vqc_pca_mnist_kernel_attempt_seb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1143da08",
   "metadata": {},
   "outputs": [],
   "source": [
    "### going to use the same steps to create data, and then try to use the kernal method###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57bfd5ed-186f-461b-b796-3f8a6e2be619",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3ebf33-fb21-4755-91f2-46a23f947d4f",
   "metadata": {},
   "source": [
    "**Importing MNIST dataset using Tensorflow**\n",
    "\n",
    "In this step, we assume you have intmernet connectivity. Connectivity is necessary for this tutorial because the tensorflow library imports the dataset from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz by default. If you do not have internet connectivity, for some strange reason, you can follow our \"How to import the MNIST dataset from a local directory\" tutorial.\n",
    "\n",
    "To load the MNIST dataset, use the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "150f3f87-b03c-4b4c-88d2-d5fb4d5e884d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "003e8e30-d6f5-4653-8161-90276461b29f",
   "metadata": {},
   "source": [
    "The MNIST dataset will be loaded as a set of training and test inputs (X) and outputs (Y). The imputs are samples of digit images while the outputs contain the numerical value each input represents. The exact shape of each dataset is provided below for your convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d8aaae1b-fc94-4e60-9aa1-295a6f6b9d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data() #loads everything, we only want some specific stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3e79a11b-c759-4902-8d03-d476a1744f1c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MNIST Dataset Shape:\n",
      "x_train: (60000, 28, 28)\n",
      "y_train: (60000,)\n",
      "x_test:  (10000, 28, 28)\n",
      "y_test:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('MNIST Dataset Shape:')\n",
    "print('x_train: ' + str(x_train.shape))\n",
    "print('y_train: ' + str(y_train.shape))\n",
    "print('x_test:  '  + str(x_test.shape))\n",
    "print('y_test:  '  + str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f43133fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "460ddc1e-d37b-4626-8e63-dfa9eb7d064f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sebts\\AppData\\Local\\Temp\\ipykernel_5052\\4053146574.py:8: DeprecationWarning: ``qiskit.algorithms`` has been migrated to an independent package: https://github.com/qiskit-community/qiskit-algorithms. The ``qiskit.algorithms`` import path is deprecated as of qiskit-terra 0.25.0 and will be removed no earlier than 3 months after the release date. Please run ``pip install qiskit_algorithms`` and use ``import qiskit_algorithms`` instead.\n",
      "  from qiskit.algorithms.optimizers import ADAM, SPSA\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "import tensorflow as tf\n",
    "from sklearn.decomposition import PCA\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": "markdown",
   "id": "1bc32a3c-6817-4f01-bc06-bc32b36467db",
   "metadata": {},
   "source": [
    "**Load the MNIST Dataset**\n",
    "\n",
    "MNIST dataset consists of thousand images of hand-written digits from 0 to 9. Every image has 28x28 pixels and every pixel's value is between 0 to 255. This dataset is too large for us. So we need to reduce it.\n",
    "\n",
    "1). Normalizing the pixel's value in the range of [0,1].\n",
    "\n",
    "2). Filtering images from the dataset, leaving only the images with number 3 and 6 as their labels.\n",
    "\n",
    "3). Reducing the total number of images in the dataset and divide the dataset into training dataset and testing dataset.\n",
    "\n",
    "4). Resize the image from 28x28 pixels to 16x16 pixels because QPCA needs the size of the image in the form of 2^n x 2^n pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f86806a-b8ad-4f98-b6b3-b1440ffd4817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of original training examples: 60000\n",
      "Number of original test examples: 10000\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "# Rescale the images from [0,255] to the [0.0,1.0] range.\n",
    "x_train, x_test = x_train[..., np.newaxis]/255.0, x_test[..., np.newaxis]/255.0 #this rescales the int intensity to 0-1 float\n",
    "#also for some reason adds another axis? (see shape below)\n",
    "\n",
    "print(\"Number of original training examples:\", len(x_train))\n",
    "print(\"Number of original test examples:\", len(x_test))\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ee04d089",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "print(x_train.shape) # from (60000, 28, 28)?\n",
    "print(x_train[0].shape) #-- a single image but with an extra set of []\n",
    "print(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7ddf841-356c-4e3f-9ac6-03a8ff46f0f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e21251ee-0cbd-402b-8aac-4bff71c15974",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of filtered training examples: 12665\n",
      "Number of filtered test examples: 2115\n"
     ]
    }
   ],
   "source": [
    "# choose the number to be classified here, in this case I selected number 0 and 1\n",
    "number_set = [0, 1]\n",
    "\n",
    "mask_train = np.isin(y_train, number_set) #is in is useful\n",
    "mask_test = np.isin(y_test, number_set)\n",
    "\n",
    "x_train = x_train[mask_train]\n",
    "y_train = y_train[mask_train]\n",
    "x_test = x_test[mask_test]\n",
    "y_test = y_test[mask_test]\n",
    "\n",
    "print(\"Number of filtered training examples:\", len(x_train))\n",
    "print(\"Number of filtered test examples:\", len(x_test))\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "26ebc1a3-85e2-4233-bc69-04efbcc58a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 1, 0, 1], dtype=uint8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train # check we only have 1 and 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "439acb74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12665,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7168a2a-7d00-4dba-bd3a-57be26db9566",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80, 28, 28, 1) (80,)\n",
      "(20, 28, 28, 1) (20,)\n"
     ]
    }
   ],
   "source": [
    "# next, reduce the number of images inside the dataset\n",
    "\n",
    "index_train = np.array([], dtype=int)\n",
    "index_test = np.array([], dtype=int)\n",
    "\n",
    "# decide how many images we want inside the dataset\n",
    "num_data = 100\n",
    "# decide what is the percentage of the dataset we want for the training dataset\n",
    "train_percentage = 0.8\n",
    "\n",
    "# train data\n",
    "counter = np.array([0,0], dtype=int)\n",
    "for i in range (len(x_train)):\n",
    "    if y_train[i] == True and counter[0] < int(train_percentage*num_data/2): # num_data/2 because we want a 50-50 split of 1's and 0's\n",
    "        index_train = np.append(index_train, np.array([int(i)]), axis=0)\n",
    "        counter[0] += 1\n",
    "\n",
    "    if y_train[i] == False and counter[1] < int(train_percentage*num_data/2): # the == True and False works to select 0 and 1\n",
    "        index_train = np.append(index_train, np.array([int(i)]), axis=0)\n",
    "        counter[1] += 1\n",
    "\n",
    "# test data\n",
    "counter = np.array([0,0], dtype=int)\n",
    "for i in range (len(x_test)):\n",
    "    if y_test[i] == True and counter[0] < int(np.round(1-train_percentage, 2)*num_data/2):\n",
    "        index_test = np.append(index_test, np.array([int(i)]), axis=0)\n",
    "        counter[0] += 1\n",
    "\n",
    "    if y_test[i] == False and counter[1] < int(np.round(1-train_percentage, 2)*num_data/2):\n",
    "        index_test = np.append(index_test, np.array([int(i)]), axis=0)\n",
    "        counter[1] += 1\n",
    "\n",
    "x_train = x_train[index_train]\n",
    "y_train = y_train[index_train]\n",
    "x_test = x_test[index_test]\n",
    "y_test = y_test[index_test]\n",
    "\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f9af6816-856c-4fc1-a9ab-03d3faab30c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print some images from the final dataset\n",
    "fig, axes = plt.subplots(2, 5, figsize=(10, 5))\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(5):\n",
    "        axes[i, j].imshow(x_train[i + j].squeeze(), cmap='gray') #imshow() is for images, and cmap grey i think interprets 0-1\n",
    "        # the .squeeze() removes and dimensions of 1, so the extra dimension we added?\n",
    "        axes[i, j].set_title(\"Label: {}\".format(y_train[i + j]))\n",
    "        axes[i, j].axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8dad08b0-a3a7-44f1-b2f5-996292252afa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1e0d115b2e0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot an example of 1 image\n",
    "\n",
    "print(y_train[0])\n",
    "\n",
    "plt.imshow(x_train[0, :, :, 0]) #this is 1st image [0, data :,:, and then removing last dim 0]\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9170ef0-c8f2-476d-9094-e1346d4c0762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80, 16, 16, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resize the images\n",
    "\n",
    "x_train_small = tf.image.resize(x_train, (16,16)).numpy() #tf is tensor flow, which I imagine is the resizing method?\n",
    "x_test_small = tf.image.resize(x_test, (16,16)).numpy() #.numpy() puts into np array?\n",
    "print(x_train_small.shape)\n",
    "len(x_train_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cae94f70-fbc0-497b-b296-5c9485918633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1e15c52ae80>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot an example of 1 resized image\n",
    "\n",
    "print(y_train[0])\n",
    "\n",
    "plt.imshow(x_train_small[0,:,:,0], vmin=0, vmax=1)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57dd93e9-bc71-47ec-8329-966df90f5c66",
   "metadata": {},
   "source": [
    "**Classical Principal Component Analysis**\n",
    "\n",
    "We will use the well-known scikit-learn package for the classical PCA (https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1d6e2484-3444-4eb7-886b-0ae8af713189",
   "metadata": {},
   "outputs": [],
   "source": [
    "# decide the number of principal components to be used\n",
    "#from sklearn.decomposition import PCA -- can do 2^n pixels, (16x16=256) above it says 2^n x 2^n but this doesn't seem right\n",
    "n_components_classic = 8\n",
    "\n",
    "pca = PCA(n_components=n_components_classic, whiten = False, random_state = 2020) #reduces dimensionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a5cc4e4-afdb-4b87-875e-5d583da65b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((80, 8), (20, 8))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_small_pca = pca.fit_transform(x_train_small.reshape(len(x_train_small),16**2)) #reshapes to 80,256 then reduces to 80,8 using pca\n",
    "x_test_small_pca = pca.fit_transform(x_test_small.reshape(len(x_test_small),16**2))\n",
    "x_train_small_pca.shape, x_test_small_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d0a487c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.149694   -1.0433712  -0.18994118 -1.1095574  -0.06876877  0.44443968\n",
      "  0.78277516  0.3613933 ]\n"
     ]
    }
   ],
   "source": [
    "print(x_train_small_pca[0]) #not normalised, hence next step, could probably wait until this point to normalise?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ad742a-b5ec-43a7-82e3-7db3a9eb78f2",
   "metadata": {},
   "source": [
    "**Dataset Reformatting**\n",
    "\n",
    "VQC class from Qiskit need a specific format for the dataset. This section is dedicated to do that. \n",
    "You can read the documentation here (https://qiskit.org/documentation/stubs/qiskit.aqua.algorithms.VQC.html). THIS LINK DOESN'T WORK BUT IT'S A VQC SO SIMILAR TO THE VQC EXAMPLE WE HAVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cfc3803c-b0c2-4b2b-9b53-d1b197704334",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(x):\n",
    "  return (x - x.min())/(x.max() - x.min())\n",
    "\n",
    "x_train_small_pca = normalize(x_train_small_pca)\n",
    "x_test_small_pca = normalize(x_test_small_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "97c1ca14-3b3c-4c0b-a600-f4704bac3cf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#only use these in forward and backwards pass, which is just to show what each step of the algo does\n",
    "x_total = np.concatenate((x_train_small_pca, x_test_small_pca))\n",
    "x_total.shape #so we could have just taken all data from test or train, just keeping convention of data set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea536340",
   "metadata": {},
   "source": [
    "# here is where i'm trying to branch off\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "40d4edec",
   "metadata": {},
   "outputs": [],
   "source": [
    "###\n",
    "\n",
    "\n",
    "### here is where i'm trying to branch off\n",
    "\n",
    "\n",
    "###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df2499b",
   "metadata": {},
   "source": [
    "## I want to reorder the data set so 0's are first and 1's second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c84c8a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.73806244, 0.25824726, 0.3864904 , 0.2483016 , 0.40469873,\n",
       "        0.48181748, 0.53265846, 0.46933827],\n",
       "       [0.10506941, 0.33157292, 0.10688335, 0.552154  , 0.40461284,\n",
       "        0.5765405 , 0.39237428, 0.363564  ],\n",
       "       [0.1137721 , 0.48358506, 0.74281305, 0.37745547, 0.45680493,\n",
       "        0.5246972 , 0.27203724, 0.3161743 ],\n",
       "       [0.0972812 , 0.50665504, 0.67195916, 0.39425394, 0.472204  ,\n",
       "        0.5078617 , 0.44623274, 0.4435359 ],\n",
       "       [0.08600479, 0.50073594, 0.64629906, 0.3942543 , 0.44020706,\n",
       "        0.4574626 , 0.443825  , 0.43025467],\n",
       "       [0.7445317 , 0.28414088, 0.28251967, 0.16118175, 0.434403  ,\n",
       "        0.5481596 , 0.4716669 , 0.4531483 ],\n",
       "       [0.10694544, 0.3416488 , 0.12409244, 0.5646306 , 0.40370503,\n",
       "        0.58927804, 0.39629307, 0.36454707],\n",
       "       [0.2784144 , 0.3328346 , 0.42240208, 0.32197303, 0.37505803,\n",
       "        0.45419744, 0.3795354 , 0.42785296],\n",
       "       [0.5717578 , 0.0197521 , 0.4566309 , 0.54267925, 0.38951346,\n",
       "        0.33407667, 0.17543308, 0.5658302 ],\n",
       "       [0.711228  , 0.        , 0.42803994, 0.44457194, 0.32801688,\n",
       "        0.33383316, 0.30082262, 0.5483615 ],\n",
       "       [0.04633472, 0.43488863, 0.530949  , 0.29996452, 0.4675396 ,\n",
       "        0.30796978, 0.47211236, 0.4435215 ],\n",
       "       [0.9403438 , 0.44983163, 0.3338021 , 0.2749186 , 0.70838434,\n",
       "        0.5137607 , 0.47629145, 0.3426932 ],\n",
       "       [0.86421573, 0.6384352 , 0.3470879 , 0.49325326, 0.5894072 ,\n",
       "        0.30513144, 0.36394584, 0.38046873],\n",
       "       [0.10770281, 0.31833348, 0.09808669, 0.54355824, 0.39336735,\n",
       "        0.5360231 , 0.38958737, 0.3700873 ],\n",
       "       [0.8032603 , 0.16856124, 0.3702102 , 0.25210547, 0.70674103,\n",
       "        0.496902  , 0.3814997 , 0.3984887 ],\n",
       "       [0.03751961, 0.3865244 , 0.34394953, 0.399694  , 0.42549342,\n",
       "        0.41675082, 0.41935298, 0.42520952],\n",
       "       [0.45163268, 0.80978984, 0.3190248 , 0.5708655 , 0.3078619 ,\n",
       "        0.31548303, 0.3876815 , 0.66048217],\n",
       "       [0.7906855 , 0.13223657, 0.5559989 , 0.6770594 , 0.56469905,\n",
       "        0.28488776, 0.30231538, 0.42892286],\n",
       "       [0.23901993, 0.47659808, 0.70599145, 0.36156943, 0.26984802,\n",
       "        0.4006343 , 0.31825724, 0.2790736 ],\n",
       "       [0.06734036, 0.5070359 , 0.5961946 , 0.36097056, 0.39652172,\n",
       "        0.39795032, 0.49375024, 0.42446673],\n",
       "       [0.70158845, 0.10283644, 0.4469627 , 0.45398912, 0.6140579 ,\n",
       "        0.4006508 , 0.42714858, 0.5354386 ],\n",
       "       [0.07019936, 0.31024182, 0.16079313, 0.47394535, 0.45843348,\n",
       "        0.45692858, 0.3716423 , 0.41115367],\n",
       "       [0.05762295, 0.29736862, 0.16995504, 0.44251436, 0.38572094,\n",
       "        0.37388685, 0.37630793, 0.33940467],\n",
       "       [0.7518769 , 0.09262442, 0.5393753 , 0.49886042, 0.5106627 ,\n",
       "        0.37903425, 0.36724958, 0.5677113 ],\n",
       "       [0.7794662 , 0.679767  , 0.3753523 , 0.50476897, 0.5417367 ,\n",
       "        0.33904243, 0.4672142 , 0.19529052],\n",
       "       [0.8163517 , 0.09502193, 0.53026885, 0.56660503, 0.41432548,\n",
       "        0.3222054 , 0.43859613, 0.43753353],\n",
       "       [0.07524204, 0.3891628 , 0.21970437, 0.46172345, 0.4139107 ,\n",
       "        0.46765432, 0.4674502 , 0.40129948],\n",
       "       [0.09044107, 0.4944546 , 0.6402346 , 0.37226117, 0.42751166,\n",
       "        0.41437697, 0.45437038, 0.42868584],\n",
       "       [0.132357  , 0.5186771 , 0.70787567, 0.44346368, 0.4188273 ,\n",
       "        0.5997387 , 0.36978438, 0.40032604],\n",
       "       [0.05153661, 0.28780505, 0.18534315, 0.41266453, 0.3846446 ,\n",
       "        0.367644  , 0.35746962, 0.39097568],\n",
       "       [0.5311128 , 0.8270567 , 0.3564544 , 0.53902835, 0.22548738,\n",
       "        0.3325441 , 0.29617777, 0.6179568 ],\n",
       "       [0.04678722, 0.45107856, 0.5438599 , 0.31940433, 0.44267413,\n",
       "        0.3646111 , 0.48639172, 0.4496521 ],\n",
       "       [0.08207135, 0.28747198, 0.14047375, 0.4569284 , 0.4216523 ,\n",
       "        0.4633014 , 0.32512683, 0.35937065],\n",
       "       [0.8585709 , 0.6172158 , 0.36112988, 0.18920393, 0.44032   ,\n",
       "        0.45580298, 0.37716827, 0.49515852],\n",
       "       [0.65816057, 0.4162098 , 0.5228211 , 0.444205  , 0.37674135,\n",
       "        0.46487054, 0.60460967, 0.32974544],\n",
       "       [0.8545929 , 0.19227481, 0.2745723 , 0.12604047, 0.42922306,\n",
       "        0.54704046, 0.3896706 , 0.5100528 ],\n",
       "       [0.8525715 , 0.8064972 , 0.2668282 , 0.39345452, 0.29076645,\n",
       "        0.29858303, 0.3208989 , 0.31248808],\n",
       "       [0.06949949, 0.47876605, 0.48848966, 0.38472286, 0.41780627,\n",
       "        0.3874484 , 0.5537066 , 0.47749656],\n",
       "       [0.0476693 , 0.36281264, 0.5545116 , 0.24913785, 0.45776758,\n",
       "        0.25755322, 0.34114107, 0.36807394],\n",
       "       [0.24322653, 0.5350247 , 0.65032107, 0.56557596, 0.4230016 ,\n",
       "        0.6915189 , 0.24311218, 0.4180346 ],\n",
       "       [0.07527701, 0.41994378, 0.43372402, 0.32572076, 0.38395175,\n",
       "        0.22478214, 0.48684248, 0.41587338],\n",
       "       [0.6347738 , 0.6474668 , 0.3725723 , 0.2147289 , 0.29600987,\n",
       "        0.60438776, 0.46691707, 0.5391574 ],\n",
       "       [0.7235774 , 0.05529329, 0.57295287, 0.5981046 , 0.45781884,\n",
       "        0.27356544, 0.33656925, 0.47781113],\n",
       "       [0.05139879, 0.4488511 , 0.47144508, 0.337725  , 0.4331011 ,\n",
       "        0.32800123, 0.5128826 , 0.4639914 ],\n",
       "       [0.06033603, 0.28736   , 0.19867393, 0.37693143, 0.4510244 ,\n",
       "        0.3687706 , 0.34140876, 0.35907814],\n",
       "       [0.0963003 , 0.52492315, 0.6997328 , 0.3900082 , 0.42551613,\n",
       "        0.47953406, 0.37704992, 0.37287867],\n",
       "       [0.77999556, 0.23551679, 0.45598698, 0.47418374, 0.16911739,\n",
       "        0.41154987, 0.58632576, 0.3608914 ],\n",
       "       [0.1273223 , 0.47483838, 0.7771986 , 0.35457826, 0.4151834 ,\n",
       "        0.5087096 , 0.26969603, 0.34606495],\n",
       "       [0.11541902, 0.32509184, 0.0994782 , 0.5590686 , 0.4109945 ,\n",
       "        0.57464147, 0.37961233, 0.36085528],\n",
       "       [0.10540772, 0.3042831 , 0.14044969, 0.5301901 , 0.4168427 ,\n",
       "        0.53753877, 0.3454478 , 0.38931584],\n",
       "       [0.7585733 , 0.76631373, 0.3197831 , 0.34741214, 0.67588097,\n",
       "        0.46260107, 0.39797646, 0.42768118],\n",
       "       [0.029661  , 0.3610358 , 0.41115856, 0.25450718, 0.42329344,\n",
       "        0.23007192, 0.44185024, 0.39146125],\n",
       "       [0.8026749 , 0.8740134 , 0.30137855, 0.3973806 , 0.4119539 ,\n",
       "        0.315251  , 0.30963627, 0.36109054],\n",
       "       [0.5072197 , 0.4985644 , 0.47908908, 0.64649624, 0.5040607 ,\n",
       "        0.45411307, 0.67306226, 0.37055898],\n",
       "       [0.04138799, 0.337375  , 0.38742948, 0.28486785, 0.42629328,\n",
       "        0.22558884, 0.38736027, 0.3838629 ],\n",
       "       [0.6031562 , 0.37685338, 0.576782  , 0.7826315 , 0.60049677,\n",
       "        0.3810683 , 0.52043056, 0.41860804],\n",
       "       [0.4114345 , 0.39279056, 0.36089772, 0.3868929 , 0.46887535,\n",
       "        0.53145677, 0.4198799 , 0.34291816],\n",
       "       [0.76484084, 0.8029412 , 0.32601202, 0.4624625 , 0.63659513,\n",
       "        0.30159122, 0.38645396, 0.3638466 ],\n",
       "       [0.04129366, 0.35397023, 0.36949974, 0.33536583, 0.42439595,\n",
       "        0.22775102, 0.410238  , 0.41238132],\n",
       "       [0.40076396, 0.3550572 , 0.53987455, 0.64971924, 0.2259242 ,\n",
       "        0.52567476, 0.46463776, 0.51756364],\n",
       "       [0.6583425 , 0.39433166, 0.44142276, 0.569613  , 0.33363208,\n",
       "        0.47886017, 0.6103126 , 0.34393278],\n",
       "       [0.459964  , 0.8115786 , 0.36220676, 0.5621589 , 0.5009898 ,\n",
       "        0.4966305 , 0.43120477, 0.66906875],\n",
       "       [0.15213858, 0.50844014, 0.77024406, 0.42019793, 0.47886714,\n",
       "        0.55238414, 0.23102453, 0.29702273],\n",
       "       [0.6899346 , 0.9121564 , 0.3411851 , 0.46358055, 0.40986407,\n",
       "        0.27913672, 0.29688382, 0.4666016 ],\n",
       "       [0.03791288, 0.3309042 , 0.36567393, 0.30225444, 0.4554235 ,\n",
       "        0.23404327, 0.38864398, 0.3686143 ],\n",
       "       [0.37123397, 0.636269  , 0.39919955, 0.35202426, 0.3128192 ,\n",
       "        0.5768097 , 0.48207363, 0.62850964],\n",
       "       [0.03901835, 0.35751113, 0.2815945 , 0.37368274, 0.3961461 ,\n",
       "        0.3128863 , 0.45089334, 0.41585267],\n",
       "       [0.0786326 , 0.48231485, 0.48404425, 0.3950379 , 0.37989438,\n",
       "        0.38665265, 0.5631816 , 0.49352416],\n",
       "       [0.04689129, 0.45612878, 0.4410786 , 0.35237354, 0.37926263,\n",
       "        0.35697225, 0.55203885, 0.46585608],\n",
       "       [0.91335297, 0.7597725 , 0.29030794, 0.41859093, 0.22542033,\n",
       "        0.35922065, 0.3518234 , 0.2716335 ],\n",
       "       [0.0338716 , 0.37375528, 0.31580216, 0.36894414, 0.3996314 ,\n",
       "        0.29568437, 0.46397606, 0.43786862],\n",
       "       [1.        , 0.4011013 , 0.4317367 , 0.41008556, 0.23791385,\n",
       "        0.4587858 , 0.40205374, 0.21275373],\n",
       "       [0.7227521 , 0.43667483, 0.43925527, 0.43666333, 0.13302314,\n",
       "        0.37925038, 0.47013396, 0.32034212],\n",
       "       [0.95572865, 0.23859647, 0.45666456, 0.30045214, 0.34222984,\n",
       "        0.4214381 , 0.40723544, 0.378746  ],\n",
       "       [0.06446055, 0.356496  , 0.1953164 , 0.4582024 , 0.38569322,\n",
       "        0.40585962, 0.46014965, 0.4071339 ],\n",
       "       [0.75297654, 0.24647765, 0.5601978 , 0.63844067, 0.2829074 ,\n",
       "        0.3132367 , 0.46921122, 0.31044945],\n",
       "       [0.73585093, 0.2825483 , 0.4757326 , 0.4380112 , 0.3935188 ,\n",
       "        0.42527813, 0.6249677 , 0.37325308],\n",
       "       [0.80700463, 0.38792425, 0.37497443, 0.2862118 , 0.5519993 ,\n",
       "        0.43066475, 0.53866655, 0.4209582 ],\n",
       "       [0.9013622 , 0.17944297, 0.44746795, 0.17873667, 0.23781678,\n",
       "        0.47783014, 0.32212162, 0.4660106 ],\n",
       "       [0.9782853 , 0.18391372, 0.42961827, 0.23444632, 0.27385426,\n",
       "        0.42646936, 0.32081327, 0.39670476]], dtype=float32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_small_pca\n",
    "#np.array([y_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "427eccbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = np.concatenate((x_train_small_pca,np.array([y_train]).T),axis=1)\n",
    "train = train[train[:,-1].argsort()]\n",
    "x_train_small_pca = train[:,0:8]\n",
    "y_train = train[:,-1]\n",
    "\n",
    "test = np.concatenate((x_test_small_pca,np.array([y_test]).T),axis=1)\n",
    "test = test[test[:,-1].argsort()]\n",
    "x_test_small_pca = test[:,0:8]\n",
    "y_test = test[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a1f34b9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1.], dtype=float32)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e8940054",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import ZZFeatureMap\n",
    "from qiskit.primitives import Sampler\n",
    "from qiskit_algorithms.state_fidelities import ComputeUncompute\n",
    "from qiskit_machine_learning.kernels import FidelityQuantumKernel\n",
    "\n",
    "mnist_feature_map = ZZFeatureMap(feature_dimension=n_components_classic, reps=2, entanglement=\"linear\") #this is now 8D\n",
    "\n",
    "sampler = Sampler()\n",
    "\n",
    "fidelity = ComputeUncompute(sampler=sampler)\n",
    "\n",
    "mnist_kernel = FidelityQuantumKernel(fidelity=fidelity, feature_map=mnist_feature_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7a0791b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ndef get_one_hot_encoding(y): #this converts the labels 0,1 to vectors |0>,|1> (maybe transpose)\\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\\ny_train_1h = get_one_hot_encoding(y_train)\\ny_test_1h = get_one_hot_encoding(y_test)\\n\\nprint(f\"Label {y_train[2]} converted to {y_train_1h[2]}\")\\nprint(f\"Label {y_train[0]} converted to {y_train_1h[0]}\")'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I don't think I need onehot for this?\n",
    "'''\n",
    "def get_one_hot_encoding(y): #this converts the labels 0,1 to vectors |0>,|1> (maybe transpose)\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": 55,
   "id": "cfd183b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Callable kernel classification test score: 0.85\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "mnist_svc = SVC(kernel=mnist_kernel.evaluate)\n",
    "\n",
    "ker = mnist_svc.fit(x_train_small_pca, y_train) #trying to save the fit\n",
    "\n",
    "mnist_score_callable_function = mnist_svc.score(x_test_small_pca, y_test)\n",
    "\n",
    "print(f\"Callable kernel classification test score: {mnist_score_callable_function}\") #works fairly good for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c4dbac46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_svc.score(x_train_small_pca, y_train) #seems to work perfectly for training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ef4aad1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = ker.predict(x_train_small_pca) #get the kernal method predictions for training data\n",
    "y_test_pred = ker.predict(x_test_small_pca) #get the kernal method prediction for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8edded42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "print(y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8d82299a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True  True  True  True False\n",
      " False  True  True  True  True False  True  True]\n"
     ]
    }
   ],
   "source": [
    "#corret answer mask\n",
    "#not one hot (y_train not _1h and no need for axis=1)\n",
    "train_pred_acc = y_train_pred == y_train\n",
    "test_pred_acc = y_test_pred == y_test\n",
    "print(test_pred_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "80c3b3d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split x data by whether the trained algo will give correct answer\n",
    "#not one hot this time\n",
    "x_all = np.concatenate((x_train_small_pca, x_test_small_pca))\n",
    "y_all= np.concatenate((y_train, y_test))\n",
    "y_pred_acc_all = np.concatenate((train_pred_acc, test_pred_acc))\n",
    "\n",
    "x_b = x_all[y_all == 0]\n",
    "x_b_good = x_b[(y_pred_acc_all[y_all == 0])]\n",
    "x_b_bad = x_b[np.logical_not(y_pred_acc_all[y_all == 0])]\n",
    "\n",
    "x_r = x_all[y_all == 1]\n",
    "x_r_good = x_r[(y_pred_acc_all[y_all == 1])]\n",
    "x_r_bad = x_r[np.logical_not(y_pred_acc_all[y_all == 1])]\n",
    "#print(y_pred_acc_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "45b342b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 8)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_b_good.shape #good 0's, all x data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "11e9ffd6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.73806244, 0.25824726, 0.3864904 , 0.2483016 , 0.40469873,\n",
       "       0.48181748, 0.53265846, 0.46933827], dtype=float32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_b_good[0] #the 8 parameters for this data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f0b177e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the font size \n",
    "\n",
    "\n",
    "plt.rcParams.update({'font.size': 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "aea70b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e1619297c0>"
      ]
     },
     "execution_count": 76,
     "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": [
    "#plot whether the algo gives the correct outcome for each x, by the first two variables in x\n",
    "plt.scatter(x_b_good[:,0], x_b_good[:,1], c='b', marker=\".\", label=\"Good class 0\", s=64)\n",
    "plt.scatter(x_b_bad[:,0], x_b_bad[:,1], c='b', marker=\"x\", label=\"Bad class 0\", s=64)\n",
    "plt.scatter(x_r_good[:,0], x_r_good[:,1], c='r', marker=\".\", label=\"Good class 1\", s=64)\n",
    "plt.scatter(x_r_bad[:,0], x_r_bad[:,1], c='r', marker=\"x\", label=\"Bad class 1\", s=64)\n",
    "\n",
    "plt.xlabel(\"$x_0$\")\n",
    "plt.ylabel(\"$x_1$\")\n",
    "\n",
    "plt.ylim(0, 1)\n",
    "plt.xlim(0,1)\n",
    "\n",
    "plt.title(\"MNIST Dataset Classification\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "18d851a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e0d2f32070>"
      ]
     },
     "execution_count": 64,
     "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": [
    "#plot whether the algo gives the correct outcome for each x, by 3rd and 4th variable in x\n",
    "plt.scatter(x_b_good[:,2], x_b_good[:,3], c='b', marker=\".\", label=\"Good class 0\")\n",
    "plt.scatter(x_b_bad[:,2], x_b_bad[:,3], c='b', marker=\"x\", label=\"Bad class 0\")\n",
    "plt.scatter(x_r_good[:,2], x_r_good[:,3], c='r', marker=\".\", label=\"Good class 1\")\n",
    "plt.scatter(x_r_bad[:,2], x_r_bad[:,3], c='r', marker=\"x\", label=\"Bad class 1\")\n",
    "\n",
    "plt.title(\"Mnist dataset classification\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4dd7712",
   "metadata": {},
   "source": [
    "# Pre Computed Kernal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a989a274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pre computing the kernal matrices, to then use later\n",
    "#it seems like the data is super diagonal, so this is a good method to use\n",
    "mnist_matrix_train = mnist_kernel.evaluate(x_vec=x_train_small_pca)\n",
    "mnist_matrix_test = mnist_kernel.evaluate(x_vec=x_test_small_pca, y_vec=x_train_small_pca)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "axs[0].imshow(\n",
    "    np.asmatrix(mnist_matrix_train), interpolation=\"nearest\", origin=\"upper\", cmap=\"Blues\"\n",
    ")\n",
    "axs[0].set_title(\"MNIST Training Kernel matrix\")\n",
    "\n",
    "axs[1].imshow(np.asmatrix(mnist_matrix_test), interpolation=\"nearest\", origin=\"upper\", cmap=\"Reds\")\n",
    "axs[1].set_title(\"Ad hoc testing kernel matrix\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "95ac2fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "axs[0].imshow(\n",
    "    np.asmatrix(mnist_matrix_train), interpolation=\"nearest\", origin=\"upper\", cmap=\"Blues\"\n",
    ")\n",
    "axs[0].set_title(\"MNIST Training Kernel Matrix\")\n",
    "\n",
    "axs[1].imshow(np.asmatrix(mnist_matrix_test), interpolation=\"nearest\", origin=\"upper\", cmap=\"Reds\")\n",
    "axs[1].set_title(\"MNIST Testing Kernel Matrix\")\n",
    "\n",
    "axs[0].set_ylim(80,0)\n",
    "axs[0].set_xlim(0,80)\n",
    "\n",
    "axs[1].set_ylim(20, 0)\n",
    "axs[1].set_xlim(0,80)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4b69adef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precomputed kernel classification test score: 0.85\n"
     ]
    }
   ],
   "source": [
    "mnist_svc = SVC(kernel=\"precomputed\")\n",
    "\n",
    "mnist_svc.fit(mnist_matrix_train, y_train)\n",
    "\n",
    "mnist_score_precomputed_kernel = mnist_svc.score(mnist_matrix_test, y_test)\n",
    "\n",
    "print(f\"Precomputed kernel classification test score: {mnist_score_precomputed_kernel}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf1d8f44",
   "metadata": {},
   "source": [
    "# QSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e1dd9631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QSVC classification test score: 0.85\n"
     ]
    }
   ],
   "source": [
    "from qiskit_machine_learning.algorithms import QSVC\n",
    "\n",
    "qsvc = QSVC(quantum_kernel=mnist_kernel) #use quantum kernal instead\n",
    "\n",
    "qker = qsvc.fit(x_train_small_pca, y_train)\n",
    "\n",
    "qsvc_score = qsvc.score(x_test_small_pca, y_test)\n",
    "\n",
    "print(f\"QSVC classification test score: {qsvc_score}\")\n",
    "\n",
    "y_train_qpred = qker.predict(x_train_small_pca) #get the qkernal method predictions for training data\n",
    "y_test_qpred = qker.predict(x_test_small_pca) #get the qkernal method prediction for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1a8193ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True  True  True  True False\n",
      " False  True  True  True  True False  True  True]\n"
     ]
    }
   ],
   "source": [
    "#corret answer mask\n",
    "#not one hot (y_train not _1h and no need for axis=1)\n",
    "train_qpred_acc = y_train_qpred == y_train\n",
    "test_qpred_acc = y_test_qpred == y_test\n",
    "print(test_pred_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ea3a9471",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split x data by whether the trained algo will give correct answer\n",
    "#not one hot this time\n",
    "x_all = np.concatenate((x_train_small_pca, x_test_small_pca))\n",
    "y_all= np.concatenate((y_train, y_test))\n",
    "y_qpred_acc_all = np.concatenate((train_qpred_acc, test_qpred_acc))\n",
    "\n",
    "x_b = x_all[y_all == 0]\n",
    "x_b_good = x_b[(y_qpred_acc_all[y_all == 0])]\n",
    "x_b_bad = x_b[np.logical_not(y_qpred_acc_all[y_all == 0])]\n",
    "\n",
    "x_r = x_all[y_all == 1]\n",
    "x_r_good = x_r[(y_qpred_acc_all[y_all == 1])]\n",
    "x_r_bad = x_r[np.logical_not(y_qpred_acc_all[y_all == 1])]\n",
    "#print(y_pred_acc_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "63810383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e1630276d0>"
      ]
     },
     "execution_count": 89,
     "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": [
    "#plot whether the algo gives the correct outcome for each x, by the first two variables in x\n",
    "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(\"Mnist dataset classification\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8e5e3264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e15f1c3b50>"
      ]
     },
     "execution_count": 90,
     "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": [
    "#plot whether the algo gives the correct outcome for each x, by 3rd and 4th variable in x\n",
    "plt.scatter(x_b_good[:,2], x_b_good[:,3], c='b', marker=\".\", label=\"Good class 0\")\n",
    "plt.scatter(x_b_bad[:,2], x_b_bad[:,3], c='b', marker=\"x\", label=\"Bad class 0\")\n",
    "plt.scatter(x_r_good[:,2], x_r_good[:,3], c='r', marker=\".\", label=\"Good class 1\")\n",
    "plt.scatter(x_r_bad[:,2], x_r_bad[:,3], c='r', marker=\"x\", label=\"Bad class 1\")\n",
    "\n",
    "plt.title(\"Mnist dataset classification\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48adc40c",
   "metadata": {},
   "source": [
    "# Quantum Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b3aec756",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is unsupervised machine learning, so we don't actually need the y data\n",
    "cluster_feature_map = ZZFeatureMap(feature_dimension=n_components_classic, reps=2, entanglement=\"linear\")\n",
    "\n",
    "cluster_kernel = FidelityQuantumKernel(feature_map=cluster_feature_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3d04cd78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'from sklearn.svm import SVC\\n\\nmnist_svc = SVC(kernel=mnist_kernel.evaluate)\\n\\nker = mnist_svc.fit(x_train_small_pca, y_train) #trying to save the fit\\n\\nmnist_score_callable_function = mnist_svc.score(x_test_small_pca, y_test)\\n\\nprint(f\"Callable kernel classification test score: {mnist_score_callable_function}\") #works fairly good for test data'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''from sklearn.svm import SVC\n",
    "\n",
    "mnist_svc = SVC(kernel=mnist_kernel.evaluate)\n",
    "\n",
    "ker = mnist_svc.fit(x_train_small_pca, y_train) #trying to save the fit\n",
    "\n",
    "mnist_score_callable_function = mnist_svc.score(x_test_small_pca, y_test)\n",
    "\n",
    "print(f\"Callable kernel classification test score: {mnist_score_callable_function}\") #works fairly good for test data'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "69cfdcb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_matrix = cluster_kernel.evaluate(x_vec=x_train_small_pca)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.imshow(np.asmatrix(cluster_matrix), interpolation=\"nearest\", origin=\"upper\", cmap=\"Greens\")\n",
    "plt.title(\"MNIST clustering kernel matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "79159449",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cluster_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a0b5089a",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'split'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_11820\\540193378.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mcluster_spectral\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSpectralClustering\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maffinity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"precomputed\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mcluster_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcluster_spectral\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcluster_matrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m#cluster_score = normalized_mutual_info_score(cluster_labels, y_train)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_spectral.py\u001b[0m in \u001b[0;36mfit_predict\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    789\u001b[0m             \u001b[0mCluster\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    790\u001b[0m         \"\"\"\n\u001b[1;32m--> 791\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    792\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    793\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_more_tags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mfit_predict\u001b[1;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[0;32m    882\u001b[0m         \u001b[1;31m# non-optimized default implementation; override when a better\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    883\u001b[0m         \u001b[1;31m# method is possible for a given clustering algorithm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 884\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    885\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    886\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1349\u001b[0m                 )\n\u001b[0;32m   1350\u001b[0m             ):\n\u001b[1;32m-> 1351\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1352\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1353\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_spectral.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    753\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    754\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massign_labels\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"kmeans\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 755\u001b[1;33m             _, self.labels_, _ = k_means(\n\u001b[0m\u001b[0;32m    756\u001b[0m                 \u001b[0mmaps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    757\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\_param_validation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    184\u001b[0m             \u001b[0mglobal_skip_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_config\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"skip_parameter_validation\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m             \u001b[0mfunc_sig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36mk_means\u001b[1;34m(X, n_clusters, sample_weight, init, n_init, max_iter, verbose, tol, random_state, copy_x, algorithm, return_n_iter)\u001b[0m\n\u001b[0;32m    441\u001b[0m     \u001b[1;36m16.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m     \"\"\"\n\u001b[1;32m--> 443\u001b[1;33m     est = KMeans(\n\u001b[0m\u001b[0;32m    444\u001b[0m         \u001b[0mn_clusters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[0minit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minit\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1349\u001b[0m                 )\n\u001b[0;32m   1350\u001b[0m             ):\n\u001b[1;32m-> 1351\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1352\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1353\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1517\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1518\u001b[0m             \u001b[0mkmeans_single\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_kmeans_single_lloyd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1519\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_mkl_vcomp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1520\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1521\u001b[0m         \u001b[0mbest_inertia\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbest_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36m_check_mkl_vcomp\u001b[1;34m(self, X, n_samples)\u001b[0m\n\u001b[0;32m    927\u001b[0m         \u001b[0mn_active_threads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mCHUNK_SIZE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mn_active_threads\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_threads\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 929\u001b[1;33m             \u001b[0mmodules\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mthreadpool_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    930\u001b[0m             \u001b[0mhas_vcomp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"vcomp\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"prefix\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    931\u001b[0m             has_mkl = (\"mkl\", \"intel\") in [\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36mthreadpool_info\u001b[1;34m()\u001b[0m\n\u001b[0;32m    103\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcontroller\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 105\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mthreadpoolctl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthreadpool_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36mthreadpool_info\u001b[1;34m()\u001b[0m\n\u001b[0;32m    122\u001b[0m     \u001b[0mIn\u001b[0m \u001b[0maddition\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meach\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0mmay\u001b[0m \u001b[0mcontain\u001b[0m \u001b[0minternal_api\u001b[0m \u001b[0mspecific\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m     \"\"\"\n\u001b[1;32m--> 124\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_ThreadpoolInfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_ALL_USER_APIS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtodicts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    125\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, user_api, prefixes, modules)\u001b[0m\n\u001b[0;32m    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 340\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_modules\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    341\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_if_incompatible_openmp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_load_modules\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    371\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_dyld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    372\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplatform\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"win32\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 373\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_enum_process_module_ex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    374\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    375\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_dl_iterate_phdr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_find_modules_with_enum_process_module_ex\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m                 \u001b[1;31m# Store the module if it is supported and selected\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 485\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_module_from_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    486\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0mkernel_32\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCloseHandle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh_process\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_make_module_from_path\u001b[1;34m(self, filepath)\u001b[0m\n\u001b[0;32m    513\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mprefix\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprefixes\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0muser_api\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mmodule_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mglobals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodule_class\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m                 \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minternal_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath, prefix, user_api, internal_api)\u001b[0m\n\u001b[0;32m    604\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minternal_api\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minternal_api\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dynlib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCDLL\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_RTLD_NOLOAD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 606\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_version\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    607\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_threads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_num_threads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    608\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_extra_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36mget_version\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    644\u001b[0m                              lambda: None)\n\u001b[0;32m    645\u001b[0m         \u001b[0mget_config\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_char_p\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[0mconfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_config\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mb\"OpenBLAS\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"utf-8\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'split'"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import SpectralClustering\n",
    "from sklearn.metrics import normalized_mutual_info_score\n",
    "\n",
    "cluster_spectral = SpectralClustering(2, affinity=\"precomputed\")\n",
    "\n",
    "cluster_labels = cluster_spectral.fit_predict(cluster_matrix)\n",
    "\n",
    "#cluster_score = normalized_mutual_info_score(cluster_labels, y_train)\n",
    "\n",
    "#print(f\"Clustering score: {cluster_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2780f91",
   "metadata": {},
   "source": [
    "# clustering example (also doesn't work)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb7c2ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.datasets import ad_hoc_data\n",
    "adhoc_dimension = 2\n",
    "train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(\n",
    "    training_size=25,\n",
    "    test_size=0,\n",
    "    n=adhoc_dimension,\n",
    "    gap=0.6,\n",
    "    plot_data=False,\n",
    "    one_hot=False,\n",
    "    include_sample_total=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a6d1c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# have to redefine y_train if execute this\n",
    "'''def plot_features(ax, features, labels, class_label, marker, face, edge, label):\n",
    "    # A train plot\n",
    "    ax.scatter(\n",
    "        # x coordinate of labels where class is class_label\n",
    "        features[np.where(labels[:] == class_label), 0],\n",
    "        # y coordinate of labels where class is class_label\n",
    "        features[np.where(labels[:] == class_label), 1],\n",
    "        marker=marker,\n",
    "        facecolors=face,\n",
    "        edgecolors=edge,\n",
    "        label=label,\n",
    "    )\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.ylim(0, 2 * np.pi)\n",
    "plt.xlim(0, 2 * np.pi)\n",
    "plt.imshow(\n",
    "    np.asmatrix(adhoc_total).T,\n",
    "    interpolation=\"nearest\",\n",
    "    origin=\"lower\",\n",
    "    cmap=\"RdBu\",\n",
    "    extent=[0, 2 * np.pi, 0, 2 * np.pi],\n",
    ")\n",
    "\n",
    "# A label plot\n",
    "plot_features(plt, train_features, train_labels, 0, \"s\", \"w\", \"b\", \"B\")\n",
    "\n",
    "# B label plot\n",
    "plot_features(plt, train_features, train_labels, 1, \"o\", \"w\", \"r\", \"B\")\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\", borderaxespad=0.0)\n",
    "plt.title(\"Ad hoc dataset for clustering\")\n",
    "\n",
    "plt.show()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c10d6fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "adhoc_feature_map = ZZFeatureMap(feature_dimension=adhoc_dimension, reps=2, entanglement=\"linear\")\n",
    "\n",
    "adhoc_kernel = FidelityQuantumKernel(feature_map=adhoc_feature_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67a31290",
   "metadata": {},
   "outputs": [],
   "source": [
    "adhoc_matrix = adhoc_kernel.evaluate(x_vec=train_features)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.imshow(np.asmatrix(adhoc_matrix), interpolation=\"nearest\", origin=\"upper\", cmap=\"Greens\")\n",
    "plt.title(\"Ad hoc clustering kernel matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e41ef5c0",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'split'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_6308\\1095581931.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0madhoc_spectral\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSpectralClustering\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maffinity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"precomputed\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mcluster_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madhoc_spectral\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madhoc_matrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mcluster_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnormalized_mutual_info_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcluster_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_spectral.py\u001b[0m in \u001b[0;36mfit_predict\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    789\u001b[0m             \u001b[0mCluster\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    790\u001b[0m         \"\"\"\n\u001b[1;32m--> 791\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    792\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    793\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_more_tags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mfit_predict\u001b[1;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[0;32m    882\u001b[0m         \u001b[1;31m# non-optimized default implementation; override when a better\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    883\u001b[0m         \u001b[1;31m# method is possible for a given clustering algorithm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 884\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    885\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    886\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1349\u001b[0m                 )\n\u001b[0;32m   1350\u001b[0m             ):\n\u001b[1;32m-> 1351\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1352\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1353\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_spectral.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    753\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    754\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massign_labels\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"kmeans\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 755\u001b[1;33m             _, self.labels_, _ = k_means(\n\u001b[0m\u001b[0;32m    756\u001b[0m                 \u001b[0mmaps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    757\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\_param_validation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    184\u001b[0m             \u001b[0mglobal_skip_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_config\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"skip_parameter_validation\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m             \u001b[0mfunc_sig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36mk_means\u001b[1;34m(X, n_clusters, sample_weight, init, n_init, max_iter, verbose, tol, random_state, copy_x, algorithm, return_n_iter)\u001b[0m\n\u001b[0;32m    441\u001b[0m     \u001b[1;36m16.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m     \"\"\"\n\u001b[1;32m--> 443\u001b[1;33m     est = KMeans(\n\u001b[0m\u001b[0;32m    444\u001b[0m         \u001b[0mn_clusters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    445\u001b[0m         \u001b[0minit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minit\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1349\u001b[0m                 )\n\u001b[0;32m   1350\u001b[0m             ):\n\u001b[1;32m-> 1351\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1352\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1353\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1517\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1518\u001b[0m             \u001b[0mkmeans_single\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_kmeans_single_lloyd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1519\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_mkl_vcomp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1520\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1521\u001b[0m         \u001b[0mbest_inertia\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbest_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py\u001b[0m in \u001b[0;36m_check_mkl_vcomp\u001b[1;34m(self, X, n_samples)\u001b[0m\n\u001b[0;32m    927\u001b[0m         \u001b[0mn_active_threads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mCHUNK_SIZE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mn_active_threads\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_threads\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 929\u001b[1;33m             \u001b[0mmodules\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mthreadpool_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    930\u001b[0m             \u001b[0mhas_vcomp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"vcomp\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"prefix\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    931\u001b[0m             has_mkl = (\"mkl\", \"intel\") in [\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36mthreadpool_info\u001b[1;34m()\u001b[0m\n\u001b[0;32m    103\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcontroller\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 105\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mthreadpoolctl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthreadpool_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36mthreadpool_info\u001b[1;34m()\u001b[0m\n\u001b[0;32m    122\u001b[0m     \u001b[0mIn\u001b[0m \u001b[0maddition\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meach\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0mmay\u001b[0m \u001b[0mcontain\u001b[0m \u001b[0minternal_api\u001b[0m \u001b[0mspecific\u001b[0m \u001b[0mentries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m     \"\"\"\n\u001b[1;32m--> 124\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_ThreadpoolInfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_ALL_USER_APIS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtodicts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    125\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, user_api, prefixes, modules)\u001b[0m\n\u001b[0;32m    338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 340\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_modules\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    341\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_if_incompatible_openmp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_load_modules\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    371\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_dyld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    372\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplatform\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"win32\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 373\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_enum_process_module_ex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    374\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    375\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_modules_with_dl_iterate_phdr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_find_modules_with_enum_process_module_ex\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m                 \u001b[1;31m# Store the module if it is supported and selected\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 485\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_module_from_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    486\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    487\u001b[0m             \u001b[0mkernel_32\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCloseHandle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh_process\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m_make_module_from_path\u001b[1;34m(self, filepath)\u001b[0m\n\u001b[0;32m    513\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mprefix\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprefixes\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0muser_api\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muser_api\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[0mmodule_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mglobals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodule_class\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m                 \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_api\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minternal_api\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath, prefix, user_api, internal_api)\u001b[0m\n\u001b[0;32m    604\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minternal_api\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minternal_api\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    605\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dynlib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCDLL\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_RTLD_NOLOAD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 606\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_version\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    607\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_threads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_num_threads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    608\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_extra_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\threadpoolctl.py\u001b[0m in \u001b[0;36mget_version\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    644\u001b[0m                              lambda: None)\n\u001b[0;32m    645\u001b[0m         \u001b[0mget_config\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_char_p\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[0mconfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_config\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mb\"OpenBLAS\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"utf-8\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'split'"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import SpectralClustering\n",
    "from sklearn.metrics import normalized_mutual_info_score\n",
    "\n",
    "adhoc_spectral = SpectralClustering(2, affinity=\"precomputed\")\n",
    "\n",
    "cluster_labels = adhoc_spectral.fit_predict(adhoc_matrix)\n",
    "\n",
    "cluster_score = normalized_mutual_info_score(cluster_labels, train_labels)\n",
    "\n",
    "print(f\"Clustering score: {cluster_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d728ae6",
   "metadata": {},
   "source": [
    "# Quantum Kernal Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f6500919",
   "metadata": {},
   "outputs": [],
   "source": [
    "# External imports\n",
    "from pylab import cm\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Qiskit imports\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.circuit import ParameterVector\n",
    "from qiskit.visualization import circuit_drawer\n",
    "from qiskit.circuit.library import ZZFeatureMap\n",
    "from qiskit_algorithms.optimizers import SPSA\n",
    "from qiskit_machine_learning.kernels import TrainableFidelityQuantumKernel\n",
    "from qiskit_machine_learning.kernels.algorithms import QuantumKernelTrainer\n",
    "from qiskit_machine_learning.algorithms import QSVC\n",
    "from qiskit_machine_learning.datasets import ad_hoc_data\n",
    "\n",
    "\n",
    "class QKTCallback:\n",
    "    \"\"\"Callback wrapper class.\"\"\"\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        self._data = [[] for i in range(5)]\n",
    "\n",
    "    def callback(self, x0, x1=None, x2=None, x3=None, x4=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            x0: number of function evaluations\n",
    "            x1: the parameters\n",
    "            x2: the function value\n",
    "            x3: the stepsize\n",
    "            x4: whether the step was accepted\n",
    "        \"\"\"\n",
    "        self._data[0].append(x0)\n",
    "        self._data[1].append(x1)\n",
    "        self._data[2].append(x2)\n",
    "        self._data[3].append(x3)\n",
    "        self._data[4].append(x4)\n",
    "        print(self._data[2])\n",
    "\n",
    "    def get_callback_data(self):\n",
    "        return self._data\n",
    "\n",
    "    def clear_callback_data(self):\n",
    "        self._data = [[] for i in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2ce6b343",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ┌──────────┐┌────────────────────────────────────────────────────────┐\n",
      "q_0: ┤ Ry(θ[0]) ├┤0                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_1: ┤ Ry(θ[0]) ├┤1                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_2: ┤ Ry(θ[0]) ├┤2                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_3: ┤ Ry(θ[0]) ├┤3                                                       ├\n",
      "     ├──────────┤│  ZZFeatureMap(x[0],x[1],x[2],x[3],x[4],x[5],x[6],x[7]) │\n",
      "q_4: ┤ Ry(θ[0]) ├┤4                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_5: ┤ Ry(θ[0]) ├┤5                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_6: ┤ Ry(θ[0]) ├┤6                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_7: ┤ Ry(θ[0]) ├┤7                                                       ├\n",
      "     └──────────┘└────────────────────────────────────────────────────────┘\n",
      "Trainable parameters: θ, ['θ[0]']\n"
     ]
    }
   ],
   "source": [
    "# Create a rotational layer to train. We will rotate each qubit the same amount.\n",
    "training_params = ParameterVector(\"θ\", 1)\n",
    "fm0 = QuantumCircuit(n_components_classic)\n",
    "fm0.ry(training_params[0], range(n_components_classic))\n",
    "\n",
    "# Use ZZFeatureMap to represent input data\n",
    "fm1 = ZZFeatureMap(n_components_classic)\n",
    "\n",
    "# Create the feature map, composed of our two circuits\n",
    "fm = fm0.compose(fm1)\n",
    "\n",
    "print(circuit_drawer(fm))\n",
    "print(f\"Trainable parameters: {training_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8ed8af34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate quantum kernel\n",
    "quant_kernel = TrainableFidelityQuantumKernel(feature_map=fm, training_parameters=training_params)\n",
    "\n",
    "# Set up the optimizer\n",
    "cb_qkt = QKTCallback()\n",
    "spsa_opt = SPSA(maxiter=10, callback=cb_qkt.callback, learning_rate=0.05, perturbation=0.05)\n",
    "\n",
    "# Instantiate a quantum kernel trainer.\n",
    "qkt = QuantumKernelTrainer(\n",
    "    quantum_kernel=quant_kernel, loss=\"svc_loss\", optimizer=spsa_opt, initial_point=[np.pi / 2]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c3072d17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.830518873432842]\n",
      "[13.830518873432842, 13.901010474564977]\n",
      "[13.830518873432842, 13.901010474564977, 14.120386783082843]\n",
      "[13.830518873432842, 13.901010474564977, 14.120386783082843, 14.90444583877735]\n",
      "[13.830518873432842, 13.901010474564977, 14.120386783082843, 14.90444583877735, 17.6231004596791]\n",
      "[13.830518873432842, 13.901010474564977, 14.120386783082843, 14.90444583877735, 17.6231004596791, 26.461406975440894]\n",
      "[13.830518873432842, 13.901010474564977, 14.120386783082843, 14.90444583877735, 17.6231004596791, 26.461406975440894, 13.937909919591434]\n",
      "[13.830518873432842, 13.901010474564977, 14.120386783082843, 14.90444583877735, 17.6231004596791, 26.461406975440894, 13.937909919591434, 14.262308651737937]\n",
      "[13.830518873432842, 13.901010474564977, 14.120386783082843, 14.90444583877735, 17.6231004596791, 26.461406975440894, 13.937909919591434, 14.262308651737937, 15.315337256410633]\n",
      "[13.830518873432842, 13.901010474564977, 14.120386783082843, 14.90444583877735, 17.6231004596791, 26.461406975440894, 13.937909919591434, 14.262308651737937, 15.315337256410633, 19.662718833191615]\n",
      "{   'optimal_circuit': None,\n",
      "    'optimal_parameters': {ParameterVectorElement(θ[0]): 1.1336969141381088},\n",
      "    'optimal_point': array([1.13369691]),\n",
      "    'optimal_value': 19.662718833191615,\n",
      "    'optimizer_evals': 30,\n",
      "    'optimizer_result': None,\n",
      "    'optimizer_time': None,\n",
      "    'quantum_kernel': <qiskit_machine_learning.kernels.trainable_fidelity_quantum_kernel.TrainableFidelityQuantumKernel object at 0x000001E168E8EA30>}\n"
     ]
    }
   ],
   "source": [
    "# Train the kernel using QKT directly\n",
    "qka_results = qkt.fit(x_train_small_pca, y_train)\n",
    "optimized_kernel = qka_results.quantum_kernel\n",
    "print(qka_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "65248a1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "17925dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy test: 0.9\n"
     ]
    }
   ],
   "source": [
    "# Use QSVC for classification\n",
    "qsvc = QSVC(quantum_kernel=optimized_kernel)\n",
    "\n",
    "# Fit the QSVC\n",
    "qsvc.fit(x_train_small_pca, y_train)\n",
    "\n",
    "# Predict the labels\n",
    "labels_test = qsvc.predict(x_test_small_pca)\n",
    "\n",
    "# Evalaute the test accuracy\n",
    "accuracy_test = metrics.balanced_accuracy_score(y_true=y_test, y_pred=labels_test)\n",
    "print(f\"accuracy test: {accuracy_test}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "860b6f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = cb_qkt.get_callback_data()  # callback data\n",
    "K = optimized_kernel.evaluate(x_train_small_pca)  # kernel matrix evaluated on the training samples\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 20\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot([i + 1 for i in range(len(plot_data[0]))], np.array(plot_data[2]), c=\"k\", marker=\"o\")\n",
    "ax[0].set_xlabel(\"Iterations\")\n",
    "ax[0].set_ylabel(\"Loss\")\n",
    "ax[1].imshow(K, cmap=matplotlib.colormaps[\"bwr\"])\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e28a5b2",
   "metadata": {},
   "source": [
    "# Try with 8 Training Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "98394012",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ┌──────────┐┌────────────────────────────────────────────────────────┐\n",
      "q_0: ┤ Ry(θ[0]) ├┤0                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_1: ┤ Ry(θ[1]) ├┤1                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_2: ┤ Ry(θ[2]) ├┤2                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_3: ┤ Ry(θ[3]) ├┤3                                                       ├\n",
      "     ├──────────┤│  ZZFeatureMap(x[0],x[1],x[2],x[3],x[4],x[5],x[6],x[7]) │\n",
      "q_4: ┤ Ry(θ[4]) ├┤4                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_5: ┤ Ry(θ[5]) ├┤5                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_6: ┤ Ry(θ[6]) ├┤6                                                       ├\n",
      "     ├──────────┤│                                                        │\n",
      "q_7: ┤ Ry(θ[7]) ├┤7                                                       ├\n",
      "     └──────────┘└────────────────────────────────────────────────────────┘\n",
      "Trainable parameters: θ, ['θ[0]', 'θ[1]', 'θ[2]', 'θ[3]', 'θ[4]', 'θ[5]', 'θ[6]', 'θ[7]']\n"
     ]
    }
   ],
   "source": [
    "# Create a rotational layer to train. We will rotate each qubit the same amount.\n",
    "training_params = ParameterVector(\"θ\", 8)\n",
    "fm0 = QuantumCircuit(n_components_classic)\n",
    "\n",
    "fm0.ry(training_params[0], 0)\n",
    "fm0.ry(training_params[1], 1)\n",
    "fm0.ry(training_params[2], 2)\n",
    "fm0.ry(training_params[3], 3)\n",
    "fm0.ry(training_params[4], 4)\n",
    "fm0.ry(training_params[5], 5)\n",
    "fm0.ry(training_params[6], 6)\n",
    "fm0.ry(training_params[7], 7)\n",
    "\n",
    "# Use ZZFeatureMap to represent input data\n",
    "fm1 = ZZFeatureMap(n_components_classic)\n",
    "\n",
    "# Create the feature map, composed of our two circuits\n",
    "fm = fm0.compose(fm1)\n",
    "\n",
    "print(circuit_drawer(fm))\n",
    "print(f\"Trainable parameters: {training_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c899cef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate quantum kernel\n",
    "quant_kernel = TrainableFidelityQuantumKernel(feature_map=fm, training_parameters=training_params)\n",
    "\n",
    "# Set up the optimizer\n",
    "cb_qkt = QKTCallback()\n",
    "spsa_opt = SPSA(maxiter=10, callback=cb_qkt.callback, learning_rate=0.05, perturbation=0.05)\n",
    "\n",
    "# Instantiate a quantum kernel trainer.\n",
    "qkt = QuantumKernelTrainer(\n",
    "    quantum_kernel=quant_kernel, loss=\"svc_loss\", optimizer=spsa_opt, initial_point=[np.pi / 2,np.pi / 2,np.pi / 2,np.pi / 2,np.pi / 2,np.pi / 2,np.pi / 2,np.pi / 2]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3d0705d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.810322388786826]\n",
      "[13.810322388786826, 13.81304416629004]\n"
     ]
    }
   ],
   "source": [
    "# Train the kernel using QKT directly\n",
    "qka_results = qkt.fit(x_train_small_pca, y_train)\n",
    "optimized_kernel = qka_results.quantum_kernel\n",
    "print(qka_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecacd76b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f79cc52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21fa60bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ff775da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "052edda3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad354fd6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d62faf3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e59be88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aad8ce98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a4e63247",
   "metadata": {},
   "source": [
    "#Train Example (works)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ea8cf886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7fZSuXbtiy5Yt+PDDDxEfH4/IyEh07tzZo2sxZcoULF++HCNHjsTs2bPRvn17FBQUYP78+Xj88cfRqVMnj7ZXrbHXrqHX49///jc2b96MkSNHol27dqisrLTfDar+/Dz66KMICwtD3759ER8fj7KyMuTm5iIqKsqeP+ONhhxjnz590KJFCzz22GN47rnn0KRJE6xcuRL79u2rs72uXbsCAF588UUMHz4cOp0O3bp1Q2hoaJ115bpuRAFJweTUoDB69GgRGhoqTp06Ve8699xzj9Dr9fYRKDt27BBpaWnCYDCIuLg48Ze//EW8+eabDR6N0qxZszrLn3vuuTpZ9r/++qt47LHHRHx8vNDr9aJ9+/YiKytLVFZWOqxntVrF//3f/4mUlBQRGhoqoqKiRO/evcWHH37o9vjffvttkZSUJEJDQ0WnTp3E4sWL64woEEKIxYsXi86dOwuDwSCuu+46kZubKxYtWlTnmI8dOyaGDh0qIiMjBQCH7bzwwgsiISFBGAwGkZycLN566606x71+/XoxfPhwce2114rQ0FARGxsrRowYIbZv3+7QnosXL4q//e1vonPnzvZj7tq1q5gyZYrDSKGioiLRt29fER4eLgC4HSnjbDSKEEIcP35c3HfffaJly5aiSZMmonPnzuKll14SVqvVvk71qIaXXnrJ5T5qasi1czYapSHXo7CwUNxxxx2iffv2wmAwiJYtW4r+/fuL/Px8+3aWLVsmBg4cKFq1aiVCQ0NF69atRWZmpti/f799HW9GozT0GL/44gvRu3dvER4eLmJiYsQjjzwivvrqKwFALFmyxL6e2WwWjzzyiIiJiRGSJDkcb+3RKEJ4f90AiOeee67OcqJAIwlRq0oPERERkQ8xZ4OIiIhkxWCDiIiIZMVgg4iIiGTFYIOIiIhkxWCDiIiIZMVgg4iIiGTl96JeVVVV+OmnnxAZGdngsstERBQYhBC4cOECWrdujZAQ3/29a7VacfnyZZ9tj9wLDQ1t8DX0e7Dx008/oW3btv7eLRERqcjJkyfRpk0br7cjhEBZWZnXc92Q50JCQtChQwenFXZr83uwERkZCQAoKSmx/6ykNkOm+WxbP2yc5/L1rDjvyzNXyy3b0+j3+vKY3ZHznHhzDjzl7bXzZ1td8fW1d3V91fJ5BwLnM1+bnJ8rub6bFy5cQFJSks9+/1cHGrGxsQgPD+cdcz+p7qUoLS1Fu3bt3J53vwcb1Q2KjIyE0Wj09+7rkHTuI7KGcnc8oT5MkfHm3PnymN2R85z48/Pj7bVTw2cd8P21d3Vcavm8A4Hzmfd0X96Q+7vpi6DAarXaA43qmabJf2JiYvDTTz/BYrGgSZMmLtflRGxEFFSEORawNq1/BV0lJMMp/zWIGq06RyM8PFzhlgSn6u4Tq9XKYKO2qD6TfLat81+84fL1KeHJPttXoNDKOdFKO6lhqr+rR45I6NbNRaBxhT55RqMDjtq/Y9z9nghEtb8//1dRLOv+2HWiDE/Oe9AFG0QUvC5csP27YgWQ7CSeLC4Gxo2D6zsfROQxBhtEFHSSk4EePZRuBZGyJEnCmjVrMHr0aNn3xaJeRERECvjiiy+g0+kwbNiwBq2fkJCAV155xWf7Ly0txfDhw322PVeC/s6GL/tT5eznl7vP01f82T/t737hQFD7+niaw6RU/kEgXeuabff2d4ZWzkvNdl5ClYItcVRScrVrzZnISCApSb79L168GE8++STefvttnDhxAu3atfN6m1arFZIkNajYVlxcnNf7ayje2SAioqBTUgJ06gTcdFP9j06dbOvJoby8HHl5eXj88ceRnp6OpUuXulx/wIABOH78OKZMmQJJkuzJmUuXLkXz5s2xfv16dOnSBQaDAcePH8eePXswZMgQXHPNNYiKikL//v3x1VdfOWxTkiSsXbsWAHDs2DFIkoTVq1dj4MCBCA8Px4033ojCwkKfHC+DDSIiCjo1k4X37q37WLHCcT1fe/fdd9G5c2d07twZ48aNw5IlSyCEqHf91atXo02bNpg9ezZKS0tRWlpqf62iogK5ubl4++238c033yA2NhYXLlzA+PHjsX37duzcuRNJSUkYMWIELrg5oOzsbEyfPh1FRUXo1KkT7r33XlgsFq+PN+i7UYgo+BTX09tQ33IKXEolCy9atAjjxo0DAAwbNgwXL17EZ599hsGDBztdPzo6GjqdDpGRkXW6Py5fvoz58+fjxhtvtC/7/e9/77DOwoUL0aJFC2zduhXp6en1tmv69OkYOXIkAOD555/HDTfcgCNHjuD6669v1HFWUyzYaDNkmr2qnz/7gbWSo+FLvqwtEsi0cj19KdhqQFRXyL7yO75+ukqf7dOfdTdqfobVmr9BwKFDh7B7926sXr0aAKDX63H33Xdj8eLF9QYbroSGhqJbt24Oy06dOoVnn30Wmzdvxs8//wyr1YqKigqcOHHC5bZqbic+Pt6+Lb8HGz/++COefvppfPzxx/jtt9/QqVMnLFq0CDfddJNXDSEikltiosD+/ZV1bo33f/CFq09YQZRktmjRIlgsFlx77bX2ZUIINGnSBGfPnkWLFi082l5YWFidAlsTJkzA6dOn8corr6B9+/YwGAzo3bs3Ll265HJbNSuBVm+zqsr7pF6Pgo2zZ8+ib9++GDhwID7++GPExsbiu+++Q/Pmzb1uCBGRPyQm1u0Xl8Jd/7VH5CsWiwXLly/HvHnzMHToUIfX7rrrLqxcuRJPPPGE0/eGhobCarU2aD/bt2/H/PnzMWLECAC2WXZ/+eUX7xrvBY+CjRdffBFt27bFkiVL7MsSEhJ83SbVUNNtdbUOcWPJdlKCWr8Pnqrdbl9+X7w9R3K2LZitX78eZ8+excMPP4yoqCiH18aMGYNFixbVG2wkJCRg27ZtuOeee2AwGHDNNdfUu5/ExET8v//3/9CzZ0+YTCb85S9/QVhYmE+PxRMejUbJz89Hz5498Yc//AGxsbHo3r073nrrLbnaRkREJKviYuCrr+o+5EoWXrRoEQYPHlwn0ABsdzaKiorqDFGtNnv2bBw7dgwdO3ZETEyMy/0sXrwYZ8+eRffu3XH//ffjqaeeQmxsrE+OoTE8urPx/fffY8GCBZg6dSpmzpyJ3bt346mnnoLBYMADDzzg9D1msxlms9n+3GQyeddiIiIiLzU0Wbh6PV/58MMP632tR48eLoe/pqWlYd++fQ7LJkyYgAkTJtRZt3v37tizZ4/DsjFjxjg8r7mvhISEOvtu3ry5y/Z4wqNgo6qqCj179sTcuXMB2A7mm2++wYIFC+oNNnJzc/H8889731IiIiIfSUoCDh9WtoJoMPEo2IiPj0eXLl0cliUnJ+ODDz6o9z1ZWVmYOnWq/bnJZELbtm3xw8Z5MBqNHjbXc8Haz8jhru4F62eD6qqZexSo3x3mcNTFQMJ/PAo2+vbti0OHDjksO3z4MNq3b1/vewwGAwwGQ+NaR0RERJrnUYLolClTsHPnTsydOxdHjhzBf/7zH7z55puYNCkw/xIgIiIi73l0Z+Pmm2/GmjVrkJWVhdmzZ6NDhw545ZVXMHbsWLnaR41w5IgEUeFi9kAWLSIiIj/yuIJoenq6y7rq/hYI/Ya+dOSIhG7dmgKY5XI9ffKMRgccrmprKHk91PRZ0GrdB2+o6fyTe77M4eC1J3c4EVuAqTmTYbKT739x8ZWhXtamfm0XEREFLwYbAUqpmQyJiIhqY7BBRH4lQSAJZhhhhQk6lMAAAcn9G4lIszQXbLBv0Lnq2gC2xNBZPt12sE1DTg3n6fcxFRXI1JsQbblaVfiM3oA8ixFFCPd18xql9uc9UOtu1OZNDkcg1uAIBgkJCZg8eTImT54s+748GvpKRNRYqajARJxGi2FDgMJCW4JRYSFa3DYYE3EaqahQuolEfjFhwgRIkmR/tGzZEsOGDcP+/ftdvm/AgAE+DQz27NmDP/7xjz7bnisMNohIdhIEMvUmID0d0rp1QFoaEBEBpKVBys8HRo5Ept4ECb6Zh4HII1YrsGUL8M47tn8bOI27N4YNG4bS0lKUlpbis88+g16v98lITyEELBZLg9aNiYlBeLh/7igy2AhQ/p7JkKiaFBKCTv3T0POeDHTqnwYpJARJMCPaYoaUnQ2E1Pq1ExICKTsb0RYzkmB2vlEiuaxeDSQmAgMHAvfdZ/s3MdG2XEYGgwFxcXGIi4tDamoqnn76aZw8eRKnT592uv6ECROwdetWvPrqq/Y7IseOHcOWLVsgSRI2bNiAnj17wmAwYPv27fjuu+9w++23o1WrVoiIiMDNN9+MTZs2OWwzISEBr7zyiv25JEl4++23cccddyA8PBxJSUnIz8/3yfFqImeD/X8e9J/qKgG4n8mwej1nmKOhLd7WS/Bk2+6k3nEbMudlI7rD1aJyZ46ewFfTngXWLANSUpy/8cpyI+T/i1LN1JT74MscjppMJhOWxMU1ul0+tXo1MGYMkJ5uu6uRkgIcPAjMnWtb/v77wJ13yt6MixcvYuXKlUhMTETLli2drvPqq6/i8OHDSElJwezZswHY7kwcO3YMAPDXv/4VL7/8Mq677jo0b94cP/zwA0aMGIGcnBw0bdoUy5Ytw6hRo3Do0CG0a1d/0cfnn38e//jHP/DSSy/h9ddfx9ixY3H8+HFER0d7dYyaCDao4STDKeiTZ7iuo8EKoiSD1Dtuw8T3FwDr1wP3Ztp+aaekoMXMmRj0/mJgjMm2LC2t7psPHgQAmKDzc6spaFmtwLRptkBj7dqrd9zS0mzPR48Gpk8Hbr8d0Pn+c7l+/XpEREQAAMrLyxEfH4/169cjpPadvyuioqIQGhqK8PBwxDkJ1mbPno0hQ4bYn7ds2RI33nij/XlOTg7WrFmD/Px8PPHEE/W2a8KECbj33nsBAHPnzsXrr7+O3bt3Y9iwYY06zmrsRglAkuEUpPAT9T8YaJCPSSEhyJyXDaxfD2n0aGDXLqC8HNi1y/Z8/XpYX54HMXcuUFXl+OaqKog5c3BGb0AJOGkj+cn27cCxY8DMmU679pCVBRw9altPBgMHDkRRURGKioqwa9cuDB06FMOHD8fx48cbtb2ePXs6PC8vL8df//pXdOnSBc2bN0dERAT+97//4cSJEy63061bN/vPzZo1Q2RkJE6d8v7/DAYbROS1pH63ILpDO0hz5wKiVpKnEJByc6G7rgNgMkFkZDiMRhEZGUBBAfIsRtbbIP8pLbX966Zrz76ejzVr1gyJiYlITEzELbfcgkWLFqG8vBxvvfVWo7dX01/+8hd88MEHmDNnDrZv346ioiJ07doVly5dcrmdJk2aODyXJAlVtf9AaARVdqMwR4O0xpP+7Np1G3ydI1Pz++Np/kZjv3vG+FjbD1e6Q+q4svyz+M7o8f4yRBcU2F86qzcgDzGqqbNRm7d1NwIlB8rVZ0OTcwHFx9v+ddO1Z19PZpIkISQkBL/99lu964SGhsLawJEy27dvx4QJE3DHHXcAsOWFVOd3KEGVwQYRaYup9Mpt1pQUWxdKbVf+Stxfeh4fWFo5VhC1sIIoKaBfPyAhwZYMWjNnA7B19eXmAh062NaTgdlsRllZGQDg7Nmz+Ne//oWLFy9i1KhR9b4nISEBu3btwrFjxxAREeEyaTMxMRGrV6/GqFGjIEkSnnnmGZ/coWgsdqMQkddKtu/GmaMnIGbOBKRagYMkQWRl4cz3x1GyfTcEJBxGU3yJZjiMpgw0SBk6HTBvni2hefRoh649XMkzwssvy5IcCgCffPIJ4uPjER8fj169emHPnj147733MGDAgHrfM336dOh0OnTp0gUxMTEu8y/+7//+Dy1atECfPn0watQo3Hbbbeih4IRZvLNBRF4TVVXImzYHE99fALF2LaTcXPtoFJGVBaSnI2/M4xAK/mVFVMedd9qGt06bBvTpc3V5hw6yDntdunQpli5d6vH7OnXqhMLCQodlCQkJELXzpK4s37x5s8OySZMcuwBrd6s42865c+c8bqczigUbWXE3I9QPN1Y86UtUU66Iu3b7cr4GX/Ypu2u3ms6xVnlyDt3VSvDl9ShaswELxzxuq7NR4xfi2e+PI2/M4yhas8Fn+/KWq++Pu++DnDkYWv1+yFnrRXZ33mkb3rp9uy0ZND7e1nUi0x2NYMU7G0TkM0VrNmDfuo1I6ncLjPGxMJWesnWd8I4GqZlOB7joviDvMdggIp8SVVU4vHWn0s0gIhXRfLChqdt1KqHkULxA6WbR6udOK+fX1zzpdgzWoa2+pOluFZIFR6MQERGRrBhsEBERkawYbBAREZGsNJGzIWd/n1b6sL0Z6qqlPmU1TbHtS74cqkye87bkuCtyl58PBDW/x5fAkUnBiHc2iIiISFYMNoiIiEhWDDaIiIgU8MUXX0Cn02HYsGENWj8hIQGvvPKKT9swYMAATJ482afbdEYVORve5GQESn+pr/NSlDoP7vrCPW1XzfOiZP6GmuoEeJPXomROjJw1VljXwXO+PEeBklvlb4sXL8aTTz6Jt99+GydOnEC7du2UbpJseGeDiIjIz8rLy5GXl4fHH38c6enpbidmGzBgAI4fP44pU6ZAkiRINWZX/uKLL3DrrbciLCwMbdu2xVNPPYXy8nL76/Pnz0dSUhKaNm2KVq1aYcyYMQCACRMmYOvWrXj11Vft26w9OZuvMNggIqKgNXYs0KNH3cfYsfLu991330Xnzp3RuXNnjBs3DkuWLHE662q11atXo02bNpg9ezZKS0tRWloKADhw4ABuu+023Hnnndi/fz/effdd/Pe//8UTTzwBAPjyyy/x1FNPYfbs2Th06BA++eQT3HrrrQCAV199Fb1798ajjz5q32bbtm1lOV5VdKMQkXZIEEiCGUZYYYIOJTBAQHL/RiIVKi4Gvv7a//tdtGgRxo0bBwAYNmwYLl68iM8++wyDBw92un50dDR0Oh0iIyMRFxdnX/7SSy/hvvvus+ddJCUl4bXXXkP//v2xYMECnDhxAs2aNUN6ejoiIyPRvn17dO/eHQAQFRWF0NBQhIeHO2xTDooFG7lle2A0Ghu0LmsUuOfPHA1vrodWcmwCpc/f3XF4msORigpk6k2Itpjty87oDcizGFGE8MY3tBFtUSu1fsbl/EwHypxH/nLo0CHs3r0bq1evBgDo9XrcfffdWLx4cb3BRn327t2LI0eOYOXKlfZlQghUVVXh6NGjGDJkCNq3b4/rrrsOw4YNw7Bhw3DHHXcgPNy776un2I1CRA2SigpMxGm0GDYEKCwELlwACgvR4rbBmIjTSEWF0k0k0oRFixbBYrHg2muvhV6vh16vx4IFC7B69WqcPXvWo21VVVVh4sSJKCoqsj/27duHkpISdOzYEZGRkfjqq6/wzjvvID4+Hs8++yxuvPFGnDt3Tp6DqweDDSJyS4JApt4EpKdDWrcOSEsDIiKAtDRI+fnAyJHI1Jsgof4+ZyICLBYLli9fjnnz5tUJENq3b+9wh6K20NBQWK1Wh2U9evTAN998g8TExDqP0NBQALY7J4MHD8Y//vEP7N+/H8eOHcPmzZvr3aYcGGwQkVtJMCPaYoaUnQ2E1Pq1ERICKTsb0RYzkmB2vgEiAgCsX78eZ8+excMPP4yUlBSHx5gxY7Bo0aJ635uQkIBt27bhxx9/xC+//AIAePrpp1FYWIhJkyahqKgIJSUlyM/Px5NPPmnf32uvvYaioiIcP34cy5cvR1VVFTp37mzf5q5du3Ds2DH88ssvqKqSp5y8KhJElZz3Q6k6Dp72nyrZ7xssOTO+7NP25znzR36JEVf+8klJcb7CleX29XzA1XF5+12Vc66U2tSaw+FPNa+lyWTCEpmTET2RXM9Hqb7l3lq0aBEGDx6MqKioOq/dddddmDt3Lr766iv06NGjzuuzZ8/GxIkT0bFjR5jNZggh0K1bN2zduhXZ2dno168fhBDo2LEj7r77bgBA8+bNsXr1asyaNQuVlZVISkrCO++8gxtuuAEAMH36dIwfPx5dunTBb7/9hqNHjyIhIcHnx62KYIOI1M0Ene2HgwdtXSi1HTzouB6RRrjotZDFhx9+WO9rPXr0cDn8NS0tDfv27auz/Oabb8ann37q9D2/+93vsGXLlnq32alTJxQWFtbfYB9hNwoRuVUCA87oDRA5OUDt26xVVRBz5uCM3oASGJRpIBGpGoMNInJLQEKexQgUFEBkZDiMRhEZGUBBAfIsRtbbICKnFOtGaTNkGiRdqMfvk7O/U+5x/oFSu8GX2J+tLq6+A0UIx0LEIHPDJkQXFNiXn9UbkIcYr+ts+KqdalfzM1/78y7ncXH+GFISczaIqMGKEI59ljDHCqIWVhAlItcYbBCRRwQkHEZTpZtBRBqiymCDt9KVFSxDXSlweNslUPN3jj8//+66EbXSXeTJORPWSzK2hNSKCaJEREQkKwYbREREJCtVdqMQadmRIxJERbv6V9BVQjKc8l+DiIgUpliw8cPGeQ2eYl6rOLRM3eQoTy7MsbAUvwBglsv19ckz7AGHmof/aiVnwJf8WcrcU768Hr4cCuvuM6umc0jKYDcKkS9ZbaM0VqwA9u6t+1ixwnE9Igo+EyZMgCRJ9kfLli0xbNgw7N+/3+X7BgwYgMmTJ/u8LaNHj/bpNp1hNwqRDJKTASfzKBERAQCGDRuGJUuWAADKysrwt7/9Denp6Thx4oTCLZMH72wQKUBUxkNUtLM9zLFKN4couF2+7Pq5DAwGA+Li4hAXF4fU1FQ8/fTTOHnyJE6fPu10/QkTJmDr1q149dVX7XdEjh07BgD49ttvMWLECERERKBVq1a4//777VPQA8D777+Prl27IiwsDC1btsTgwYNRXl6OWbNmYdmyZVi3bp19m64mbfMG72zUEAz90WqjpvwEf7KemOjwXCs5HP7kqp8/UM+JktdeznLmNY/DZDIhLs7PU626IgTwySfAnDm22YtTUoDsbCA9HZD8Uxn34sWLWLlyJRITE9GyZUun67z66qs4fPgwUlJSMHv2bABATEwMSktL0b9/fzz66KP45z//id9++w1PP/00MjMzsXnzZpSWluLee+/FP/7xD9xxxx24cOECtm/fDiEEpk+fjuLiYphMJvtdlujoaFmO0aNgY9asWXj++ecdlrVq1QplZWU+bRRRoFuxwtbVUlwMjBsH5nAQKeHyZVugcfvttqADAHbtsj1ftw4YNgxo0kSWXa9fvx4REREAgPLycsTHx2P9+vUICXHe4RAVFYXQ0FCEh4cjLi7OvnzBggXo0aMH5s6da1+2ePFitG3bFocPH8bFixdhsVhw5513on379gCArl272tcNCwuD2Wx22KYcPO5GueGGG1BaWmp/HDhwQI52EQW06pyOZN5MI1JOkya2OxrVgUY1IYC5c2ULNABg4MCBKCoqQlFREXbt2oWhQ4di+PDhOH78uEfb2bt3Lz7//HNERETYH9dffz0A4LvvvsONN96IQYMGoWvXrvjDH/6At956C2fPnpXjkFzyuBtFr9fLHgEpISQkBD3vyYCp9BRKtu+GqKpSukmkYcX13IGubzkRKeTgQc+W+0izZs2QmJhof37TTTchKioKb731FnJychq8naqqKowaNQovvvhindfi4+Oh0+mwceNGfPHFF/j000/x+uuvIzs7G7t27UKHDh18ciwN4XGwUVJSgtatW8NgMKBXr16YO3currvuOjna5hfLxk5G5rxsRHdoh0feeQ0AcOboCeRNm4OiNRs82hbragSv6j7pI0ckdOt2pWvEhcjIhm9bzhoFnuYEqKXuhj9zG5Ssu6Hk3ClBMyV9Soqt68TZcj+SJAkhISH47bff6l0nNDQUVqvVYVmPHj3wwQcfICEhAXq98//SJUlC37590bdvXzz77LNo37491qxZg6lTpzrdphw86kbp1asXli9fjg0bNuCtt95CWVkZ+vTpg19//bXe95jNZphMJoeHWoSEhGDi+wvQ4kARkJYGREQAaWlocaAIE99fgNQ7blO6iaQxiYkC+/dXQt9pFvSdZkHXbiEAx7obhw8DSUkKN5SIbDkb2dl1E0ElCZg5U9ZRKWazGWVlZSgrK0NxcTGefPJJXLx4EaNGjar3PQkJCdi1axeOHTuGX375BVVVVZg0aRLOnDmDe++9F7t378b333+PTz/9FA899BCsVit27dqFuXPn4ssvv8SJEyewevVqnD59GslX+nATEhKwf/9+HDp0CL/88gsuy3TMHgUbw4cPx1133YWuXbti8ODBKCgoAAAsW7as3vfk5uYiKirK/mjbtq13LfahJpIErF8PafRoW2RbXg7s2mV7vn49Ml+eCameZB2i+iQmCkjhJ2yPpqUAruZo9OjBQININZo0sY06WbfO4Q9OrFtnWy5jzsYnn3yC+Ph4xMfHo1evXtizZw/ee+89DBgwoN73TJ8+HTqdDl26dEFMTAxOnDiB1q1bY8eOHbBarbjtttuQkpKCP//5z4iKikJISAiMRiO2bduGESNGoFOnTvjb3/6GefPmYfjw4QCARx99FJ07d0bPnj0RExODHTt2yHK8Xg19bdasGbp27YqSkpJ618nKysLUqVPtz00mkyoCjpCQEISEhtqSgJwkB0m5uYguLERSv1tweOtOZRpJAYM5HEQqJUm2USc17yhcvizrsNelS5di6dKlHr+vU6dOKCwsrLM8KSkJq1evdvqe5ORkfPLJJ/VuMyYmBp9++qnHbfGUV8GG2WxGcXEx+vXrV+86BoMBBoPBm934VHU/ZM97Mmw5Gm6Sg4zxwVdwSU1zQ2i19omnORzQVcrfKCf8mfsgZw4Aczhsap5TX393XG1P8/kcte9gyHhHI1h5FGxMnz4do0aNQrt27XDq1Cnk5OTAZDJh/PjxcrVPNqbSK7NuukkOsq9H1AjVORw9xrxQ/0qcBZaIApxHwcYPP/yAe++9F7/88gtiYmKQlpaGnTt32guFaEnJ9t04c/QEWsycacvRqNmVIkkQWVk4+/1xlGzfrVgbKTBU53AQEQUrj4KNVatWydUOvxNVVcibNgcT318AsXYtpNxce6lakZUFpKcjb8zjrLdRizDHuq52yb/SiYioloCbG8WTfsqiNRuwcMzjtjobNZJuzn5/HHljHndbZ0Pz/ZQN5Jh/4L6sds15PurbFpE7NT8rnuZFcH4Z/9ZE8WTbl+D7P+BE7SR/8gtPznvABRueKlqzAfvWbURSv1tgjI9lBVEXLlyw/Vs9r0dtnOeDiPypyZVEzoqKCoSFhSncmuBz6dIlAIBOp3O7btAHG4CtS4XDWxuuumYEEZGSdDodmjdvjlOnbHdSw8PDIflpptZgV1VVhdOnTyM8PLzeyqU1MdggIlIIc6C8Vz1XV3XAQf4TEhKCdu3aNSjA03ywodU6DMHC075yXk9lBWqug5zH1di6G8IcC0uxiyHRV7jKgfLkuNQyr42vSZKE+Ph4xMbGylZqm5wLDQ1FSAOrbGs+2CAi0qQrdzSYA+UbOp2uQbkDpAwGG0RECmIOFAUDVQYbar29p+ahrt6cM0+Py908H1uXzED37hyKFgy8KY+t5BTmwdBd5O6YArVbhdRJlcEGqVNkpO1fd/N8VK9HREQEMNggD1TP81Fdb8OZyEjbekRERNUYbJBHGEgQEZGnFAs2suJuRigaNmRGKWrK0fBnqWF/Hjf7iakh5J7W3ZNcB3c8bau7HKjGcrdfV9PTO8PvKnmDdzaIiJSgqwTgPgeqej0iLWOwQUSkAMlwCvrkGawgSkGBwQYRkUIYSFCwkISf5+Y1mUyIiorCg2irupyNYMnRcCdYczb8ddy+zjeQkyf5C76+lp5cD1+eU7lrbqj1+vur1ojJZEJcXBzOnz8Po9Hol32S8tT1vz0REREFHAYbREREJCvmbGiYBIEkmGGEFSboUAIDBNxP9UtERORPQRdsqCkvwxupqECm3oRoi9m+7IzegDyLEUUIV7BlROSKJ7kR/szv8HRfgTKfDPkHu1E0KBUVmIjTaDFsCFBYCFy4ABQWosVtgzERp5GKCqWbSEREZMdgQ2MkCGTqTUB6OqR164C0NCAiAkhLg5SfD4wciUy9CRJYVpyIiNSBwYbGJMGMaIsZUnY2EFLr8oWEQMrORrTFjCSYnW+AiIjIzwIuZ0OrORkNrVFghNX2Q0qK8xWuLLev1wDBWlejtppt0+rnKJB4cj28nTtFrfkHnrZLzTke1YT1ko9bQlrAOxsaY4LO9sPBg85XuLLcvh4REZHCGGxoTAkMOKM3QOTkAFVVji9WVUHMmYMzegNKYFCmgURERLUEXDdKoBOQkGcxYmJBAURGhi13IyUFOHgQYs4coKAAeYhhvQ0iko0wx7qcQE5Ym0HSldfzGvPJgpFiwUZu2Z6grovvTe5CEcKxEDHI3LAJ0QUF9uVn9QbkIYZ1Nnyg9vVhDodzas3B8TaHQ6v8UcNDmGNhKX6hUe+1MQHw5v2kRbyzoVFFCMc+S5hjBVELK4gSkcyu3NFYsQJIdhJrfvQR8Mwz9b++dy/wxz/K3EZSHQYbGiYg4TDqv5VJRCSX5GSgR4+6y4uLXb9+8aK87SJ1YrBBREReKSmxFTIGgKNHlW0LqVPABxue9EvKPdZeLf3brKvhuWDJ4VBrvQlvz78/j0vJz7wn58XdOWno786SEqBTpwbvloIUh74SEVGjVd/RWLHClo+xYoWy7SF1Cvg7G0REJL/6cjSIAA0GG1oawqaV26kUvNTabUKN483vnNq/M+r7bHz9tYS+fa8mghbX86umvuWHDjW2haRlmgs2iIhIOZGRtn/HjWvc6xScGGwQEVGDJSYK7N9fiR5jbIW5RGU8rCcm2l9PSgIOH76ay1FcbAs8dO0WQmpaCmE1w/qdEi0nJTHYICIijyQmCkjhJ+p9PSmp7jKpaantPZz1NSipMtjwZ16GN33WahrW6cscjdrnn/36RIHP3e+z+nI6audw1Fbfcgouqgw2iIhIGxqco6GrlL0tpF4MNoiIqNGqcziqczQAoP+DtSZa01VCMpzyb8NIVRhskOZIEI4T0IET0BEpKTFRODx3lc9BwUmxYKPNkGmQdKF+3y9zNOryZY6G3OcoFRXI1JsQbTHbl53RG5BnMaII4bLuOxB4m3+jpu+AL9X8DjBHyTlPSsa7OocmkwlxcSt91i7SBpYrJ81IRQUm4jRaDBsCFBbaxtYVFqLFbYMxEaeRigqlm0hERE4w2CBNkCCQqTcB6emQ1q0D0tKAiAggLQ1Sfj4wciQy9SZIEO43RkREfsVggzQhCWZEW8yQsrOBkFof25AQSNnZiLaYkQSz8w0QBQgpJASd+qeh5z0Z6NQ/DVLt7wORCgV8gqia8w88IedcJ1roozbCavshJcX5CleW29cjAMzRaAxvc5hqf1d9eQ5T77gNmfOyEd2hnX3ZmaMnkDdtDorWbPDZfhqi5nFxLiZyhyExaYIJOtsPBw86X+HKcvt6RAEm9Y7bMPH9BWhxoMihG7HFgSJMfH8BUu+4TekmEtWLwQZpQgkMOKM3QOTkAFVVji9WVUHMmYMzegNKYFCmgUQykkJCkDkvG1i/HtLo0cCuXUB5ObBrl+35+vXIfHkmu1RItfjJJE0QkJBnMQIFBRAZGQ6jUURGBlBQgDyLkfU2KCAl9bsF0R3aQZo7FxC1kqCFgJSbi+jr2iOp3y3KNJDIDc3nbLBPOngUIRwLEYPMDZsQXVBgX35Wb0AeYlhn4wot5OBoiRpyOIzxsbYf3HQj2tfzA+ZpkCe8urORm5sLSZIwefJkHzWHyLUihCPb0gr/RCu8jWvwT7RCtqUVAw0KaKbSK6W+3SRI29cjUplGBxt79uzBm2++iW7duvmyPURuCUg4jKb4Es1wGE3ZdUIBr2T7bpw5egJi5kxAqvV5lySIrCyc+f44SrbvVqaBRG40Kti4ePEixo4di7feegstWrTwdZuIiKgGUVWFvGlzgPR0iLVrHUajiLVrgfR05E2fC1E7eZpIJRqVszFp0iSMHDkSgwcPRk5Ojst1zWYzzOarhZZMJpPH+/NlH7RWcjS00h+qlfMZyILh+6Gm74Mvczg8Od9FazZg4ZjHbXU2Cgvty89+fxx5Yx73eZ0NNZ1z0j6Pg41Vq1Zh7969+PLLLxu0fm5uLp5//nmPG0ZERI6K1mzAvnUbkdTvFhjjY2EqPYWS7bt5R4NUz6Ng4+TJk/jzn/+MTz/9FE2bNm3Qe7KysjB16lT7c5PJhLZt23rWSiIiAmDrUjm8dafSzSDyiEfBxt69e3Hq1CncdNNN9mVWqxXbtm3Dv/71L5jNZuh0jhUcDQYDDAbXhZbkHKqn1tvCmmO1ImTHDkhlZfjXhKdRAgMTM2WS2C4WEeH1B/MXKypx5IRvRh3w+2Hj6ndQ7W4TX3LXVeHP6+PLbhNX50xYL/lsP6QdHgUbgwYNwoEDBxyWPfjgg7j++uvx9NNP1wk0KDCErF2LJllZCDl+HAAwFcAZvQF5FiOHnPpYYrtYfLvuBbfrdbl9hs8CDiIiuXkUbERGRiKl1jjvZs2aoWXLlnWWU2AIWbsWoWPHAunpwKpVtvH8Bw+iRU4OJhYUYCGLaflU9R2NsWOBYid/aCYnAytXwuWdDyIitdF8BVGSkdWKJllZQHo6pLVrr07tnpYGKT8fIiMDmRs2YZ8ljF0qPlZcDHz9tdKtICLyDa+DjS1btjTqfT9snAej0ejt7gOWu75afwxLC9mxw9Z1smrV1UDD/mIIpOxsRBcUIAlmHAb/0m6o2vkBjckJ2LZkBkTtOTICjFqGXipZ/l3O6eqJ/IkTsVG9pLIy2w9uSiQbYfVTi4iISIsYbFC9RFyc7Qc3kz+ZwMRgIiKqH4MNqldV376oat8eIicHqF00qKoKYs4cnNEbUALXQ5spsEkQ6IRK9EQ5OqESEgK7e4eIPCcJP3f8mkwmREVFoaysLOBzNgKhfzUVFZiI08DIkZCys+2jUcScOUAQj0aRK59AkiQ0bdrU7WiUysrKRuVs+PozmYoKZOpNiLZcnZKgscOi1ZKjoSVy/o6Rq+6GsF6C5cBKnD9/PuD/D6CrOBqFXCpCOBYiBpkbNiG6oMC+/KzegLwgDTT8YeVKpVvgnj0QHZYO1AhEOSyaiGpjsEFuFSEc+yxhSIIZRlhhgg4lFlYQlYMQAl1un+FQR2PbkhlO11OSBIFMvQkYlg5p3ToOiyYilxhsUIMISBze6ie1K4MqHVg4kwSzreskO5vDoonILQYbMnLV56nlfA45+9a1fF58Rcm6Dg1lH+7MYdGKkrMOR+1tefO9r/mZNplMiIvTQD8h+RRHoxCRx+zDnTksmogagMEGEXmsBAac0Rs4LJqIGoTBBhF5TEBCnsUIFBRAZGQAhYXAhQtAYaHteUEB8ixGJocSEYAAyNlozLwSDaWFvnN/8Gf9A84FoR3BMixayc+gp9+9muv7ut2utscaKeSO5oMNIlIOh0UTUUMw2CAir3BYNBG5w5wNIiIikpUq72zImYfhCXftCNScDjX1v8rZB03q4su6Dt7uWy3ctcvVOVLT95iIdzaIiIhIVgw2iIiISFYMNoiIiEhWiuVstBkyDZIuVKndUw3s26Vgo9YcDU8pmedC5Ane2SAiIiJZMdggIiIiWSnWjSIq2gI6J5M06SohGU75v0FBRKu3WlnK3HtaOoe+LI+t5uMkCgaKBRvW77IAGJ2+pk+ewYCDiIgoQCgWbGzdCkREOC4rLgbGjQNgZeljIiKiQKFYsJGaChid39gg8hkJwnGSMHCSMCIif1NluXK18rY8OfuN/SsVFcjUmxBtMduXndEbkGcxBsz058GK3yXf89c0EcJ6yS/7IXXhaBQKSKmowEScRothQ4DCQuDCBaCwEC1uG4yJOI1UVCjdRCKioMFggwKOBIFMvQlIT4e0bh2QlmZLEEpLg5SfD4wciUy9CRKE0k0lIgoKDDYo4CTBjGiLGVJ2NhBS6yMeEgIpOxvRFjOSYHa+ASIi8inFcjaKipyPRgGArUtmoHv3q391KjXlfKBOIa9VDe2nN8Jq+yElxfkKV5bb1wtiWqq7Qe7VvH6e1iKp/ftOqd+7FJgUCzb696//tchI/7WDAo8JOtsPBw/aulBqO3jQcT0iIpKVYsHGp59WolmzuhOxRUYCiYnsS6fGK4EBZ/QGtMjJseVo1OxKqaqCmDMHZ/UGlFicVLAlIiKfUyzY6NZNwGhkUEG+JyAhz2LExIICiIwMW+5GSgpw8CDEnDlAQQHyEMN6G0QNJMyxrostcpoJckMTdTZq9iX6uh/Rl3kZ7O9WjyKEYyFikLlhE6ILCuzLz+oNyEMM62xQwPN2+vnq341Hjkjo1s19VWdOM0GuaCLYIGqMIoRjnyXMsYKohRVEiTxx4YLt3xUrgGQnf09xmglqCAYbFNAEJBwGfwkSeSs5GejRQ+lWkFaxzgYRERHJSnN3NtRU+0KrORre9uXKSavnlEjt5P7e166PVB+TyYS4uJU+3TepH+9sEBERkawYbBAREZGsNNeN4hdWK0J27IBUVgYRF4eqvn0BHatNElHwKq6n16W+5UQ1MdioYUp4MlJRgUy9CdGWq5N0ndEbkGcxBmxtBuZJuKemvBYiX2jo9/48EgBssQ1vdYHTTJArDDZqSEUFJuI0MCwdqFF1skVODiYWFGAhi0ERUZCJwjHcgwF4cscn9a7DaSbIHQYb1axWZOpNwLB0SOvWXZ1PIy0NUn4+REYGMjdswj5LGItCEVFQicKxBo00IaoPE0SvCNmxA9EWs20ejZBapyUkBFJ2NqItZiTB7HwDRERE5FTQ3dmor5+yJ8rxCGDrOnHmynIjrPI0jIgUo9acHDXlU3nSFrWeT1IO72xcYcKV0SYHDzpf4cpy+3pERETUIAw2riiBAWf0BoicHKCqyvHFqiqIOXNwRm9ACQzKNJCIiEijFOtGaTNkGiRdKAB5S5A39NafgIQ8ixETCwogMjJsuRtXRqOIOXOAggLkIYbJoUQBQCu3+d21U03dLDW5atclVNX7GgUuj+5sLFiwAN26dYPRaITRaETv3r3x8ccfy9U2vytCOBYiBmc3bAL69AGMRqBPH5zdsInDXomIiBrJozsbbdq0wQsvvIDExEQAwLJly3D77bfj66+/xg033CBLA/2tCOHYZwlDEswwwgoTdCixGHhHg4iIqJE8CjZGjRrl8HzOnDlYsGABdu7cGTDBBmDrUjmMpko3g4iIKCA0OmfDarXivffeQ3l5OXr37l3vemazGWbz1doUJpOpzjpRfSY5PPcmh0OtfZhEpByt5Gh4qvZx8fcfqZXHo1EOHDiAiIgIGAwGPPbYY1izZg26dOlS7/q5ubmIioqyP9q2betVg4mIiEhbPA42OnfujKKiIuzcuROPP/44xo8fj2+//bbe9bOysnD+/Hn74+TJk141mIiIiLTF426U0NBQe4Joz549sWfPHrz66qtYuHCh0/UNBgMMBtamICIiClZe19kQQjjkZBARKc2XORq1c8p8ydc1hmoeN/M3SE08CjZmzpyJ4cOHo23btrhw4QJWrVqFLVu24JNP6p96mIiIiIKbR8HGzz//jPvvvx+lpaWIiopCt27d8Mknn2DIkCFytY+IiIg0zqNgY9GiRXK1Q7MkCMcCYGABMCIiopoCbop5f447T0UFMvUmRFuu5qyc0RuQZzH6vbS5J33U7MulaoHyWfA2R0POvAxP9ivnPFFESuKsr42UigpMxGm0GDYEKCwELlwACgvR4rbBmIjTSEWF0k0kIiJSBQYbjSBBIFNvAtLTIa1bB6SlARERQFoapPx8YORIZOpNkCCUbioREZHiGGw0QhLMiLaYbdPQh9Q6hSEhkLKzEW0xIwkcEkxERKRYzsYPG+fBaDTKvh85cjiMsNp+SElxvsKV5fb1ZOBNH7W79wZKPz4FLq3maBAFK97ZaAQTdLYfDh50vsKV5fb1iIiIghiDjUYogQFn9AaInBygqsrxxaoqiDlzcEZvQAlYpp2IiIjBRiMISMizGIGCAoiMDIfRKCIjAygoQJ7FyHobRERECMA6G+74KoejCOFYiBhkbtiE6IIC+/KzegPyEOP3Ohu+xJwOUptAzdFgXQ0KFkEXbPhSEcKxzxLmWEHUwgqiRERENTHY8JKAhMNoqnQziIiIVCvogw1Oyew5njPt0tL18qTrRK3dJP6m1utb81qaTCYsiYtTsDWkBCaIEhERkawYbBAREZGsgr4bhYjIV4Q5FrC6yOHSVUIynPJfg4hUQrFgIyvuZoTWc2PF22FujeXP6em9Vbttajlntan5HKqFu3yDYBkeqdRn2FeEORaW4hfcrrd/fyUSE30zSaNavl9av3YkP97ZICLyhSt3NFasAJKdxADFxcC4cbb6f0TBhsEGEZEPJScDPXoo3QoidWGCKBEREclKlXc21NIPSd7TUh6Mv7AmhPeC9Rzy+0NaxTsbREREJCsGG0RERCQrxbpRJAh0QuXVCczACcyISPuK6xkFWt9yomCgWLDxjO4UEqyX7M/P6A3Isxg1PTU7ucccDs/Vzk8IlLobATf3ia4SgG14qyuRkQ3fpFa+H2qp+0PqpViw0WLwQGDWLCAlBTh4EC1ycjCxoAALEcOAg4g0RzKcgj55Bra+PavedSIj4bOCXkRaolw3yqpVQPPmtidpaZDy8yEyMpC5YRP2WcLYpUJEmiMZTqF7dwYTRLUplyAaElLnuZSdjWiLGUkwK9MmIiIi8jl11dlISQEAGGFVuCHqJ2efqCf9497mDzCHg7QuUHJofMnV9/gSqvzYElILdQ19PXgQAGCCTuGGEBERka8oF2xUVdV5LubMwRm9ASUwKNMmIiIi8jnFgg1x991AYaFtCsTCQoiMDKCgAHkWI5NDiYiIAohiORuiuBjo0+fq84QEXP7PfzB+9GgUBUG/faCMQ3eX3+Fpf7ZaczhYR4CIqPEUCzbMO3fCfOAApLIyiLg4VPXtC+iYq0FERBRolBuNotOh6tZbFds9ERER+Ye6hr4SNYIEgSSYOc9OEBPmWMDa1PbzpWigqlaSeYgZUugZ28+6SkiGU35uIVFwU2Ww4ao/XC19+I0RjP383s7rUfOcObv2qahApt6EaMvVQnCBPs+OVudKkevzL8yxsBS/4NF79MkzGh1w+PN8qzWHyVM1j8NkMmFJXJyCrSElqKvOBpEHUlGBiTiNFsOGOIxsanHbYEzEaaSiQukmkj9cuaOxYgWwd2/dx4oVsL9e/XP1e4jIP1R5Z4PIHQkCmXoTMCwd0rp1V8vfc56doJWcDPTo4fp1IlKGYsFGmyHTIOlCHfpandJVwrT3OftTd7ditXqbkdyree1Dtm2DYdgwIDu7/nl2CgqQBDMOg3/Fal3trgtfTzlfc/vutu3pvn3Z7aLWbpVg7CImzyh6Z6Ohfa1HjlRyWmZyIJWV2X64Mp9OHZxnh4hINZTtRqnR1+rsFmdxMTBunK0rnqgmUZ1gdvAgkJZWdwXOs0NEpBqqyNlw19dKVFtV3744ozegRU4OpPx8x66UK/PsnNUbUGLhPDtEREpTRbDhTv8HX4AUfsLpa7X7Q5nT4T05+8d9NmxTp0Oz5UuAsWMhRo+GlJVl6zo5eBAiNxf46CPkiWuYHBqgan5uvv5aQt++tjuhzlQvr/n61iUz0L27865ZX3/+Xb3f23wOf5YJYF4GeUMTwQaRM1WjR+PSypVokpUFqfY8OytXoui+LAVbR/4SGWn7d9w41+vVfL36PUTkHww2SNOqRo+GedQohOzY4WSeHQYbwSAxUWD//kp7btfJkxIqapVYCQ8H2ra13cmIjAQTzon8jMEGaR/n2Ql6NYOH+rpHiEg5igUbP2ych+++i2pQX6srnk5xrtVS6EqO7ddEDocTvqxJwP5qz2n1nPm6hoc3+/Lm+6Cm81/zOIX1koItIaUoemejoX2t0FXK3hYiIiKSh6LBRu2+1mr9H6xR6IszNBIREWma4jkbzhK16hvmSkT+J0EgCWYYYYUJOpTAwCHFROQRj4KN3NxcrF69Gv/73/8QFhaGPn364MUXX0Tnzp192ihf5gh40ieqpj5Ob9U8bl9Pie3JPBJqEkjX119SUYFMvQnRFrN92Rm9AXkWI4oQrmDLgoOcOR2+pKXfA6QMj6aY37p1KyZNmoSdO3di48aNsFgsGDp0KMrLy+VqHxEpJBUVmIjTaDFsCFBYaJs3oLAQLW4bjIk4jVRUuN8IERE8vLPxySefODxfsmQJYmNjsXfvXtzKoYdEAUOCQKbeBAxLh7Ru3dVy8GlpkPLzITIykLlhE/ZZwtilQkRueXRno7bz588DAKKjo33SGCJShySYEW0xQ8rOdpx3BgBCQiBlZyPaYkYSzM43QERUQ6MTRIUQmDp1Kn73u98hpb5pvgGYzWaYzVd/IZlMJo/3JWeOgJzzFqiFnLUs3G2LfbnaZITV9kN93+0ry+3rkSLU+vvL1b5NJhPi4lb6sTWkBo2+s/HEE09g//79eOedd1yul5ubi6ioKPujbdu2jd0lEfmJCTrbDwcPOl/hynL7ekRELjQq2HjyySeRn5+Pzz//HG3atHG5blZWFs6fP29/nDx5slENJSL/KYEBZ/QGiJwcoKrK8cWqKog5c3BGb0AJDMo0kIg0xaNgQwiBJ554AqtXr8bmzZvRoUMHt+8xGAwwGo0ODyJSNwEJeRYjUFAAkZHhMBpFZGQABQXIsxiZHEpEDeJRzsakSZPwn//8B+vWrUNkZCTKysoAAFFRUQgLC5OlgbXJOU9HbVoZ465mPEfaVYRwLEQMMjdsQnRBgX35Wb0BeYhhnQ2VkzNXqzZ+z8kdj4KNBQsWAAAGDBjgsHzJkiWYMGGCr9pERCpRhHDss4Q5VhC1sIIoEXnGo2BDCE7dTBRsBCQcRlOlm0FEGuZVnQ0iIiIidxSfiE3L/Nkn6ktabTfZaOV6TQlPdnjubm6a2ut7g/PgEKkL72wQERGRrBhsEBERkaw0142i5vLXcg6V9eeQX/I9pa6XL7smvKWmtrgSKN81NXW31bz2l1DlYk0KVLyzQURERLJisEFERESyUn03ypEjEi5cuPpcVLRzXEFXCclwyr+NIiIiogZTdbBx5IiEbt1qFxOaVWc9ffKMoAs4AqVfmUgOng67dUXN3zW15GVoJR+HlKPqYKP6jsaKFUCyk89ycTEwbhwAK6sbEhERqZWqg41qyclAjx5Kt4KIiIgaQxPBBjmSpLqTYKVefzWX5WJFJY6cCK5uJSJSgNWKkB070BPltkn6wEn6yDnFgo2suJsRemUwDEsLN5wkSWjatG630e53Zjk873L7DAYcPqRkP71a+uW1LFByOJT8LNQ+h6moQKbehGiLGY9cWXZGb0CexYgihPu/gaRqHPqqUWPH2rqWaj/GjrW9HhHOPBYikkcqKjARp9Fi2BCgsNCWYFdYiBa3DcZEnEYqKpRuIqkMu1E0qrgY+PprpVtBRMFGgkCm3gQMS4e0bh0QcuVv1rQ0SPn5EBkZyNywCfssYexSITtNBBvF9dzxrG85ERHJIwlmRFvMQHb21UCjWkgIpOxsRBcUIAlmHAbvsJKNKoKN+sZon0cCgC224a0ufPX+DCQmCgDqGgNP2qXlzxFrHrjn6hx5ms/hyxwOX+ZkyPU5MMJq+yElxfkKV5bb1yOCSoKN+kThGO7BADy545N614mMhD3QICIieZmgs/1w8CCQllZ3hYMHHdcjgsqDDcAWcHTvzmCCiEgNSmDAGb0BLXJyIOXnO3alVFVBzJmDs3oDSiwG5RpJqqP6YIOcc1ZR1dVyIiJfEJCQZzFiYkEBREYGpOxsW9fJwYMQc+YABQXIQwyTQ8mBJITw620Dk8mEqKgoPIi29job3vC2RoecffNyjImvr85GbZWVlai+tJ4eo1r7jf1Zj0XLdTWYs+EduX+neHN91XRta9bZqFZfnY2a59RkMiEuLg7nz5+H0Wj0W3tJWbyzoTFCCFRWVjZoPSIiuRQhHPssYUiCGUZYbRVELawgSs4x2NAgBhJEpAYCEoe3UoMETrBxpUa/VFYGEReHqr59AR2zoYmIiJSm+WBjSniyy77D8RXHXb4/GOad8OdcDnL2KXu6bc65Q0oIlJwMb/C7R7Vpfm4UdzX6Q9auVbqJREREQU3TwYa9Rn/6lRr9aWlARIS9Rj9GjkSTmTMBKyvZERERKUWxbpTcsj32YU+NvXXYkBr9Up8+WBCZWG8Sk1pu9/mzi0DJKbL9yZfTiqtZoNx6D0aBcu3cfbdq/o4R1ktyN4dUSNN3Nlijn4iISP00HWw41Oh3hjX6iYiIFKfpYKO6Rr/IyQGqqhxfvFKj/4zegBKwRj8REZFSVDH0tXZ/X0P7MX1Ro1+rfabBko/gSzxnpAZa/Z1Tm6vvT6DmgVHjqSLY8EYRwrEQMcjcsAnRBQX25Wf1BuQhpk6NfiIiIvIvzQcbAGv0ExERqVlABBsAa/QTqYUE4Rj4g4E/UbBTZbDR2ByOYOZNPkIwlGwn//Bk2nE1kTN/J1B+f9U+R8zLIE9oejQKEamHu6kDUlGhdBOJSCEMNojIaw2ZOiBTb4IEoXRTiUgBDDaIyGvVUwdILqYOiLaYkQSz8w0QUUBTZc5Gba76UwOlP9TXap4X1pNwjnU3fEdrUwfwWrvHHA3yJd7ZICKvceoAInKFwQYReY1TBxCRKww2yC8kCHRCJXqiHJ1QqfpEQWGOhahoV//DHKt0E1WleuoAXJk6oOZoFJGRYZs6wGJkvQ2iICUJIfz6W99kMiEqKgr6rmMh6UKdruNN3QfmcLjn75oCWqm9UH1ejhyR0K2b+wJx+uQZkAynfN4Ob+ueKPkd0Nq1loNWfwf5K0dDWC/BcmAlzp8/D6PRKMs+SH00kSBK2lVdewHD0oEaE+W1yMnBxIICLFTh/DUXLtj+XbECSHby/0ZxMTBuHAArK9bWxqkDiMgZBhskG3vthWFXai9UD4m8UntBZGQgc8Mm7LOEqfI/o+RkoEcPpVuhPZw6gIhqY84GySbQay+IynjmcBARNYAq72y46it015/NeVXc81d9Ca3VXvCU9cREh+dy5XCQ91hXoy5/npOav7dNJhPi4lb6bd+kDryzQbIJ9NoLK1YAe/fa/gXAHA4ionow2CDZBHrtheqcDmdJpEREdJUqu1EoMFTXXph4pfaCVGM0ipgzx1Z7ATGqTA4FbKNOPFlORETOeRxsbNu2DS+99BL27t2L0tJSrFmzBqNHj5ahac7VzudgDoe6ODvfCxGDzA2bEF1QYF9+Vm9AngqHvQJAZKTt33HjGrae2tS8Bvy8y08r59hdjoandTW8rQdDwcXjYKO8vBw33ngjHnzwQdx1111ytIkCjNZqLyQmCuzfX2mvt3HokISHHjI41N2IjASSkpRrIxGRlngcbAwfPhzDhw+Xoy0UwLRWeyExsW5hXdbdICJqHNlzNsxmM8zmq3UUTCaTT7fPbhXvcap1m5rnwdk5YA6HcxKE410rKH/XKlg/w95gtwjJSfZgIzc3F88//7zcuyGSTUNzOKCrlL0taqOVuVCISFmyBxtZWVmYOnWq/bnJZELbtm3l3i2RzyQmCuiTZ7iuo6GrDLqCXlqc94aIlCF7sGEwGGAwaLOOAlG1YAsk3NH6vDdE5F9BX2eDwwRJaXL2lcuVo1Q97w1czXtTUIAkmDWVGNwQvsxxkvN3DvNWSE08DjYuXryII0eO2J8fPXoURUVFiI6ORrt27XzaOCJSp0Cf94aIfMvjcuVffvklunfvju7duwMApk6diu7du+PZZ5/1eeOISJ0Cfd4bIvItj+9sDBgwAELUrUHgK8Icy0Q80pTEdrGICK//M3uxohJHTgTWZ7Z63psWOTmQ8vMdu1KuzHtzVm9AiYX5WkSkYM7GDxvnwWg0Oiw7ckRCt27u+3drTuXty/5uNdXg8La/1Zu2s+5GXbU/Z9X1XRLbxeLbdS+4fX+X22c0OuBwV0bak++Arz7jWp/3xhuB+n3w5neGJ6XQhfVSo/dD2qWqBNHq8tA1y0LXVFx8pdYBp/Imlai+ozF2rPPiXsnJwMqVcHnnQ6uKEK65eW+ISBmqCjaqsSw0aU1xMfD110q3wv+0Nu8NESlDlcEGEWmH1ua9ISL/02SwsXXJDHTvLl+SaiBQS/0QNeXBaIWnU317Oj9QTYF6fdzlHXlynFrN0ZD7Wro6L55+hinweTz0lYiIiMgTDDaIiIhIVqrsRuFU3qQ1zkZPuVpODSOFhCCp3y0wxsfCVHoKJdt3Q1RVKd0sIvKQqoKNhk7lXb0eNYy3/fKsu1G/ixW2aeVXrmzYeoC8c6EAjv3lnu5LTTkcqXfchsx52YjucHUahDNHTyBv2hwUrdng0baCIUdDbt6cl5qfQ5PJhLg4N18YCjiqCjYSEwX276+019twJjLSth6RGhw5cQpdbp8RdBVE5ZZ6x22Y+P4CYP164N5MW/nzlBS0mDkTE99fgIVjHvc44CAi5agq2AAYSJD2MJDwLSkkBJnzsoH16yGNHg1UT4+waxek0aMh1q5F5sszsW/dRnapEGmE6oINIi3gHD7ySep3i63r5N7Mq4FGNSEg5eYiurAQSf1uweGtO5VpJBF5JOCDjUCpG+BLvszh8LQf15/1DuS69keOSLAUu58PpeYcPkrxpgYHoEwOhzE+1vaDmxll7ev5mbdz1QTD7yS585JIewI+2CDyNc7hIy9T6ZUALSUF2LWr7gopKY7rEZHqMdggaiTO4SOPku27ceboCbSYOdMxZwMAJAkiKwtnvz+Oku27FWsjEXmGwQaRiiW2i9X8SBdPa2WIqirkTZuDie8vgFi7FlJurn00isjKAtLTkTfmcSaHEmmI5oONQO3/DJbaFu5yArw5bjXVjGhMH3Ziu1h8u859bkiX22c0OODwdw5HY2tlFK3ZgIVjHre9t7DQvvzs98eRJ8Ow10D9fnmD54R8SfPBBlGgqr6jMXas8+q5ycm2YmKu7nwoydtaGUVrNmDfuo2sIEoUABhsEKlccTHw9ddKt8IzvqqVIaqqOLyVKAAoFmy0GTINki7U6WucItt7npSs9uYc+rq7R85bt77+rHAOn/oFUq0Mb6dL18rvJHabkJx4Z4PIQw2dwwe6SjcrBC6118ogIv9isEHkocREAX3yDFYQdYG1MoioJgYbRI0QzIFEQ7BWhiMJAkkwwwgrTNChBAYISEo3i8hvVBlseDM8jzkcdXk73NETWhqyq+a21eSsSqmr5Z7w5VDYmtde7bUyXF17T3M03J2zVFQgU29CtMVsX3ZGb0CexYgihHu0L1/SyuefAoMqgw0ishXsAmzDWxuyntr4u1aGGoWsXYuJOA0MSweys23dRwcPokVODiYWFGAhYhQNOIj8hcEGkUodOXEKXW6foekKokFdK8NqRZMrd3GkdeuAkBDb8rQ0SPn5EBkZyNywCfssYexSoYDHYINIxdQcSDRUUNbKsFqhmz8fIcePA3/9a93hvyEhkLKzEV1QgCSYcRjqLMxG5CuaCDZc9aHKWUNCy2qel9rnz10/fbCeMwpc/sxPWBbe3jFHY9Ik4KWXgHnzgDvvvLrilRE5RlhlaQdzMkhNQpRuABFRoKjO0WgxbAhQWAhcuGD7t2tXYMwYYPXqqytfqTVigk6h1hL5D4MNIiJfqJ2jkZYGRETY/l27FkhPB6ZPB6xWoKoKYs4cnNEbUAKD0i0nkp0mulGIiBpCyXoWITt22HI0Vq26mgxqfzEEyMoC+vQB/v1viI8/BgoKkIcYJodSUFAs2Phh4zwYjUYA3s094GmdAFf9mGrOTdBS/Yqa5Gy3r+shkOfU9J1xVc/CHU8+S7U/R9XnoCfK8Qhgz8Woo3r5E0/grN6AvEYMe9XK9762mudXWC8p2BJSCrtRiEjzUlHhNFeixW2DMRGnEbJ2rextsOdeuJkPJg8tkG1pxfoaFFQYbBCRpkkQyNSbnOZKSPn5wMiRaDJzpi1XQkYlMOCM3gCRkwPUriNSI0fjc0Sy64SCDoMNItK0JJgRbTFDys52mishZWcj5NgxhOzYIWs7BCRbl01BAURGhsMdFpGRYcvRsBgZaFBQUkWCaO0+UF/mcLjbV01qri+h1b7aQOHNZ9JTvvw+BAN7nQo3uRJSWVmjtu9Jrk8RwrEQMcjcsAnRBQX25Q3N0dDq95yfUXJHFcEGEVFjOeRKpKXVXeFKroSIi/NLe4oQjn2WMMdRMRbO8krBjd0oRKRpbnMlcnNRlZCAqr59/dYmAQmH0RRfohkOoykDDQp6DDaISNNc5kqMHg2sX4/Lc+cCOlbqJFKKKrtR5Oyz9qQuh5pyOLypV+HufPpzrhRvjoP9wlTN2efm0tq1aJKVBalPH/sykZCAyytXomr0aId1vZlvyZe/B3ydo+FNvRA590WkymCDiMhTVaNHwzxqFEJ27IBUVgYRF2frOuEdDSLFMdggosCh06Hq1luVbgUR1aKJYMPVdOneqrm9QJ2uPlBudwbrkFA1HbdSn3lvuxsC5bPizXEo2W1Sc98mkwlxcSu9agtpDxNEiYiISFYMNoiIiEhWDDaIiIhIVprI2ahJLcNiAe3mcKiJnFPQByp3n8tAyU/whqfnwNU59fX3WivDvZXMD6HAwzsbREREJCsGG0RERCQrzXWjEBFRXYntYhER3rTe1yVJghDCjy0iuqpRwcb8+fPx0ksvobS0FDfccANeeeUV9OvXz6NttBkyDZIuFIB3/Xty9l97msOhJnIed80+ZyXzVNRUf4J8z1Vug7fX2p/fZX/kaCS2i8W3615wu15lZWW9AYcv81yIavM42Hj33XcxefJkzJ8/H3379sXChQsxfPhwfPvtt2jXrp0cbSQiIheq72iMHQsUO4ltkpOBlayjRQryONj45z//iYcffhiPPPIIAOCVV17Bhg0bsGDBAuTm5vq8gURE1DDFxcDXXyvdCqK6PAo2Ll26hL1792LGjBkOy4cOHYovvvjC6XvMZjPMZrP9+fnz5wEAwnrZvsxkMnnSDI8I6yWfbctdOy+hymf7csddW/x13L4+Zm8+C+6OWc5tuyPnZ7w2b9qqlc+4P6+Ht8fsyb4ae1zWy2aYTCZYrfW8bgVMJtfdKJ7uu7Gf6QsXLtj2x/yR4CI88OOPPwoAYseOHQ7L58yZIzp16uT0Pc8995wAwAcffPDBBx/2x8mTJz3574c0rlEJopIkOTwXQtRZVi0rKwtTp061Pz937hzat2+PEydOICoqqjG71xyTyYS2bdvi5MmTMBqNSjfHL3jMwXHMQHAeN4+58ccshMCFCxfQunVrH7aO1M6jYOOaa66BTqdDWVmZw/JTp06hVatWTt9jMBhgMBjqLI+KigqaL2k1o9HIYw4CwXjMQHAeN4+5cYLlD026yqOiXqGhobjpppuwceNGh+UbN25Enz59fNowIiIiCgwed6NMnToV999/P3r27InevXvjzTffxIkTJ/DYY4/J0T4iIiLSOI+Djbvvvhu//vorZs+ejdLSUqSkpOCjjz5C+/btG/R+g8GA5557zmnXSqDiMQeHYDxmIDiPm8dM5BlJCI4/IiIiIvlwIjYiIiKSFYMNIiIikhWDDSIiIpIVgw0iIiKSlV+Djfnz56NDhw5o2rQpbrrpJmzfvt2fu/e7bdu2YdSoUWjdujUkScLatWuVbpLscnNzcfPNNyMyMhKxsbEYPXo0Dh06pHSzZLVgwQJ069bNXuyod+/e+Pjjj5Vull/l5uZCkiRMnjxZ6abIatasWZAkyeERFxendLNk9+OPP2LcuHFo2bIlwsPDkZqair179yrdLNIQvwUb1VPTZ2dn4+uvv0a/fv0wfPhwnDhxwl9N8Lvy8nLceOON+Ne//qV0U/xm69atmDRpEnbu3ImNGzfCYrFg6NChKC8vV7ppsmnTpg1eeOEFfPnll/jyyy/x+9//Hrfffju++eYbpZvmF3v27MGbb76Jbt26Kd0Uv7jhhhtQWlpqfxw4cEDpJsnq7Nmz6Nu3L5o0aYKPP/4Y3377LebNm4fmzZsr3TTSEn9NwnLLLbeIxx57zGHZ9ddfL2bMmOGvJigKgFizZo3SzfC7U6dOCQBi69atSjfFr1q0aCHefvttpZshuwsXLoikpCSxceNG0b9/f/HnP/9Z6SbJ6rnnnhM33nij0s3wq6efflr87ne/U7oZpHF+ubNRPTX90KFDHZa7mpqeAsP58+cBANHR0Qq3xD+sVitWrVqF8vJy9O7dW+nmyG7SpEkYOXIkBg8erHRT/KakpAStW7dGhw4dcM899+D7779Xukmyys/PR8+ePfGHP/wBsbGx6N69O9566y2lm0Ua45dg45dffoHVaq0zWVurVq3qTOpGgUMIgalTp+J3v/sdUlJSlG6OrA4cOICIiAgYDAY89thjWLNmDbp06aJ0s2S1atUq7N27F7m5uUo3xW969eqF5cuXY8OGDXjrrbdQVlaGPn364Ndff1W6abL5/vvvsWDBAiQlJWHDhg147LHH8NRTT2H58uVKN400pFFTzDeWJ1PTk/Y98cQT2L9/P/773/8q3RTZde7cGUVFRTh37hw++OADjB8/Hlu3bg3YgOPkyZP485//jE8//RRNmzZVujl+M3z4cPvPXbt2Re/evdGxY0csW7YMU6dOVbBl8qmqqkLPnj0xd+5cAED37t3xzTffYMGCBXjggQcUbh1phV/ubDRmanrStieffBL5+fn4/PPP0aZNG6WbI7vQ0FAkJiaiZ8+eyM3NxY033ohXX31V6WbJZu/evTh16hRuuukm6PV66PV6bN26Fa+99hr0ej2sVqvSTfSLZs2aoWvXrigpKVG6KbKJj4+vEzQnJycHdHI/+Z5fgg1OTR88hBB44oknsHr1amzevBkdOnRQukmKEELAbDYr3QzZDBo0CAcOHEBRUZH90bNnT4wdOxZFRUXQ6XRKN9EvzGYziouLER8fr3RTZNO3b986w9cPHz7c4Mk3iQA/dqME49T0Fy9exJEjR+zPjx49iqKiIkRHR6Ndu3YKtkw+kyZNwn/+8x+sW7cOkZGR9rtZUVFRCAsLU7h18pg5cyaGDx+Otm3b4sKFC1i1ahW2bNmCTz75ROmmySYyMrJOHk6zZs3QsmXLgM7PmT59OkaNGoV27drh1KlTyMnJgclkwvjx45VummymTJmCPn36YO7cucjMzMTu3bvx5ptv4s0331S6aaQl/hz68sYbb4j27duL0NBQ0aNHj4AfDvn5558LAHUe48ePV7ppsnF2vADEkiVLlG6abB566CH75zomJkYMGjRIfPrpp0o3y++CYejr3XffLeLj40WTJk1E69atxZ133im++eYbpZsluw8//FCkpKQIg8Egrr/+evHmm28q3STSGE4xT0RERLLi3ChEREQkKwYbREREJCsGG0RERCQrBhtEREQkKwYbREREJCsGG0RERCQrBhtEREQkKwYbREREJCsGG0RERCQrBhtEREQkKwYbREREJCsGG0RERCSr/w+mxZTM9acYRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#have to redefine y_train if running this\n",
    "'''adhoc_dimension = 2\n",
    "X_train, y_train, X_test, y_test, adhoc_total = ad_hoc_data(\n",
    "    training_size=20,\n",
    "    test_size=5,\n",
    "    n=adhoc_dimension,\n",
    "    gap=0.3,\n",
    "    plot_data=False,\n",
    "    one_hot=False,\n",
    "    include_sample_total=True,\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.ylim(0, 2 * np.pi)\n",
    "plt.xlim(0, 2 * np.pi)\n",
    "plt.imshow(\n",
    "    np.asmatrix(adhoc_total).T,\n",
    "    interpolation=\"nearest\",\n",
    "    origin=\"lower\",\n",
    "    cmap=\"RdBu\",\n",
    "    extent=[0, 2 * np.pi, 0, 2 * np.pi],\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    X_train[np.where(y_train[:] == 0), 0],\n",
    "    X_train[np.where(y_train[:] == 0), 1],\n",
    "    marker=\"s\",\n",
    "    facecolors=\"w\",\n",
    "    edgecolors=\"b\",\n",
    "    label=\"A train\",\n",
    ")\n",
    "plt.scatter(\n",
    "    X_train[np.where(y_train[:] == 1), 0],\n",
    "    X_train[np.where(y_train[:] == 1), 1],\n",
    "    marker=\"o\",\n",
    "    facecolors=\"w\",\n",
    "    edgecolors=\"r\",\n",
    "    label=\"B train\",\n",
    ")\n",
    "plt.scatter(\n",
    "    X_test[np.where(y_test[:] == 0), 0],\n",
    "    X_test[np.where(y_test[:] == 0), 1],\n",
    "    marker=\"s\",\n",
    "    facecolors=\"b\",\n",
    "    edgecolors=\"w\",\n",
    "    label=\"A test\",\n",
    ")\n",
    "plt.scatter(\n",
    "    X_test[np.where(y_test[:] == 1), 0],\n",
    "    X_test[np.where(y_test[:] == 1), 1],\n",
    "    marker=\"o\",\n",
    "    facecolors=\"r\",\n",
    "    edgecolors=\"w\",\n",
    "    label=\"B test\",\n",
    ")\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\", borderaxespad=0.0)\n",
    "plt.title(\"Ad hoc dataset for classification\")\n",
    "\n",
    "plt.show()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1fab37d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ┌──────────┐┌──────────────────────────┐\n",
      "q_0: ┤ Ry(θ[0]) ├┤0                         ├\n",
      "     ├──────────┤│  ZZFeatureMap(x[0],x[1]) │\n",
      "q_1: ┤ Ry(θ[0]) ├┤1                         ├\n",
      "     └──────────┘└──────────────────────────┘\n",
      "Trainable parameters: θ, ['θ[0]']\n"
     ]
    }
   ],
   "source": [
    "# Create a rotational layer to train. We will rotate each qubit the same amount.\n",
    "training_params = ParameterVector(\"θ\", 1)\n",
    "fm0 = QuantumCircuit(2)\n",
    "fm0.ry(training_params[0], 0)\n",
    "fm0.ry(training_params[0], 1)\n",
    "\n",
    "# Use ZZFeatureMap to represent input data\n",
    "fm1 = ZZFeatureMap(2)\n",
    "\n",
    "# Create the feature map, composed of our two circuits\n",
    "fm = fm0.compose(fm1)\n",
    "\n",
    "print(circuit_drawer(fm))\n",
    "print(f\"Trainable parameters: {training_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "866fd62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate quantum kernel\n",
    "quant_kernel = TrainableFidelityQuantumKernel(feature_map=fm, training_parameters=training_params)\n",
    "\n",
    "# Set up the optimizer\n",
    "cb_qkt = QKTCallback()\n",
    "spsa_opt = SPSA(maxiter=10, callback=cb_qkt.callback, learning_rate=0.05, perturbation=0.05)\n",
    "\n",
    "# Instantiate a quantum kernel trainer.\n",
    "qkt = QuantumKernelTrainer(\n",
    "    quantum_kernel=quant_kernel, loss=\"svc_loss\", optimizer=spsa_opt, initial_point=[np.pi / 2]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ad052373",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{   'optimal_circuit': None,\n",
      "    'optimal_parameters': {ParameterVectorElement(θ[0]): 1.5452155595419974},\n",
      "    'optimal_point': array([1.54521556]),\n",
      "    'optimal_value': 17.900589588084703,\n",
      "    'optimizer_evals': 30,\n",
      "    'optimizer_result': None,\n",
      "    'optimizer_time': None,\n",
      "    'quantum_kernel': <qiskit_machine_learning.kernels.trainable_fidelity_quantum_kernel.TrainableFidelityQuantumKernel object at 0x00000139656A7C40>}\n"
     ]
    }
   ],
   "source": [
    "# Train the kernel using QKT directly\n",
    "qka_results = qkt.fit(x_train_small_pca, y_train)\n",
    "optimized_kernel = qka_results.quantum_kernel\n",
    "print(qka_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2f60895e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy test: 0.7\n"
     ]
    }
   ],
   "source": [
    "# Use QSVC for classification\n",
    "qsvc = QSVC(quantum_kernel=optimized_kernel)\n",
    "\n",
    "# Fit the QSVC\n",
    "qsvc.fit(X_train, y_train)\n",
    "\n",
    "# Predict the labels\n",
    "labels_test = qsvc.predict(X_test)\n",
    "\n",
    "# Evalaute the test accuracy\n",
    "accuracy_test = metrics.balanced_accuracy_score(y_true=y_test, y_pred=labels_test)\n",
    "print(f\"accuracy test: {accuracy_test}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "772085aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = cb_qkt.get_callback_data()  # callback data\n",
    "K = optimized_kernel.evaluate(X_train)  # kernel matrix evaluated on the training samples\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 20\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "ax[0].plot([i + 1 for i in range(len(plot_data[0]))], np.array(plot_data[2]), c=\"k\", marker=\"o\")\n",
    "ax[0].set_xlabel(\"Iterations\")\n",
    "ax[0].set_ylabel(\"Loss\")\n",
    "ax[1].imshow(K, cmap=matplotlib.colormaps[\"bwr\"])\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a68d9fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61a9f999",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46181b84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "939763f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f58a325",
   "metadata": {},
   "outputs": [],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1e8a750",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "774b2d45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64660673",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb957cd7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60315536",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "348d36db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9390a8c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d716281f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a74c62a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fddd3e70",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd4dbb06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd3a574b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51f9bc56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d698990d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa343e13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d55f3282-5a09-4701-bd30-e590dc349c4a",
   "metadata": {},
   "source": [
    "**Variational Quantum Classifier**\n",
    "\n",
    "This is the final section and finally we can play with the VQC.\n",
    "\n",
    "We need to tune the VQC by trying different parameters until we get good results. The best parameters for VQC that is trained with QPCA dataset maybe (and by 'maybe' I mean high chance) different with the one that is trained with classical PCA. There are many good tutorials on VQC here https://github.com/qiskit-community/qiskit-community-tutorials/tree/master/machine_learning ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d3fe035-ae89-4925-907d-187feaefc413",
   "metadata": {},
   "source": [
    "**Data embedding circuits**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91bf8ba3-7aa9-4814-b226-2f33a5c6c450",
   "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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      "text/plain": [
       "<Figure size 1547.39x1545.13 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_zz_feature_map(feature_dim, feature_map_rep, ent): #this is our generic feature map for 8 components (qubits)\n",
    "    return ZZFeatureMap(feature_dimension=feature_dim, reps=feature_map_rep, entanglement=ent)\n",
    "\n",
    "fmap_zz = get_zz_feature_map(n_components_classic, 2, 'linear') #can't remember what rep is? possibly just visual rep depth\n",
    "input_params = fmap_zz.parameters\n",
    "fmap_zz.decompose().draw('mpl', scale=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b0083d62-786a-4c81-b56e-bcecdbe09771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ParameterView([ParameterVectorElement(x[0]), ParameterVectorElement(x[1]), ParameterVectorElement(x[2]), ParameterVectorElement(x[3]), ParameterVectorElement(x[4]), ParameterVectorElement(x[5]), ParameterVectorElement(x[6]), ParameterVectorElement(x[7])])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmap_zz.parameters #8 parameters to encode the 8 features of our reduced dimensionality data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "39d55540-5b19-4532-8a82-9800d66c28ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 962.109x491.633 with 1 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_two_locals(feature_dim, rotations, var_form_rep, ent): #sets up our general ansatz\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(n_components_classic, ['ry', 'rz'], 2, 'linear') #do we just do ry and rz bc we have x in cx?\n",
    "weights_params = ansatz_tl.parameters\n",
    "ansatz_tl.decompose().draw('mpl', scale=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e427580b-7d54-4de3-8b54-9b48509e5e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 962.109x491.633 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = np.random.random(len(ansatz_tl.parameters)) #get random weights from 0 to 1 for each rotation\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": 28,
   "id": "01145898-0d7f-460e-a0d6-9bf184ed32e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 845.053x491.633 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_circuit = fmap_zz.compose(ansatz_tl) #variational circuit, I am not sure if there is more to the circuit past two local?\n",
    "var_circuit.draw('mpl', scale=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe84b00-e757-4be5-9cd2-61cdc80818b5",
   "metadata": {},
   "source": [
    "**Primitives**\n",
    "\n",
    "For info, see https://docs.quantum.ibm.com/run/primitives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8c27a1e0-398c-46f5-a8dd-43e5b323eaf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#it seems like there might be better options for this? am I accessing the online simulator?\n",
    "\n",
    "# for qiskit_ibm_runtime.Options, see https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.options.Options\n",
    "qasm_sim = AerSimulator()\n",
    "options = {}\n",
    "sampler = BackendSampler(backend=qasm_sim, options=options)\n",
    "estimator = BackendEstimator(backend=qasm_sim, options=options)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d1b4ab-36d0-498b-b5b7-1a2effadeaee",
   "metadata": {},
   "source": [
    "**Quantum Neural Network**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58bfb2c-3324-4950-8e46-182fef617abb",
   "metadata": {},
   "source": [
    "**SamplerQNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "658f518b-4eba-4c1a-bd86-78802048548f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#these are the loss (or cost?) fns from the example, I think parity might work for more classes? but we only have two so can use one qubit binary?\n",
    "\n",
    "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": 31,
   "id": "87faaaee-ed57-4940-8bba-9dbce09c853e",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_circuit_with_meas = var_circuit.copy()\n",
    "var_circuit_with_meas.measure_all() #adds classical measurement (in Z?) to the circuit, I think this would be done automatically?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f24afc9a-3488-4561-aec5-d61c34bd8fd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#is this just to define the parameterised ciruit sampler as the AerSimulator?\n",
    "paramShiftSampGrad = ParamShiftSamplerGradient(sampler=sampler)\n",
    "\n",
    "sampler_qnn = SamplerQNN(sampler=sampler, #calling the sampler again\n",
    "                         circuit=var_circuit_with_meas, #this is our ciruit we built above\n",
    "                         input_params=input_params, #from fmap_zz.parameters, I think this is where the 8 points per data set are put in\n",
    "                                                           \n",
    "                         weight_params=weights_params, #this is from our two local (ansatz_tl.parameters)\n",
    "                                                           \n",
    "                         interpret=one_qubit_binary, #the one qubit binary loss fn (same = 0, dif = 1)\n",
    "                         output_shape=2, #gives us [a,b] output, not sure what this refers to? chance of 0 and 1?\n",
    "                         gradient=paramShiftSampGrad) #why are we putting the sampler in again?\n",
    "\n",
    "\n",
    "# if your embedding strategy is not a parametrized circuit\n",
    "# (e.g. amplitude encoding) you will have to do some extra work!\n",
    "# if you train your embedding as well, the trainable\n",
    "# parameters of the embedding must be added"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b70344b6-c769-497c-bec7-1e1dfce2d396",
   "metadata": {},
   "source": [
    "**Forward pass**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5eebb0fd-61d0-4707-935c-67744eb02401",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 100 samples: (100, 2)\n",
      "Output of the forward pass for first sample: [0.66503906 0.33496094]\n"
     ]
    }
   ],
   "source": [
    "# The result of the neural network on a forward pass.\n",
    "output = sampler_qnn.forward(x_total, weights) #(100, 8) the full (reduced) x data set, \n",
    "#weights = np.random.random(len(ansatz_tl.parameters)) -- our random set of starting parameters in the two local\n",
    "print(f\"Output shape for {len(x_total)} samples: {output.shape}\")\n",
    "print(f\"Output of the forward pass for first sample: {output[0]}\") #i think this is chance of outputting 0 or 1, \n",
    "                                                                   #but I am not sure if this means correct/wrong or 0,1 the pictures\n",
    "                                                                   # I don't think this knows about y? so I think it's picture 0 or 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c911a33-66d7-4966-86a0-6151899f1c61",
   "metadata": {},
   "source": [
    "**Backward pass**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0addf495-edd8-4d6e-aa9a-2d10f7c6f641",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 100 samples: (100, 2, 48)\n",
      "Output of the backward pass for first sample: [[ 0.15136719  0.12011719  0.00732422  0.00585938 -0.00048828  0.00195312\n",
      "   0.02246094  0.00585938  0.32519531  0.01171875 -0.00244141 -0.00732422\n",
      "  -0.0078125  -0.00976562 -0.0234375  -0.00585938 -0.07080078 -0.02148438\n",
      "   0.00097656  0.01757812 -0.01464844 -0.00976562  0.00048828  0.00830078\n",
      "   0.0078125   0.00439453 -0.01074219 -0.00439453 -0.01269531 -0.00390625\n",
      "  -0.01025391 -0.00244141 -0.01855469  0.00195312  0.00683594 -0.00195312\n",
      "  -0.00976562  0.00488281  0.01611328  0.00830078 -0.00390625  0.00048828\n",
      "   0.01074219 -0.0078125  -0.02929688 -0.00341797  0.00439453 -0.00244141]\n",
      " [-0.15136719 -0.12011719 -0.00732422 -0.00585938  0.00048828 -0.00195312\n",
      "  -0.02246094 -0.00585938 -0.32519531 -0.01171875  0.00244141  0.00732422\n",
      "   0.0078125   0.00976562  0.0234375   0.00585938  0.07080078  0.02148438\n",
      "  -0.00097656 -0.01757812  0.01464844  0.00976562 -0.00048828 -0.00830078\n",
      "  -0.0078125  -0.00439453  0.01074219  0.00439453  0.01269531  0.00390625\n",
      "   0.01025391  0.00244141  0.01855469 -0.00195312 -0.00683594  0.00195312\n",
      "   0.00976562 -0.00488281 -0.01611328 -0.00830078  0.00390625 -0.00048828\n",
      "  -0.01074219  0.0078125   0.02929688  0.00341797 -0.00439453  0.00244141]]\n"
     ]
    }
   ],
   "source": [
    "# The result of the neural network on a backward pass\n",
    "_, weights_grad = sampler_qnn.backward(x_total, weights) #same thing but backwards\n",
    "print(f\"Output shape for {len(x_total)} samples: {weights_grad.shape}\")\n",
    "print(f\"Output of the backward pass for first sample: {weights_grad[0]}\") \n",
    "#these weights are the parameter values for the two local?\n",
    "#but there's two sets of 48? (0 and 1 values?)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58524679-a163-4e8b-b500-738cb387258b",
   "metadata": {},
   "source": [
    "**EstimatorQNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "982a7d11-4371-41d1-8bd1-235777e65926",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parity observable: ZZZZZZZZ\n",
      "First qubit observable: IIIIIIIZ\n"
     ]
    }
   ],
   "source": [
    "obs_parity = Pauli('Z' * n_components_classic)\n",
    "obs_one_qubit = Pauli('I' * (n_components_classic - 1) + 'Z')\n",
    "print(f\"Parity observable: {obs_parity}\")\n",
    "print(f\"First qubit observable: {obs_one_qubit}\") #ahhh so these are our choices of observables to then turn into loss fn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2e90289c-89f7-4e3e-b5f6-3b1867a08f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "paramShiftEstGrad = ParamShiftEstimatorGradient(estimator=estimator) #this time we're using the estimator rather than sampler, this just does the matrices?\n",
    "\n",
    "estimator_qnn = EstimatorQNN(estimator=estimator,\n",
    "                             circuit=var_circuit,\n",
    "                             observables=obs_one_qubit, #this time we're applying the observable (IIIIIIIZ) rather than 1 qubit binary\n",
    "                                                        # i'm not sure how the 1 qubit binary in the sampler is calculated then\n",
    "                                                        # possibly something to do with output shape?\n",
    "                             \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",
   "id": "d3b6aef3-3b32-4546-ac25-c9afec639222",
   "metadata": {},
   "source": [
    "**Forward pass**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "268a6bfd-5949-4f77-becc-cf529b10feaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 100 samples: (100, 1)\n",
      "Output of the forward pass for first sample: [0.35546875]\n"
     ]
    }
   ],
   "source": [
    "# The result of the neural network on a forward pass.\n",
    "output = estimator_qnn.forward(x_total, weights)\n",
    "print(f\"Output shape for {len(x_total)} samples: {output.shape}\")\n",
    "print(f\"Output of the forward pass for first sample: {output[0]}\") \n",
    "#this time we're simply outputting the expectation value of the observable IIIIIIIZ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a662dad-1c7f-44e1-b02c-2f717e1402eb",
   "metadata": {},
   "source": [
    "**Backward pass**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d74cec27-826c-4b99-afc2-e1288447c62f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 100 samples: (100, 1, 48)\n",
      "Output of the backward pass for first sample: [[ 0.33691406  0.23144531 -0.00195312  0.00195312 -0.0078125  -0.02832031\n",
      "  -0.00097656  0.00097656  0.64355469  0.01953125 -0.00976562  0.02246094\n",
      "  -0.01171875 -0.01464844  0.04101562  0.00195312 -0.13964844  0.00390625\n",
      "  -0.00097656 -0.00292969  0.00097656 -0.00878906  0.01953125 -0.0078125\n",
      "  -0.00195312  0.02636719  0.00878906 -0.03613281 -0.02539062 -0.00390625\n",
      "  -0.04492188 -0.00976562 -0.0234375   0.01367188 -0.00585938  0.01367188\n",
      "  -0.02734375  0.00683594  0.02636719  0.01367188 -0.00195312  0.01269531\n",
      "   0.00390625  0.00488281 -0.01660156  0.00683594 -0.00097656 -0.0546875 ]]\n"
     ]
    }
   ],
   "source": [
    "# The result of the neural network on a backward pass\n",
    "_, weights_grad = estimator_qnn.backward(x_total, weights)\n",
    "print(f\"Output shape for {len(x_total)} samples: {weights_grad.shape}\")\n",
    "print(f\"Output of the backward pass for first sample: {weights_grad[0]}\")\n",
    "# i think these are directly the parameter values for two local?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85311c2a-080f-4f54-9ec1-f5be64551640",
   "metadata": {},
   "source": [
    "**Train a Neural Network Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "41f552d6-96d7-405b-82df-9db00bc44a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train #just our y data of if it's a 0 or 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "16f26f9c-0870-4e40-bb3d-d34663da6b1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 1 converted to [0 1]\n",
      "Label 0 converted to [1 0]\n"
     ]
    }
   ],
   "source": [
    "def get_one_hot_encoding(y): #this converts the labels 0,1 to vectors |0>,|1> (maybe transpose)\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": 41,
   "id": "bcb66377-7752-4379-bea0-87ddc670fc12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def spsa_callback(nfev, params, fval, stepsize, accepted=None): #this is just to pull out loss every 3 iterations\n",
    "    #doesn't use params or stepsize (i think it's hard set to 3 below)\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": 46,
   "id": "7c72ae40-d51d-4808-936f-d0c513ac4682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 8)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_small_pca.shape #previously had wrong x_train here, we want the reduced dimensionality set of 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e57c4aab-5c4e-416d-9904-d61f4482eeb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_1h.shape #2 bc we've converted into one hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8613b552-2fcb-46b4-9556-247937bee46e",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_itr = 10\n",
    "spsa_opt = SPSA(maxiter=max_itr, callback=spsa_callback) # from qiskit.algorithms.optimizers\n",
    "spsa_loss_recorder = [] #empty list to append training loss into \n",
    "initial_point = np.random.random((len(ansatz_tl.parameters),)) #random starting parameter set for our twolocal ansatz\n",
    "\n",
    "vqc = NeuralNetworkClassifier(neural_network=sampler_qnn, #SamplerQNN(sampler=sampler, circuit=var_circuit_with_meas,input_params=input_params,\n",
    "                              # weight_params=weights_params, interpret=one_qubit_binary, output_shape=2,gradient=paramShiftSampGrad) \n",
    "                              # so outputs [a,b] vector, for a|0>+b|1> from 1st qubit?\n",
    "                              one_hot=True, #does loss as vectors\n",
    "                              optimizer=spsa_opt, #above\n",
    "                              initial_point=initial_point) #random twolocal starting point\n",
    "\n",
    "vqc = vqc.fit(x_train_small_pca, y_train_1h) #run, I think the .fit runs the training?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fad41f82-3ddc-4fc5-ad5d-be1c1bfa6369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training loss')"
      ]
     },
     "execution_count": 45,
     "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) #plots fval, I'm confused about the % 3 bc this would imply only every third point?\n",
    "#maybe 3 per itteration? so 30 total?\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "73bc199b-df0d-4fed-af6c-624d80cde86d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on the train set 0.8375\n",
      "Score on the test set 0.9\n"
     ]
    }
   ],
   "source": [
    "score_train = vqc.score(x_train_small_pca, y_train_1h) #how well does it run on the training set\n",
    "score_test = vqc.score(x_test_small_pca, y_test_1h) #how well does it run on the test set (assesses how good in more general case)\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": 48,
   "id": "d45bcd1f-cfdb-43a0-8571-59783ca1ae80",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = vqc.predict(x_train_small_pca) #what's the predicted y outcome [0,1] / [1,0] for pca'ed training data\n",
    "y_test_pred = vqc.predict(x_test_small_pca) #same for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9f99d61e-c86b-4e74-8572-dae671990ddd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#corret answer mask\n",
    "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": 54,
   "id": "32082ac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "        True,  True, False, False,  True,  True,  True,  True, False,\n",
       "        True,  True,  True,  True,  True, False,  True, False,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True, False, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "        True,  True,  True, False, False,  True,  True,  True])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a839a3e0-c5da-47b0-9cd5-e2018d328116",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split x data by whether the trained algo will give correct answer\n",
    "x_all = np.concatenate((x_train_small_pca, x_test_small_pca))\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": 52,
   "id": "1cb8ead1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 8)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_b_good.shape #good 0's, all x data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3a7a37af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5717578 , 0.0197521 , 0.4566309 , 0.54267925, 0.38951346,\n",
       "       0.33407667, 0.17543308, 0.5658302 ], dtype=float32)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_b_good[0] #the 8 parameters for this data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3153ca1e-cad7-4b5f-887e-13235186a3e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x201097a7310>"
      ]
     },
     "execution_count": 51,
     "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": [
    "#plot whether the algo gives the correct outcome for each x, by the first two variables in x\n",
    "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(\"Mnist dataset classification\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1669baea-d5a6-4c00-b141-0b7e385b6cac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2004e417c70>"
      ]
     },
     "execution_count": 55,
     "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": [
    "#plot whether the algo gives the correct outcome for each x, by 3rd and 4th variable in x\n",
    "plt.scatter(x_b_good[:,2], x_b_good[:,3], c='b', marker=\".\", label=\"Good class 0\")\n",
    "plt.scatter(x_b_bad[:,2], x_b_bad[:,3], c='b', marker=\"x\", label=\"Bad class 0\")\n",
    "plt.scatter(x_r_good[:,2], x_r_good[:,3], c='r', marker=\".\", label=\"Good class 1\")\n",
    "plt.scatter(x_r_bad[:,2], x_r_bad[:,3], c='r', marker=\"x\", label=\"Bad class 1\")\n",
    "\n",
    "plt.title(\"Mnist dataset classification\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d07fc22-df36-44d2-81d4-660a6b045dfd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2469c6c8-36ce-4be3-b3ce-003e10992350",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}