{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f7e586d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "plt.rcParams.update({'font.size': 18,\n",
    "                     'xtick.labelsize' : 18,\n",
    "                     'ytick.labelsize' : 18,\n",
    "                     'legend.fontsize': 20,\n",
    "                     'axes.labelsize': 20,\n",
    "                     'axes.titlesize': 20,\n",
    "                     'figure.figsize': (8, 8)\n",
    "                    })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb55d2f5",
   "metadata": {},
   "source": [
    "## Concentric circles toy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6204f88f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 85412 characters]\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_samples = 500\n",
    "data, labels = datasets.make_circles(n_samples=n_samples, factor=0.6, noise=0.05)\n",
    "\n",
    "plt.scatter(data[:, 0], data[:, 1], c=labels, cmap=\"tab20\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1599ee1a",
   "metadata": {},
   "source": [
    "## Elongated blobs toy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "26808f66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 54948 characters]\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_state = 170\n",
    "data, labels = datasets.make_blobs(n_samples=n_samples, random_state=random_state)\n",
    "transformation = [[0.6, -0.6], [-0.4, 0.8]]\n",
    "data = np.dot(data, transformation)\n",
    "\n",
    "plt.scatter(data[:, 0], data[:, 1], c=labels, cmap=\"tab20\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b5f5c69",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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