aNsHnzZvj6+upmu69pxowZ+PjjjzF37lxMnToVWVlZSE5Oxueff27x+2sTD5R144SYsEv2sn+/fmKh9paW5uiaOcbGjUK4uUnvgZsbk5gbmgdJ2L1xQ4gePYwn52qTeHv0kMpZ24QJEwQA3c3f3188/vjj4quvvtIr99prr4nAwEDRuHFjMWbMGLF8+XKhVCr1yixdulQEBQWJxo0biwkTJojXX3+91oRdIYQ4efKkGDx4sGjUqJHw9/cXffr0EefOndPVrXrC7t69e0VMTIzw9vYWHTt2FOnp6QKASE1NFUIIsX79etGpUyfh5+cnAgICxIABA8Tx48eFEEKkpqaK7t27i4CAAOHn5yd69Oghvv/++1rrlp6eLjp37iy8vLxEq1atxJo1a+p+Q81gjYRdLg9AVE9qtdRVVH3OF3d3KYG3obU68L2gB10eQKOR5nExdr6o1YC/P6BUWqGi5HBcHoDIgVQqYP166SINSPfr1jXMizUn7qMHpVSa/r+jUjFwIX3MeSF6AImJUo7Lr79Kk9Q5a+Bi61wU7cR9NVteHmTiPubPEJEpbHkhqkattnzYs3bItCMvsLXV2x4joqzdCsVRXERUGwYvRP8h1wtmbfW25yKS1pq4jwtfElFdGLwQQb4XzLrqbe9cFGu0QjF/hojqwuCFCPK9YNZVbzkuIinHOhORfTF4IYJ8L5h11VuOI6LkWGcisi8GL0SQ7wXTnHrLcRFJOdaZiOyHQ6WJ/kMuw55rMqfeKpV8Xo+WHOtMRPbBlheSnfoMZzaXMwx7rg+51puIpIUgd+7cWa/H5ufnQ6FQICcnx6p1cnYMXkhW5DqcmYicV3FxMV5++WVERETAx8cHzZo1Q+/evbF27Vr88ccfjq6eU1u/fj369euHgIAAKBQK3Lhxwy7Py24jkg2u4kxE1nb+/Hn06tULTZo0wTvvvIMOHTqgoqICZ86cwSeffIIWLVpg5MiRjq6m0/rjjz8QHx+P+Ph4JCUl2e152fJCZrNld4055DqcmYgsZMcvm5kzZ8LDwwNHjx7F6NGjERMTgw4dOuDZZ5/Fd999hxEjRujKFhQU4Omnn0bjxo0REBCA0aNH4/fff9c73po1a9CmTRt4eXkhOjoamzdv1tt/9uxZPPHEE/Dx8UG7du2wd+/eOutYVVWF9957DxEREfD29kbLli2xdOlSo2UrKyuRmJiI1q1bw9fXF9HR0Vi5cqVemfT0dMTGxsLPzw9NmjRBr169cPHiRQDAyZMn0b9/f/j7+yMgIABdu3bF0aNHTdZt9uzZmD9/Pnr06FHn67AmtryQWZKT77d6uLlJI1zsPQLEFuvnWIrr7RDZmB2/bK5evYo9e/bgnXfegZ+fn9EyCoUCACCEwKhRo+Dn54eMjAxUVFRg5syZGDNmDNLT0wEAqampePnll7FixQoMHDgQ3377LSZNmgSVSoX+/fujqqoKzzzzDIKCgpCdnY2bN29i9uzZddYzKSkJGzZswPLly9G7d28UFRXhl19+MVq2qqoKKpUK27dvR1BQEDIzMzFt2jSEhIRg9OjRqKiowKhRozB16lR8/vnnKC8vx+HDh3Wv88UXX0Tnzp2xZs0auLu7IycnB56enpa/ubYmXIxGoxEAhEajcXRVXEZhoRBubkIA92/u7tJ2e9u4UXpubR02brTvc2vfBzc3+z43kbMrLS0Vubm5orS0tP4HsfOXTXZ2tgAgduzYobc9MDBQ+Pn5CT8/P/H6668LIYTYs2ePcHd3FwUFBbpyP//8swAgDh8+LIQQomfPnmLq1Kl6x3r++efFU089JYQQYvfu3cLd3V0UVns9//73vwUAkZqaarSON2/eFN7e3mLDhg1G91+4cEEAECdOnDD5OmfOnCmeffZZIYQQV69eFQBEenq60bL+/v4iJSXF5LFMSUtLEwDE9evX6yxr6lyx5PrNbiOqkzN11zhq/g+5Lh9AJCsO+rLRtjpoHT58GDk5OXj00UdRVlYGAMjLy0NoaChCQ0N15dq1a4cmTZogLy9PV6ZXr156x+rVq5fe/pYtW0JVrdk2Li6u1rrl5eWhrKwMAwYMMPv1rF27Ft26dcPDDz+Mxo0bY8OGDSgoKAAANG3aFBMnTsSQIUMwYsQIrFy5EkVFRbrHzp07F1OmTMHAgQPx7rvv4ty5c2Y/rz0xeKE6OcPss9W7wB0xLNiZAjgil2XnL5uIiAgoFAqDLpjw8HBERETA19dXt00IYRDkGNtes0z1/UIIg8cbO2Z11etgju3bt2POnDmYPHky9uzZg5ycHEyaNAnl5eW6Mps2bUJWVhZ69uyJbdu2ISoqCtnZ2QCAxYsX4+eff8awYcOwf/9+tGvXDqmpqRbVwR4YvFCdHD37rDMMj3aGAI7I5dn5yyYwMBCDBg3Cxx9/jDt37tRatl27digoKEBhYaFuW25uLjQaDWJiYgAAMTEx+OGHH/Qel5mZqduvPcalS5d0+7Oysmp93sjISPj6+mLfvn1mvaYDBw6gZ8+emDlzJjp37oyIiAijrSedO3dGUlISMjMz0b59e3z22We6fVFRUZgzZw727NmDZ555Bps2bTLrue2JwQuZpaF31zg6gCNqMOz8ZbN69WpUVFSgW7du2LZtG/Ly8nD69Gls2bIFv/zyC9z/859+4MCB6NixI1588UUcP34chw8fxvjx49G3b19069YNAPDaa68hJSUFa9euxdmzZ/Hhhx9ix44dePXVV3XHiI6Oxvjx43Hy5EkcOHAACxYsqLV+Pj4+mDdvHl5//XV8+umnOHfuHLKzs5Fs4ldcREQEjh49it27d+PMmTNYuHAhjhw5ott/4cIFJCUlISsrCxcvXsSePXtw5swZxMTEoLS0FLNmzUJ6ejouXryIgwcP4siRI7rgy5ji4mLk5OTg1/80Q586dQo5OTm4du2a+R9CfViclePkmLDrWvbv18/d097S0hxTn8JC6bkdkaxsT4WF0nvv6q+TrMcqCbsOcunSJTFr1izRunVr4enpKRo3bixiY2PFBx98IO7cuaMrd/HiRTFy5Ejh5+cn/P39xfPPPy+Ki4v1jrV69WoRHh4uPD09RVRUlPj000/19p8+fVr07t1beHl5iaioKLFr165aE3aFEKKyslK8/fbbIiwsTHh6eoqWLVuKd955RwhhmLB79+5dMXHiRKFUKkWTJk3En//8ZzF//nzx2GOPCSGEKC4uFqNGjRIhISHCy8tLhIWFib/+9a+isrJSlJWVibFjx4rQ0FDh5eUlWrRoIWbNmlXrZ7po0SIBwOC2adMmk4+xRsKuQggjnXAydvPmTSiVSmg0GgQEBDi6OvSA1Gqpq6jm8Oj8fLZ6WMKSId7OMCye5Ofu3bu4cOECWrduDR8fH0dXh5yYqXPFkus3u43IqbG75sFZkjPkLN10RES1YfBCTs9R+TauwNJghKOqiEgOOMMuyYJKxdaW+qgtGDH2fjpqFmNbzFzM2ZCJXBdbXohcmKVDvB3RTWeLofDOMLyeiGyHwQuRC6tPMGLPbjpb5Ngwb4fI9bHbiMjFJSYCQ4ZIXUUREea1otirm87Sbi1HHZOInAuDF6IGwFlzhmyRY+MMq48TkW2x24iIHMYWOTYcXk/k+tjyQkQOVZ9urfock6OPiFwHW16IyOFssVJ49WNy9BE5wuLFi9GpU6d6P37ixIkYNWqU1erjShi8EJFL4+gjmdBoTH8oarW03wYmTpwIhUKhuwUGBiI+Ph4//vijTZ5PLoqKijBu3DhER0fDzc0Ns2fPdnSV9DB4ISKXxlmDZUCjAeLjgb59gcJC/X2FhdL2+HibBTDx8fEoKipCUVER9u3bBw8PDwwfPtwmzyUXZWVlePjhh7FgwQI89thjjq6OAQYvROTSLJ2ojxzg1i2gpAQ4f17q69MGMIWF0t/nz0v7b92yydN7e3ujefPmaN68OTp16oR58+ahsLAQly9f1pWZN28eoqKi0KhRI4SHh2PhwoW4d++e3nHeffddNGvWDP7+/khMTMTdu3frfO6ff/4Zw4YNQ0BAAPz9/dGnTx+cO3fOaNldu3ahd+/eaNKkCQIDAzF8+HC9suXl5Zg1axZCQkLg4+ODVq1aYdmyZbr9ixcvRsuWLeHt7Y0WLVrgpZdeMlmvVq1aYeXKlRg/fjyUSmWdr8PeGLwQkUvj6CMZUKmA9HQgPPx+AJOZeT9wCQ+X9tvhQ7t9+za2bt2KiIgIBAYG6rb7+/sjJSUFubm5WLlyJTZs2IDly5fr9m/fvh2LFi3C0qVLcfToUYSEhGD16tW1Ptdvv/2GJ554Aj4+Pti/fz+OHTuGyZMno6Kiwmj5O3fuYO7cuThy5Aj27dsHNzc3/OlPf0LVf5oW//GPf+Cbb77B9u3bcfr0aWzZsgWtWrUCAHz11VdYvnw51q1bh7Nnz2Lnzp3o0KHDA75bjsPRRkTk8mwxoomsLDRUClC0AUuvXtJ2beASGmqzp/7222/RuHFjAFKAEBISgm+//RZu1Zrs3nzzTd2/W7VqhVdeeQXbtm3D66+/DgBYsWIFJk+ejClTpgAA3n77bXz//fe1tr6sWrUKSqUSX3zxBTw9PQEAUVFRJss/++yzen8nJycjODgYubm5aN++PQoKChAZGYnevXtDoVAgLCxMV7agoADNmzfHwIED4enpiZYtWyI2Ntbct8jpsOWFiBoEW4xoIisLDQU2b9bftnmzTQMXAOjfvz9ycnKQk5ODQ4cOYfDgwRg6dCguXryoK/PVV1+hd+/eaN68ORo3boyFCxeioKBAtz8vLw9xcXF6x635d005OTno06ePLnCpy7lz5zBu3DiEh4cjICAArVu3BgBdPSZOnIicnBxER0fjpZdewp49e3SPff7551FaWorw8HBMnToVqampJlt45IDBCxEROYfCQiAhQX9bQoJhEq+V+fn5ISIiAhEREYiNjUVycjLu3LmDDRs2AACys7MxduxYDB06FN9++y1OnDiBBQsWoLy8/IGe19fX16LyI0aMwNWrV7FhwwYcOnQIhw4dAgBdPbp06YILFy7gf/7nf1BaWorRo0fjueeeAwCEhobi9OnTWLVqFXx9fTFz5kw88cQTBnk7csHghYiIHK96cm54OHDwoH4OjI0DmOoUCgXc3NxQWloKADh48CDCwsKwYMECdOvWDZGRkXqtMgAQExOD7OxsvW01/66pY8eOOHDggFkBxNWrV5GXl4c333wTAwYMQExMDK5fv25QLiAgAGPGjMGGDRuwbds2fP3117h27RoAKVgaOXIk/vGPfyA9PR1ZWVk4depUnc/tjJjzQkREjqVWGybn1syB6dcPyMiwSb9fWVkZiouLAQDXr1/Hxx9/jNu3b2PEiBEAgIiICBQUFOCLL77A448/ju+++w6pqal6x3j55ZcxYcIEdOvWDb1798bWrVvx888/Izw83OTzzpo1Cx999BHGjh2LpKQkKJVKZGdnIzY2FtHR0XplH3roIQQGBmL9+vUICQlBQUEB5s+fr1dm+fLlCAkJQadOneDm5oYvv/wSzZs3R5MmTZCSkoLKykp0794djRo1wubNm+Hr66uXF1NTTk4OACmJ+fLly8jJyYGXlxfatWtn9ntrM8LFaDQaAUBoNBpHV4WIqMEoLS0Vubm5orS01PIH37ghRI8eQoSHC1FQoL+voEDa3qOHVM7KJkyYIADobv7+/uLxxx8XX331lV651157TQQGBorGjRuLMWPGiOXLlwulUqlXZunSpSIoKEg0btxYTJgwQbz++uviscceq/X5T548KQYPHiwaNWok/P39RZ8+fcS5c+d0dXv66ad1Zffu3StiYmKEt7e36Nixo0hPTxcARGpqqhBCiPXr14tOnToJPz8/ERAQIAYMGCCOHz8uhBAiNTVVdO/eXQQEBAg/Pz/Ro0cP8f3339dat+rvi/YWFhZW53taF1PniiXXb8V/Kugybt68CaVSCY1Gg4CAAEdXh4ioQbh79y4uXLiA1q1bw8fHx/IDaDTSPC7GWlbUasDfH3DC+UbIcqbOFUuu3+w2IiIix1MqTQcnHCJGNTBhl4iIiGSFwQsRERHJCoMXIiJIaRVpaVxtmkgO7BK8rF69WpeY07VrVxw4cMCsxx08eBAeHh7o1KmTbStIRA1acjIQFgY8+aR0n5zs6BrJl4uNASEbsMY5YvPgZdu2bZg9ezYWLFiAEydOoE+fPhg6dKjetMrGaDQajB8/HgMGDLB1FYmoAVOrgWnTgP+sbYeqKmD69Pq3wDTUFhztFPd//PGHg2tCzk57jpi7LIIxNh8q3b17d3Tp0gVr1qzRbYuJicGoUaP0luquaezYsYiMjIS7uzt27typmyynprKyMpSVlen+vnnzJkJDQxv8UGm1Gjh7FoiMdM5EfWevHzUcaWlSi4ux7f36WXas5OT7gZCbm7SadWKiVaopC0VFRbhx4waCg4PRqFEjKBQKR1eJnIgQAn/88QdKSkrQpEkThISE6O13mqHS5eXlOHbsmMEsgIMHD0ZmZqbJx23atAnnzp3Dli1b8Pbbb9f6HMuWLcOSJUusUl9X4exfoM5eP1MYcLmmyEjpPNS2vACAu7u0+rSWOZ+9qRacIUMazvnSvHlzAEBJSYmDa0LOrEmTJrpzpb5sGrxcuXIFlZWVaNasmd72Zs2a6aZiruns2bOYP38+Dhw4AA+PuquXlJSEuXPn6v7Wtrw0VM7+Bers9TNFrgEX1U2lkj7P6dOBykopcFm37v75aO5nf/asfgAESMf79VfnPretSaFQICQkBMHBwbJd8I9sy9PTE+7u7g98HLtMUlez6VAIYbQ5sbKyEuPGjcOSJUsQFRVl1rG9vb3h7e1tlXq6Amf/AnX2+hkj14CLzJeYKH2ev/4qtbhoP1dLPntzWnAaCnd3d6tcoIhMsWnCblBQENzd3Q1aWUpKSgxaYwDg1q1bOHr0KGbNmgUPDw94eHjgrbfewsmTJ+Hh4YH9+/fbsrouQfsFWp0zfYE6e/2MqS3gItehUkk5LtWDEks+e20LjvaaXbMFh4isx6bBi5eXF7p27Yq9e/fqbd+7dy969uxpUD4gIACnTp1CTk6O7jZjxgxER0cjJycH3bt3t2V1XYKzf4E6e/2MkWPARdZh6WefmAjk50vJvvn57FokshWbdxvNnTsXCQkJ6NatG+Li4rB+/XoUFBRgxowZAKScld9++w2ffvop3Nzc0L59e73HBwcHw8fHx2A7mWaqCdxZOHv9aqorJ4JcV30+e5WK5waRrdk8eBkzZgyuXr2Kt956C0VFRWjfvj3+9a9/ISwsDIA0tK6uOV/Ico76AjV3RI7cvuDlFnCR9fCzJ3I+Np/nxd4sGSdO1sUROUQcUk9UX5Zcv7m2EVmFtWcpJZIjLjNAZB8MXsgqOCKHGjoG8ET2w+CFrIIjcqihYwBPZD8MXhogWywcJ8ch0ETWxACeyH4YvDQw1uqTNxYAcY4LasgYwBPZD0cbNSBqtRSw1Jy+PD/fsi9YjioiMk2t5rBqovrgaCMyyhp98kxKJKqdsWUGiMi6GLw0INbok2dSIhERORqDFyej0ZhuxVCrpf31ZY0+eSYlEhGRozF4cSIaDRAfD/TtCxQW6u8rLJS2x8c/WADzoEm1TEokIiJHY/DiRG7dAkpKgPPnpT5zbQBTWCj9ff68tP/WLfOPaawlR9snr91vKY4qIiIiR2Lw4kRUKiA9HQgPvx/AZGbeD1zCw6X95rZy2LIlh0mJRETkKAxenExoqH4A06uXfuASGmr+sWzRkkNERORoDF6cUGgosHmz/rbNmy0LXADrt+QQERE5AwYvTqiwEEhI0N+WkGDY9WMOa7bkkIPZciiak7DF0hVE5HoYvDiZ6l064eHAwYP6LSf1DWCs0ZJDDmSPoWgOZq2lK4jI9TF4cSJqtWGXTs+ehl0/lv4qtWZLDjmIiycwceZmIgeSYZMngxcn4u8PBAcbdulU7/oJDpbKmcsWLTnkAC6ewMSZm4kcRKZNnlyY0cloNNKPZ2PXILVaClyUSvOOpVZLvQk1c1xqBjQZGbK95jU81T88LRdIYLLWoqFEZAEn+4/HhRllTKk0fc6oVOYHLoBtWnLIwVw0gYkzNxM5gIybPNny4uKs2ZJDTsBFW1601GrpezMigoELkc2x5YWclTVbcsjBGkACE2duJrIjGTd5suWFSA6YwEREtuIkTZ6WXL897FQnInoQ2gQmwHgCU79+TGAiovpRqWT3o4fBC5EcKJXArl3GE5hCQ6UWFyYwyZpaLeVPRkbK7jpCZHfMeSGSCyYwuSyZTrVB5DAMXoiIHIizCxNZjsELEZEDyXiqDSKHYfBCRORAkZGAW41vYnd3aeCHpWS4RA1RvTB4ISJyIGtNtcG8GWpIOM8LEZETeJCpNpxsolSieuE8L0REMvMgU23UljfD4IVcEbuNiIhkzpp5M0RywOCFiEjmZLxEDVG9sNuIiMgFJCYCQ4Y4xRI1RDbH4IWIyEXIcIkaonphtxERERHJCoMXIiIikhUGL0RERCQrDF6IiIhIVhi8EBERkawweHERGo3pxdjUamk/ERGRK2Dw4gI0GiA+HujbFygs1N9XWChtj49nAENERK6BwYsLuHULKCkBzp8H+vW7H8AUFkp/nz8v7b91y5G1JCK1GkhLM91KSkTmYfDiAlQqID0dCA+/H8BkZt4PXMLDpf2cvIrIcZKTpZWfn3xSuk9OdnSNiORLIYQQjq6ENVmypLajaTRSa4ixoEKtBvz9AaXS/ONVb2nR0gYuoaEPWlsiqi+1WgpYqq/87O4O5OfzRwWRliXXb7a8OIgt8lRCQ4HNm/W3bd7MwKXBYNa20zp7Vj9wAYDKSmkdIiKyHIMXB7FFnkphIZCQoL8tIcEwOCIXxKxtpxYZCbjV+LZ1d5cWUCQiyzF4cRBr56lUD3rCw4GDB/WPzQDGxTFr26mpVMD69VLAAkj369aZ9/+bSb5Ehpjz4mDWyFNRq6Uf1tWDntBQw4AmI4P9607JWslPNT/wzZulpreaJwY5jFotdRVFRJj3fzE5GZg2TepycnOTAqDERNvXk8gRLLl+M3hxApmZQK9e9/8+eBDo2dP8x2t7DEpKDK9P2utZcDCwa5dlCcBkB9b+8Ji17TIcleSrVks5OpGR/LFD9sWEXRmxRp6KUild2zIyDK9PoaHSdgYuTsra3T3M2nYZjkjy5XBukgsGLw5kzTwVpdL0rySVioGL07JF8hOztmVNm+PSuLF9k3zV6vtdVIB0P306c23IOTF4cRC12vD61LOn4XWMXxwNQGio/gffq1f98lSYtS171Vs+evSQ4s76JPnWB4dzk5wweHEQf38plaHm9an6dSw4WCpHDcCDdvcwGpY9Yy0fW7YAWVlSS0x+vm2TdTmcW8Ya4BxPDF4chHkqpOdBu3v8/YHAQKBlS+PRcMuW0n5Gw07LVMvHnTtS3Gnr5NkHGc5NDtRA53iyS/CyevVqtG7dGj4+PujatSsOHDhgsuyOHTswaNAgPPzwwwgICEBcXBx2795tj2raHfNUCID1unvqGjjoWgMLXY4ztHwkJkotPPZo6SEraaBzPNk8eNm2bRtmz56NBQsW4MSJE+jTpw+GDh2KgoICo+X/7//+D4MGDcK//vUvHDt2DP3798eIESNw4sQJW1eVyP6s1d1z6xZw7RpQUGD8C6ygQNrvYl9grsRZWj5UKvu09JCVWDPpX07dT8LGYmNjxYwZM/S2tW3bVsyfP9/sY7Rr104sWbLErLIajUYAEBqNxqJ6EjnEjRtC9OghRHi4EAUF+vsKCqTtPXpI5eqiLQ9I9wcP6v9d8/jklAoLhUhLk+6JzFb9/7/2Zsn/e2t+F9WTJddvD1sGRuXl5Th27Bjmz5+vt33w4MHIzMw06xhVVVW4desWmjZtanR/WVkZysrKdH/fvHmz/hUmsjdt8pOxGXa1yU/mzrCrzW/R/uLSznzISepkRaViqwfVgzbpv/qMp5Yk/dfsfjI2Vbu2nBPkNNi02+jKlSuorKxEs2bN9LY3a9YMxcXFZh3j73//O+7cuYPRo0cb3b9s2TIolUrdLZRf0CQ31kx+4iR1RA3Tgyb9W3vOKRuzS8KuQqHQ+1sIYbDNmM8//xyLFy/Gtm3bEBwcbLRMUlISNBqN7lbIuSyoIeMkdUQNj7WS/q0155Qd2DR4CQoKgru7u0ErS0lJiUFrTE3btm1DYmIitm/fjoEDB5os5+3tjYCAAL0bUYPESeqIGh5rz/Ekk9ZbmwYvXl5e6Nq1K/bu3au3fe/evehZy8qDn3/+OSZOnIjPPvsMw4YNs2UViVwDJ6kjapisPeOpTFpvbZqwCwBz585FQkICunXrhri4OKxfvx4FBQWYMWMGAKnb57fffsOnn34KQApcxo8fj5UrV6JHjx66VhtfX18onSBJiMgpab/AAONfYNrVqTlJHZFrsWbSf83W282bpcClZhKvE1AIYfuZq1avXo33338fRUVFaN++PZYvX44nnngCADBx4kTk5+cjPT0dANCvXz9kZGQYHGPChAlISUmp87ksWVKbyKVoNMa/wACpxcXcLzAianjUamk23po5LjUDmowMmyXtWnL9tkvwYk8MXoiIiCykXWagpMSwhUUbwAQH23TdGkuu3zbvNiIiIiInZ83uJztg8EJERERSYGIqOHGS+V20uKo0ERERyQqDFyIiIpIVBi8uSk6LgxIREVmCwYsL0iaN9+1rOK9QYaG0PT6eAQwREdWDWg2kpTl00ksGLy6o5uKg2gCm+nD9khKpHBERkdmSk4GwMODJJ6X75GSHVIPBiwuS2eKgRCQjTvCjmxxFrQamTQOqqqS/q6qA6dMdcjIweHFRMloclIhkwkl+dJOjnD17P3DRqqwEfv3V7lVh8OLCZLI4KBHJgBP96CZHiYwE3GqEDe7uQESE3avC4MWFyWRxUCKSASf60U2OolIB69dLAQsg3a9b55AcBAYvMmdqSHRhIdC79/2uooMH9XNgGMAQkSWc6Ec3OVJiIpCfLyU+5edLfzsAgxcZMzUkWq2WApeCAsDbG/jmG6BnT8MkXjb3EpG5nOhHNzmaSiVdRBz44TN4kTFTQ6I1GuD336V/N2t2f6mK6km8wcHSGltEROZykh/dRFAIIYSjK2FNliyp7Qqqz90SHi4l5CYkSH+3bAn88INhgq5a7VSLgxIREVl0/eaq0jKnbU3RBjC9eknbaxsSzWZeIiKSM3YbuQAOiW6AOFMY/QdPBWqIGLy4AA6JbmA4Uxj9B08FaqgYvMhczZwXDol2cZwpjP6DpwI1ZAxenISp+VoAabuxFaDVasP1ijgk2sVxpjDZs1Y3D08FasgYvDgBU/O1ANLffftK+2sGMP7+0pDnmsm5HBLtwjhTmKxZs5uHpwI1ZAxenICp+VqqdwmVlEjlqlMqgV27gIwMw+Tc0FBp+65dHBLtUjhTmGxZu5uHpwI1ZJznxUnUNl8LV4ImA2q11D8QEcGrlUykpUktLsa29+tX/+PyVCBXYcn1m8GLE6kewGgxcCFyDWq11FVUPU/F3V2aqZZBB9mMRiM12xs7yZxsxlJLrt/sNnIinK+FyHWxm4fsrr4JlTLA4MWJcL4WItfGtYHIruqbUCkDDF6cBOdrIWoYnGBBXmooVCrDuTMyMw3n2JDhycjgxQlwvhYiIrKJ6nNnaBfAc4GRIAxenADnayEiIptxwYRKrirtBLTztfz2G6BQ6O/Tztdy86Zj6kZERDJnKqGSLS9kDYmJxpPChQBGjJBtUjgRETmKiyZUMnhxEi6cFE72ZK2Fc4hI/lw4oZLBi5Nw4aRwshdrLpxDRPLnwgmVnGHXyXCWXaoXTt9KRMZwhl2yBxdMCid7OHtWP3ABgMpKadEbImq4lErTP2BUKqcJXCzF4MXJcJZdqpfISMCtxn9nd3dptT4iIhfD4MWJuGhSONkDF84hogaEwYuTcOGkcLIXLpxDRDVpNKYvHGq1/vwblpR1ME5S5yS0SeGA8aTwfv1kmxRO9qRSsbWFiCTaVaVLSgxHfWib+oODpVlSAfPLOkGeDIMXJ6GdZddYUrh2ll0nSgonIiJnV3MCMW1QUnNYq3YCMXPLOsGFiN1GTsRFk8KJiMgRLJlATGaTjXGeFyIiIldmyQRiDpxsjPO8EJHz4JIFRI5lyQRiMplsjMELEdkOlywgcjxLJhCTyWRjDF7sQEajz4isR60Gpk27P/NvVRUwfTpbYIjsyZIJxGQ02RiDFxvTjlTr29fwcy8slLbHxzOAIRfEJQvI1Tn7L1NLJhCT2WRjDF5srOZINW0AUz3ALSm5P1KNyGVwyQJyZXL4ZWrJqtIyW4GawYuNyWz0GZH1cMkCcmVy+GWqnUAsI8Mw4VY7gZh20jlLyjoBDpW2EweOPqOGSK2Wum0iIx0fLKjVUldRRITj60IWcabTyCnVzBHZvFlKbq3+y5Rf8GbjUGknJJPRZ+QKnG2Ej0olfcHz6icrznYaOaXqXSrnzwO9ejFwsRO2vNgJW17ILtRq6UpTPVHW3V1aqJHBA5mJp5GFMjOlwEXr4EEp2ZUswpYXJyOj0WckdxzhQ1bA08gCMpkXxdUweLExmY0+I7njCB+yAp5GZuIvU4dh8FKHBx3GL7PRZyR3HOFDVsDTyAz8ZepQzHmphXYYf0mJYW6KNuAODq579JhGI42WM/YfX62WAhcnGX1GroIjfMgKeBrVwloXCNKx5PrN4KUWarU0z1DN5PGaLYUZGfyPTUTU4PCXqVU5XcLu6tWr0bp1a/j4+KBr1644cOBAreUzMjLQtWtX+Pj4IDw8HGvXrrVHNQ1wgjkiIjJJqTR9AVCpGLjYkM2Dl23btmH27NlYsGABTpw4gT59+mDo0KEoKCgwWv7ChQt46qmn0KdPH5w4cQJvvPEGXnrpJXz99de2rqpRHMZPRERW4exrIcmIzbuNunfvji5dumDNmjW6bTExMRg1ahSWLVtmUH7evHn45ptvkJeXp9s2Y8YMnDx5EllZWXU+n63meXHUMH62ShIRuQBnzZFxoouM03QblZeX49ixYxg8eLDe9sGDByMzM9PoY7KysgzKDxkyBEePHsW9e/cMypeVleHmzZt6N2tz1DB+Oaz7RUREZnDGtZBkfJGxafBy5coVVFZWolmzZnrbmzVrhuLiYqOPKS4uNlq+oqICV65cMSi/bNkyKJVK3S3Uyv04jhzG74znOhER1YMzJlHK+CJjl4RdhUKh97cQwmBbXeWNbQeApKQkaDQa3a3QitGEo4fxO+O5TkRE9eRsSZQyvsh42PLgQUFBcHd3N2hlKSkpMWhd0WrevLnR8h4eHggMDDQo7+3tDW9vb+tVuhrtBHOA8QnmtF2Utpxgrvpzac91gAnDRESypF2lt3oSpSNX6ZXpRcamLS9eXl7o2rUr9u7dq7d979696Gki2zUuLs6g/J49e9CtWzd4enrarK7GKJVS7lRGhuHnFxoqbbdHbhVXpCYichGWJFHaa3SSHC8ywsa++OIL4enpKZKTk0Vubq6YPXu28PPzE/n5+UIIIebPny8SEhJ05c+fPy8aNWok5syZI3Jzc0VycrLw9PQUX331lVnPp9FoBACh0Whs8nocoaBAiPBwIYD7t/BwaTsREclE9S/z8HAhDh7U/7v6l/qNG0L06GH8y157nB49pHLWrJcDLzKWXL9tHrwIIcSqVatEWFiY8PLyEl26dBEZGRm6fRMmTBB9+/bVK5+eni46d+4svLy8RKtWrcSaNWvMfi5XC14sOdeJnEZhoRD790v3RCT9XzD25V3zS177f8bS8vXlRBcZpwte7MmVghd7nbtEVrVxoxBubtIJ6uYm/U3U0NWnJcXWgYWTXWQsuX5zbSMn5qxzGhGZpFYDYWFAVdX9be7uQH6+U45YILKr+kwIV33Yspa1kmmd7CLDhRldJHgBnGryQ5ILtRo4exaIjLR/wJCWBjz5pPHt/frZty5kEX7XODFbTvHuRB+808ywSw+O636RRZKTpZaPJ5+U7pOT7fv8kZGAW42vFXd3ICLCvvUgi8h4olXXZ+sp3mV6kWHwQuQq1Gpg2rT7XTZVVcD06babRdEYlQpYv14KWADpft06dhk5ORlPtOraHDnFu5Nj8ELkKs6e1c81AYDKSuDXX+1bj8REKcclLU26T0y07/OTxWQ80arrcvQU707OpjPsEpEdabtsaibLOqLLRqXilU5mZDrRqutyhinenRhbXohcBbts6AHJcaJVp2CLmXCdZYp3J8XRRkSuRq2WuooiIhi4kEVsOSrXZTnZcGM542gjooZMpZK+MB80cFGrpbyVBtqn3tAwN7SemO3sEAxeiMiQo4dck10xN/QBMNvZIdhtRET6OEtug8OeDytgn9sDs+T6zdFGRKSvtiHXDF5ckjY31NhEq9rcUM6wWwdttnP1mXCZ7Wwz7DYiIn2cJbdBkulEq87D1jPhkh4GL0Skj0OuiSzDbGe7Y84LERnHIddEdVOrpcWfqifnhoYaBjQZGfx/VAfmvBDRg+MsuUR140y4DsHghYiIqL6Y7ewQDF6IiIgehFJpOjhh66VNMGGXiIiIZIXBCxEREckKgxciIiKSFQYvREREJCsMXoiIiOqi0ZhemVKtlvaT3TB4ISIiqo125cq+fQ1nyy0slLbHxzOAsSMGL0RkSK0G0tJM/9Ikakhu3ZKW3K453X/1WXRLSqRyZBcMXohIX3IyEBYGPPmkdJ+c7OgaETmWSiXNllt9vaLMTP3p/9PTOaeLHXFtIyK6T62WApaqqvvb3N2B/Hx+MVPDptEAp08DL7wgBSxa4eHA558D0dGcRfcBWXL9ZssLEd139qx+4AIAlZXSAo32xq4rchbanJcXXgD+9jf9fX/7m7SdOS92xeCFiO6LjATcanwtuLtLK0vbE7uuyJlUz3kZPVp/3+jRzHlxAAYvRHSfSgWsXy8FLIB0v26dfbuM1Gpg2rT7LUBVVcD06WyBIcdRqYDPPgM8PICKCul+7Vr9vz/7jF2rdsTghYj0JSZKOS5padJ9YqJ9n9+Zuq6IAClwHjfufqBSUQHMmKH/97hxDLDtiMELERlSqaSRFI74JeksXVdEWv7+QHCwlJy7fbv+vu3bpe3BwVI5sgsGL0TkXJyh64rqzxUTrZVKYNcuaVTRq6/q73v1VWn7rl0cbWRHDF6IXI0rXDwc3XVF9ePKidY3b94fJh0eDhw8eH/elxdekPaT3XCeFyJXkpx8P9nVzU1qweCFn+zBlecIUqulJQCqT0gXGqo/w254OJCRIf/X6kCc54WoIeIoHXIkV060rp7zog1cAOleO/Muc17sysPRFSAiK6nt4sFfg2Rr2kTrmi0vrpBorc15uXXL8P9SaKjU4uLvz5wXO2LLC5Gr4CgdciRXT7RWKk2/FpWKgYudMXghchWufvGwFldIaHZWTLQmO2HCLpGrUaulrqKICAYuNTGhmchpWXL9ZvBCRA2DK4+GIXIBHG1ERFSTK4+GIfvSaEx3O6rVXF3aDhi8EFHDwIRm+3PF/CKNBoiPl+Z9KSzU31dYKG2Pj2cAY2MMXoioYWBCs3256my7t24BJSXSxHT9+t0PYKpPWFdSIpUjm2HOCxE1LExotj1Xzy+qObPu5s1AQoLhDLxkEUuu35ykjogaFpXKNS6gzszVJ0zUzqyrDWB69ZK2M3CxG3YbERGRdTWE/KLQUKnFpbrNmxm42AmDFyIisq6GkF9UWCh1FVWXkGCYxEs2weCFiBoWVxwB44xcebbdmjkvBw9K9zWTeMlmGLwQUcPhqiNgnJVKJV3MXanFRa3WD1zS04GePe+vLq0NYBgc2xSDFyJqGNTq+0sDANL99Om8yJBl/P2B4GDD5FxtEm94uLTf39+RtXR5HG1ERA2Dq4+AIftQKoFdu6R5XGqeN6GhQEaGFLhwlWmbYvBCRA2DdgRMzblHXGkEDNmHUmk6OGEgbBfsNiKihqEhjIAhaiDY8kJEDUdiIjBkCGfYJdvTaIx3LQFSnhW7lh4Igxcialg4wy7ZmnbxxpISwxl3tcOsg4Ol3BkGMPVi026j69evIyEhAUqlEkqlEgkJCbhx44bJ8vfu3cO8efPQoUMH+Pn5oUWLFhg/fjwuXbpky2oSERFZDxdvtDmbBi/jxo1DTk4Odu3ahV27diEnJwcJNWckrOaPP/7A8ePHsXDhQhw/fhw7duzAmTNnMHLkSFtWk4iIyHpUKsN5XzIzDeeHYQtgvdlsVem8vDy0a9cO2dnZ6N69OwAgOzsbcXFx+OWXXxAdHW3WcY4cOYLY2FhcvHgRLVu2rLM8V5UmIiKnUL2lRYuLN5pkyfXbZi0vWVlZUCqVusAFAHr06AGlUonMzEyzj6PRaKBQKNCkSROj+8vKynDz5k29GxE5KU7NTw0JF2+0GZsFL8XFxQgODjbYHhwcjOLiYrOOcffuXcyfPx/jxo0zGYUtW7ZMl1OjVCoRypOCyDnZYmp+BkPkzLh4o81YHLwsXrwYCoWi1tvRo0cBAAqFwuDxQgij22u6d+8exo4di6qqKqxevdpkuaSkJGg0Gt2tkCcFkfOxxdT8XKeInBkXb7Qpi4dKz5o1C2PHjq21TKtWrfDjjz/i999/N9h3+fJlNGvWrNbH37t3D6NHj8aFCxewf//+Wvu+vL294e3tbV7licgxrD01v6lgaMgQJkGS4xlbvFG79pF2e79+0lICPF/rxeLgJSgoCEFBQXWWi4uLg0ajweHDhxEbGwsAOHToEDQaDXr27GnycdrA5ezZs0hLS0NgYKClVSQiZ2Ptqfm5TlH9qdXS+xcZyffKVrSLNwLGF2/UzvPCxRvrzWY5LzExMYiPj8fUqVORnZ2N7OxsTJ06FcOHD9cbadS2bVukpqYCACoqKvDcc8/h6NGj2Lp1KyorK1FcXIzi4mKUl5fbqqpEZI4HyS+x9tT82mCoOq5TVDd2tdmHdvHGjAzD5Fzt4o2coO7BCBu6evWqePHFF4W/v7/w9/cXL774orh+/bpeGQBi06ZNQgghLly4IAAYvaWlpZn1nBqNRgAQGo3Gui+GqCHbuFEINzchAOl+48b6HaewUIi0NOneGnVyd5fq5O5e/zo1FIWF9z9D7c3d3TqfBZEVWHL9ttk8L47CeV6IrEytln6l1+zyyc93fLeDWs11isyVlia1uBjb3q+f3atjNnZzNRhOMc8LEbmI2vJLHE2lki68vKjVTY5dbdbu5uLQepfB4IWIaifHix4Zsnbeka1Ze3g9831cCoMXIqqd3C56ZFpiotTdl5Ym3ScmOrpGplmzxc8W8wyRQ1k8VJqIGqDERGkOFeaXyJ9KJY/Pz5rD6zm03uWw5YWIzNMQ8kuYE+E8rNnix65Pl8PghYgIYE6EM7JWNxe7Pl0Oh0oTETnzcHCyHg6td2qWXL+Z80JExJyIhkEu+T5UJ3YbERExJ4JIVhi8EBExJ4JIVthtREQEcDg4kYwweCEi0mJOBJEssNuIiIiIZIXBCxEREckKgxciIiKSFQYvRETWxCUGiGyOwQsRkbU4aokBBkzUwDB4ISKyBrUamDbt/ky9VVXA9Om2DyjksCYTgyuyMgYvRETWUNsSA7biqIDJEnIIrkh2GLwQEVmDI5YYyMy0f8BkCTkEVyRLDF6IiKzB3ksMJCcDY8cabnemNZkc0RpFDQJn2CUishZ7LTGgbdEQQn+7s63JpG2Nqh7AOFNwRbLFlhciImtSqYB+/WwbQBhr0QCAzz+XAihnYUlrFJN6yQIMXoiI5MZUfk1cnGPqU5vERCA/XwpM8vONB1dM6iULMXghIpIbe+fXPKjaWqOY1Ev1wOCFiEiOzGnRkANTSb1ffskAhkxi8EJEJFf2yK+xNWNdYAAwdy67kMgkBi9EROQ4NbvAqmMXEpnA4IWIyJ44qsaQtgvsww8N93FeGDKCwQsRkb1wVI1pKhXw/PP2n6WYZInBCxGRPXBUTd3kNoqKHIYz7BIR2UNtU+Xz4nyfvWYpJllj8EJEZAtqtRSwREZKF2BOlW8+lYpBC9WK3UZERNZmLLeFXSJEVqMQoubKXvJ28+ZNKJVKaDQaBAQEOLo6RNTQqNVSwFKzhSU/XwpU1Gp2iRAZYcn1m91GRETWVFduC7tEiB4Yu42IiKzJ1KKJzG0hshoGL0RE1sTcFiKbY7cREZG1cbgvkU0xeCEisgXmthDZDLuNiIiISFYYvBAREZGsMHghIiIiWWHwQkRERLLC4IWIiIhkhcELEVFDpFYDaWnSPZHMMHghImpojC0cSSQjDF6IiBoStRqYNu3++ktVVcD06WyBIVlh8EJE1JDUtnAkkUwweCEiaki4cCS5AAYvREQNCReOJBfAtY2IiBoaLhxJMsfghYioIeLCkSRj7DYiIiIiWWHwQkRERLLC4IWIiIhkxabBy/Xr15GQkAClUgmlUomEhATcuHHD7MdPnz4dCoUCK1assFkdiYiISF5sGryMGzcOOTk52LVrF3bt2oWcnBwkJCSY9didO3fi0KFDaNGihS2rSERERDJjs9FGeXl52LVrF7Kzs9G9e3cAwIYNGxAXF4fTp08jOjra5GN/++03zJo1C7t378awYcNsVUUiIiKSIZu1vGRlZUGpVOoCFwDo0aMHlEolMjMzTT6uqqoKCQkJeO211/Doo4/W+TxlZWW4efOm3o2IiIhcl82Cl+LiYgQHBxtsDw4ORnFxscnHvffee/Dw8MBLL71k1vMsW7ZMl1OjVCoRGhpa7zoTERGR87M4eFm8eDEUCkWtt6NHjwIAFAqFweOFEEa3A8CxY8ewcuVKpKSkmCxTU1JSEjQaje5WWFho6UsiIiIiGbE452XWrFkYO3ZsrWVatWqFH3/8Eb///rvBvsuXL6NZs2ZGH3fgwAGUlJSgZcuWum2VlZV45ZVXsGLFCuTn5xs8xtvbG97e3pa9CCIiIpIti4OXoKAgBAUF1VkuLi4OGo0Ghw8fRmxsLADg0KFD0Gg06Nmzp9HHJCQkYODAgXrbhgwZgoSEBEyaNMnSqhIREZELstloo5iYGMTHx2Pq1KlYt24dAGDatGkYPny43kijtm3bYtmyZfjTn/6EwMBABAYG6h3H09MTzZs3r3V0UnVCCABg4i4REZGMaK/b2ut4bWy6MOPWrVvx0ksvYfDgwQCAkSNH4uOPP9Yrc/r0aWg0Gqs9561btwCAibtEREQydOvWLSiVylrLKIQ5IY6MVFVV4dKlS/D39zc76Zcsd/PmTYSGhqKwsBABAQGOro7L4vtsP3yv7YPvs33I8X0WQuDWrVto0aIF3NxqH09k05YXR3Bzc4OKy7zbTUBAgGz+Y8gZ32f74XttH3yf7UNu73NdLS5aXJiRiIiIZIXBCxEREckKgxeqF29vbyxatIhz7NgY32f74XttH3yf7cPV32eXS9glIiIi18aWFyIiIpIVBi9EREQkKwxeiIiISFYYvBAREZGsMHghIiIiWWHwQma7fv06EhISoFQqoVQqkZCQgBs3bpgsf+/ePcybNw8dOnSAn58fWrRogfHjx+PSpUv2q7QMrF69Gq1bt4aPjw+6du2KAwcO1Fo+IyMDXbt2hY+PD8LDw7F27Vo71VTeLHmfd+zYgUGDBuHhhx9GQEAA4uLisHv3bjvWVt4sPae1Dh48CA8PD3Tq1Mm2FXQRlr7PZWVlWLBgAcLCwuDt7Y02bdrgk08+sVNtrUwQmSk+Pl60b99eZGZmiszMTNG+fXsxfPhwk+Vv3LghBg4cKLZt2yZ++eUXkZWVJbp37y66du1qx1o7ty+++EJ4enqKDRs2iNzcXPHyyy8LPz8/cfHiRaPlz58/Lxo1aiRefvllkZubKzZs2CA8PT3FV199Zeeay4ul7/PLL78s3nvvPXH48GFx5swZkZSUJDw9PcXx48ftXHP5sfS91rpx44YIDw8XgwcPFo899ph9Kitj9XmfR44cKbp37y727t0rLly4IA4dOiQOHjxox1pbD4MXMktubq4AILKzs3XbsrKyBADxyy+/mH2cw4cPCwB1fpE1FLGxsWLGjBl629q2bSvmz59vtPzrr78u2rZtq7dt+vTpokePHjaroyuw9H02pl27dmLJkiXWrprLqe97PWbMGPHmm2+KRYsWMXgxg6Xv87///W+hVCrF1atX7VE9m2O3EZklKysLSqUS3bt3123r0aMHlEolMjMzzT6ORqOBQqFAkyZNbFBLeSkvL8exY8cwePBgve2DBw82+Z5mZWUZlB8yZAiOHj2Ke/fu2ayuclaf97mmqqoq3Lp1C02bNrVFFV1Gfd/rTZs24dy5c1i0aJGtq+gS6vM+f/PNN+jWrRvef/99PPLII4iKisKrr76K0tJSe1TZ6lxuVWmyjeLiYgQHBxtsDw4ORnFxsVnHuHv3LubPn49x48bJapVTW7ly5QoqKyvRrFkzve3NmjUz+Z4WFxcbLV9RUYErV64gJCTEZvWVq/q8zzX9/e9/x507dzB69GhbVNFl1Oe9Pnv2LObPn48DBw7Aw4OXJHPU530+f/48fvjhB/j4+CA1NRVXrlzBzJkzce3aNVnmvbDlpYFbvHgxFApFrbejR48CABQKhcHjhRBGt9d07949jB07FlVVVVi9erXVX4ec1Xz/6npPjZU3tp30Wfo+a33++edYvHgxtm3bZjSAJ0PmvteVlZUYN24clixZgqioKHtVz2VYck5XVVVBoVBg69atiI2NxVNPPYUPP/wQKSkpsmx9YZjbwM2aNQtjx46ttUyrVq3w448/4vfffzfYd/nyZYPov6Z79+5h9OjRuHDhAvbv389Wl/8ICgqCu7u7wS+lkpISk+9p8+bNjZb38PBAYGCgzeoqZ/V5n7W2bduGxMREfPnllxg4cKAtq+kSLH2vb926haNHj+LEiROYNWsWAOkiK4SAh4cH9uzZgyeffNIudZeT+pzTISEheOSRR6BUKnXbYmJiIISAWq1GZGSkTetsbWx5aeCCgoLQtm3bWm8+Pj6Ii4uDRqPB4cOHdY89dOgQNBoNevbsafL42sDl7Nmz+P7773mBrcbLywtdu3bF3r179bbv3bvX5HsaFxdnUH7Pnj3o1q0bPD09bVZXOavP+wxILS4TJ07EZ599hmHDhtm6mi7B0vc6ICAAp06dQk5Oju42Y8YMREdHIycnRy/Hju6rzzndq1cvXLp0Cbdv39ZtO3PmDNzc3KBSqWxaX5twXK4wyU18fLzo2LGjyMrKEllZWaJDhw4GQ6Wjo6PFjh07hBBC3Lt3T4wcOVKoVCqRk5MjioqKdLeysjJHvASnox3umJycLHJzc8Xs2bOFn5+fyM/PF0IIMX/+fJGQkKArrx0qPWfOHJGbmyuSk5M5VNoMlr7Pn332mfDw8BCrVq3SO29v3LjhqJcgG5a+1zVxtJF5LH2fb926JVQqlXjuuefEzz//LDIyMkRkZKSYMmWKo17CA2HwQma7evWqePHFF4W/v7/w9/cXL774orh+/bpeGQBi06ZNQgghLly4IAAYvaWlpdm9/s5q1apVIiwsTHh5eYkuXbqIjIwM3b4JEyaIvn376pVPT08XnTt3Fl5eXqJVq1ZizZo1dq6xPFnyPvft29foeTthwgT7V1yGLD2nq2PwYj5L3+e8vDwxcOBA4evrK1QqlZg7d674448/7Fxr61AI8Z9sPyIiIiIZYM4LERERyQqDFyIiIpIVBi9EREQkKwxeiIiISFYYvBAREZGsMHghIiIiWWHwQkRERLLC4IWIiIhkhcELERERyQqDFyIiIpIVBi9EREQkK/8f9eV4/SzEKBQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_b_good[:,0], x_b_good[:,1], c='b', marker=\".\", label=\"Good class 0\")\n",
    "plt.scatter(x_b_bad[:,0], x_b_bad[:,1], c='b', marker=\"x\", label=\"Bad class 0\")\n",
    "plt.scatter(x_r_good[:,0], x_r_good[:,1], c='r', marker=\".\", label=\"Good class 1\")\n",
    "plt.scatter(x_r_bad[:,0], x_r_bad[:,1], c='r', marker=\"x\", label=\"Bad class 1\")\n",
    "\n",
    "plt.title(\"Moon dataset classification\")\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ba0b25e8af5c74af8e2c6ced0e74862d1fdf41eb019455e1e720bbed2b1ca1d9"
  },
  "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}