dateset_gau_4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c7b874a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining the DataSet\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from qiskit_aer import AerSimulator\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "SEED = 123\n",
    "qasm_sim = AerSimulator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4315e36c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  3.44718421   4.81148403]\n",
      " [  3.80191292   2.78129454]\n",
      " [  4.47883723   3.98326915]\n",
      " [  3.63849962   0.04219733]\n",
      " [  3.37211469   3.38573454]\n",
      " [  4.40019203   1.86977759]\n",
      " [  6.10914344   3.09645613]\n",
      " [  2.46468648   5.41045946]\n",
      " [  1.49467386   3.01022412]\n",
      " [  0.5681573    3.39342594]\n",
      " [  2.75614778   4.40110961]\n",
      " [  5.41994664   4.54615004]\n",
      " [  5.79030423   2.77424397]\n",
      " [  5.04279664   6.10821345]\n",
      " [  2.86103975   1.55670637]\n",
      " [  7.38331541   4.58394598]\n",
      " [  3.03991      3.86606129]\n",
      " [  7.11965624   7.09258255]\n",
      " [  5.31162995   3.75444206]\n",
      " [  2.67653105   5.66287338]\n",
      " [  5.38414173   7.16521266]\n",
      " [  4.00402473   4.97329389]\n",
      " [  3.18173568   6.33548395]\n",
      " [  5.28284047   1.97954038]\n",
      " [  2.22674495   3.09808318]\n",
      " [  1.3229417   -1.00613909]\n",
      " [  1.75739386  -3.49260326]\n",
      " [  2.58645344  -2.02049102]\n",
      " [  1.55725426  -6.84729852]\n",
      " [  1.23100069  -2.75231848]\n",
      " [  2.49013314  -4.60897898]\n",
      " [  4.58316261  -3.10661072]\n",
      " [  0.11963264  -0.27254701]\n",
      " [ -1.06838534  -3.21222293]\n",
      " [ -2.20313174  -2.74289847]\n",
      " [  0.47659837  -1.50874306]\n",
      " [  3.73907236  -1.33110554]\n",
      " [  4.19266592  -3.50123841]\n",
      " [  3.27715984   0.58202361]\n",
      " [  0.60506427  -4.99241134]\n",
      " [  6.14369819  -1.28481516]\n",
      " [  0.82413469  -2.16404075]\n",
      " [  5.82078298   1.78762461]\n",
      " [  3.60641205  -2.30074582]\n",
      " [  0.37908819   0.03659565]\n",
      " [  3.69522049   1.87657798]\n",
      " [  2.00492927  -0.8079633 ]\n",
      " [  0.99783497   0.86037199]\n",
      " [  3.57115229  -4.47454756]\n",
      " [ -0.17178502  -3.104618  ]\n",
      " [ -2.87407851   3.28306891]\n",
      " [ -2.31320318   0.07305747]\n",
      " [ -1.24289187   1.97354621]\n",
      " [ -2.57158229  -4.25783548]\n",
      " [ -2.99277384   1.02876102]\n",
      " [ -1.36724084  -1.36817737]\n",
      " [  1.33484858   0.5713717 ]\n",
      " [ -4.42754383   4.23013222]\n",
      " [ -5.96126845   0.43502693]\n",
      " [ -7.42621975   1.04092221]\n",
      " [ -3.96670305   2.63420998]\n",
      " [  0.24513277   2.86353904]\n",
      " [  0.83071953   0.06190954]\n",
      " [ -0.35119374   5.33337815]\n",
      " [ -3.80085428  -1.86318643]\n",
      " [  3.34949136   2.92329966]\n",
      " [ -3.51803558   1.78822431]\n",
      " [  2.93260962   6.88980235]\n",
      " [  0.07386904   1.61173881]\n",
      " [ -4.09258815   4.62923367]\n",
      " [  0.18852024   7.00464065]\n",
      " [ -1.99363634   3.53891277]\n",
      " [ -3.2937895    5.69272436]\n",
      " [  0.02834888  -1.19462716]\n",
      " [ -4.80376241   0.57394429]\n",
      " [ -8.95750504  -6.59446843]\n",
      " [ -8.34309689 -10.11085978]\n",
      " [ -7.17062959  -8.02897867]\n",
      " [ -8.62613703 -14.85511531]\n",
      " [ -9.08752926  -9.06393899]\n",
      " [ -7.30684707 -11.68965345]\n",
      " [ -4.34685641  -9.56498389]\n",
      " [-10.65924103  -5.55701256]\n",
      " [-12.33935217  -9.71434211]\n",
      " [-13.94412592  -9.05061708]\n",
      " [-10.15441525  -7.30525778]\n",
      " [ -5.54058028  -7.05404038]\n",
      " [ -4.89910212 -10.12307173]\n",
      " [ -6.19382324  -4.34846719]\n",
      " [ -9.97273703 -12.2319087 ]\n",
      " [ -2.13992582  -6.9885759 ]\n",
      " [ -9.66292466  -8.23198865]\n",
      " [ -2.59659689  -2.64348991]\n",
      " [ -5.72819029  -8.42531882]\n",
      " [-10.29231546  -5.11981882]\n",
      " [ -5.60259619  -2.51769085]\n",
      " [ -7.99302896  -6.31420553]\n",
      " [ -9.41727538  -3.95482314]\n",
      " [ -5.77805513 -11.49953872]\n",
      " [-11.07136783  -9.56216576]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "n_samples=25\n",
    "a = 2\n",
    "b = -2\n",
    "\n",
    "x1, _ = datasets.make_gaussian_quantiles(mean = [2*a,2*a], cov=2.0, n_samples=n_samples, n_features=2, n_classes=2, random_state=SEED)\n",
    "x2, _ = datasets.make_gaussian_quantiles(mean = [1*a,1*b], cov=3.0, n_samples=n_samples, n_features=2, n_classes=2, random_state=SEED)\n",
    "x3, _ = datasets.make_gaussian_quantiles(mean = [1*b,1*a], cov=5.0, n_samples=n_samples, n_features=2, n_classes=2, random_state=SEED)\n",
    "x4, _ = datasets.make_gaussian_quantiles(mean = [4*b,4*b], cov=6.0, n_samples=n_samples, n_features=2, n_classes=2, random_state=SEED)\n",
    "\n",
    "y1 = np.array([0 for i in range(n_samples)])\n",
    "y2 = np.array([1 for i in range(n_samples)])\n",
    "y3 = np.array([2 for i in range(n_samples)])\n",
    "y4 = np.array([3 for i in range(n_samples)])\n",
    "\n",
    "x = np.concatenate((x1, x2, x3, x4))\n",
    "y = np.concatenate((y1, y2, y3, y4))\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "faee19aa-11cf-4553-87cf-8617800371b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizatin\n",
    "x[:,0] = x[:,0] / (np.max(x[:,0]) - np.min(x[:,0]))\n",
    "x[:,1] = x[:,1] / (np.max(x[:,1]) - np.min(x[:,1]))\n",
    "\n",
    "num_classes = 4\n",
    "num_features = 2\n",
    "adhoc_dimension = 2\n",
    "x_blue = x[y==0]\n",
    "x_red = x[y==1]\n",
    "x_green = x[y==3]\n",
    "x_m = x[y==4]\n",
    "\n",
    "test_ratio = 0.2\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_ratio, random_state=SEED, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "15e2147e-d483-413f-a653-b8cffdb04813",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of the dataset\n",
    "\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",
    "\n",
    "def plot_dataset(x_train, y_train,x_test, y_test):\n",
    "\n",
    "# train_features, train_labels, test_features, test_labels\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.ylim(-1, 1)\n",
    "    plt.xlim(-1, 1)\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 train plot\n",
    "    plot_features(plt, x_train,  y_train, 0, \"s\", \"w\", \"b\", \"A train\")\n",
    "\n",
    "    # B train plot\n",
    "    plot_features(plt, x_train,  y_train, 1, \"o\", \"w\", \"r\", \"B train\")\n",
    "\n",
    "    # C train plot\n",
    "    plot_features(plt, x_train,  y_train, 2, \"v\", \"w\", \"g\", \"C train\")\n",
    "\n",
    "    # D train plot\n",
    "    plot_features(plt, x_train,  y_train, 3, \"D\", \"w\", \"m\", \"D train\")\n",
    "\n",
    "    # A test plot\n",
    "    plot_features(plt, x_test, y_test, 0, \"s\", \"b\", \"w\", \"A test\")\n",
    "\n",
    "    # B test plot\n",
    "    plot_features(plt, x_test, y_test, 1, \"o\", \"r\", \"w\", \"B test\")\n",
    "\n",
    "    # C test plot\n",
    "    plot_features(plt, x_test, y_test, 2, \"v\", \"g\", \"w\", \"A test\")\n",
    "\n",
    "    # D test plot\n",
    "    plot_features(plt, x_test, y_test, 3, \"D\", \"m\", \"w\", \"B test\")\n",
    "\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\", borderaxespad=0.0)\n",
    "    plt.title(\"Ad hoc dataset\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_dataset(x_train, y_train,x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c936bee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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