Perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3d850ea-a18d-464a-b38f-bd01fbd598da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec7874de-c20b-4e31-aac3-bdecb6978815",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Perceptron:\n",
    "    \"\"\"Perceptron classifier.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    eta : float\n",
    "        Learning rate (between 0.0 and 1.0)\n",
    "    n_inter : int\n",
    "        Passes over the training dataset.\n",
    "    random_state : int\n",
    "        Random number generator seed for random weight initialization.\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    w_ : 1d-array\n",
    "        Weights after fitting.\n",
    "    b_ : Scalar\n",
    "        Bias unit after fitting\n",
    "    errors_ : list\n",
    "        Number of misclassifications (updates) in each epoch\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=50, random_state=1):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "        self.random_state = random_state\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"Fit training data.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array_like}, shape = [n_examples, n_features]\n",
    "            Training vectors, where n_examples is the number of examples and n_features is the number of features.\n",
    "        y : array_like, shape = [n_examples]\n",
    "            Target values.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "        \n",
    "        \"\"\"\n",
    "        rgen = np.random.RandomState(self.random_state)\n",
    "        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=X.shape[1])\n",
    "        self.b_ = np.float_(0.)\n",
    "        self.errors_ = []\n",
    "\n",
    "        for _ in range(self.n_iter):\n",
    "            errors = 0\n",
    "            for xi, target in zip(X, y):\n",
    "                update = self.eta * (target - self.predict(xi))\n",
    "                self.w_ += update * xi\n",
    "                self.b_ += update\n",
    "                errors += int(update != 0.0)\n",
    "            self.errors_.append(errors)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_) + self.b_\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step.\"\"\"\n",
    "        return np.where(self.net_input(X) >= 0.0, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7cc6a57e-8fdb-42d6-aad7-547afc9929a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From URL:  https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "s = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "print('From URL: ',s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72d20e24-393e-45ed-a124-c26011196008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       0    1    2    3               4\n",
       "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "149  5.9  3.0  5.1  1.8  Iris-virginica"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(s, header=None, encoding='utf-8')\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6b8f30c-d84e-4e20-bf87-51976c2210d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "y = df.iloc[0:100, 4].values\n",
    "y = np.where(y=='Iris-setosa', 0,1)\n",
    "X = df.iloc[0:100, [0,2]].values\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "689dc1eb-9cd2-4e68-8eb2-54869f4c628e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Ljz/+uBYvXqwZM2ZU+t0TJkxQeHi40tLS9PXXX2vNmjW69dZbdfXVV6t58+Ze9RMAILiRqBhs8uTJ+vnnnzVkyBC1aNFCUtnZjnXr1mnbtm3q06ePevToof/7v/9zzq9KdHS05s6dq9TUVJ133nnauXOn3nvvPdWrV3EXCA0N1QcffKC4uDgNGzZM3bp100MPPaSQkBBJ0siRIzV//nw9+uij6tq1qxYuXKj09HT179+/0u+OjIzUihUrdOjQIZ133nkaPXq0Bg4cqH/84x/edRAAIOg5LMuy/B1ETRUUFCg2Nlb5+fmKiYlxmXfixAllZ2erbdu2Cg8P93jZ3IJoJm+3K+CtzZvL6iidzqZNUkqK7+JYuVLKy6t6flycNHiw776/nD8KY5rKm74wZb+SamebVnf8PhV1VKoQtPejA/CKCUW1Vq6ULrro9O0++MC3yQp/0P3K274wYb+SzNymJCrVCPb/sQB4zoQ/Yqo7k1KTdjXlr8KYJvK2L0zYryQztymJCgB4iD9i4AvsV5VjMC0AADBW0CcqATxWGJVgewJA3RK0iUqDBg0kSceOHfNzJLBT+fYs374AgOAWtGNUQkJC1KhRI+X9/9FkkZGRzicRI/BYlqVjx44pLy9PjRo1ctZ0AQAEt6BNVCQpPj5ekpzJCgJfo0aNnNsVACR76n5QD8ZcQZ2oOBwOJSQkKC4uTr/88ou/w4GXGjRowJkUQJK7P2e+/tkzofaHHXU/7FiGCX1hBxPXI6gTlXIhISEc4AAEje7d7W1XUybU/rCj7ocdyzChL+xg4nrUiUQFAOAbph94a1Ow9IVp6xG0d/0AAIDAR6ICAACMRaICAACMRaICAACMxWBaoA4wpUYE9S5+tXJl9U83jouTBg/2bQwm9KUJMcBsJCpAkLOjRoQpcZiyLt5auVK66KLTt/vgg8qTFTtqXZjQl6bULzGxdgh+RaICBDk7akSYEocp6+Itd4tlV9XOjloXJvSlKfVLTKwdgl+RqABAAOKg+Ss7+oL+NBeDaQEAgLFIVAAAgLFIVAAAgLFIVAAAgLEYTAvALdS7QGWCZb+gxo+5SFSAIGdKzQ3qXfwqLs7edjVhwn5hyvakxo/ZSFSAIGdKzQ3qXfxq8OCyYm7+rExrwn5hyvakxo/ZSFSAOsCUAzf1Ln7l6/L47jChL02IAWZjMC0AADAWiQoAADAWiQoAADAWiQoAADAWg2kBoI4yoe6HCTHAbCQqAE7LlHoXsI8JtXFMqT1CjR+zkagAOC1T6l3APibUxjGl9gg1fsxGogLALfzAojLBsl9Q48dcDKYFAADGIlEBAADGIlEBAADGIlEBAADGIlEBAADG4q4fALXGjuJeK1dKeXlVz4+L8/2TiYOhSJkJdT9MiAHmI1EBUCvsKO61cqV00UWnX8YHH/guWTGlSJm3TKj7YUIMMB+JCoBaYUdxr+rOpNSkXU2YUqTMDiYkACbEALMxRgUAABiLRAUAABiLRAUAABiLRAUAABiLRAUAABiLu34A1CnBUAMFqEtIVADUioMHvW/3yy/uLaOqdnbUQKFIGVC7SFQA1IqmTb1v1727e8uoqp0dNVAoUgbULhIVAPAQSQhQexhMCwAAjEWiAgAAjEWiAgAAjEWiAgAAjMVgWgBuWbmy+qcSx8VJgwfXXjyBztt6LtSDQV1hTKLy0EMPadasWZo+fbrmzZvn73AA/MbKldJFF52+3QcfVJ2s2FF/xNtl2FHLxQ7e1nOxox4MECiMSFQ2btyohQsXqru7RRIA1KrqzqS4286O+iPeLsOOWi528Laeix31YIBA4fdEpbCwUBMmTNCzzz6r+++/39/hAPAhO/665wwBULf4fTDtLbfcouHDh2vQoEGnbVtUVKSCggKXFwAACF5+PaPy+uuva/Pmzdq4caNb7efMmaN7773Xx1EBAABT+O2Myq5duzR9+nS98sorCg8Pd+szs2bNUn5+vvO1a9cuH0cJAAD8yW9nVDZt2qS8vDylpKQ4p5WUlOijjz7SP/7xDxUVFSkkJMTlM2FhYQoLC6vtUAEAgJ/4LVEZOHCgtm7d6jLtmmuuUefOnTVz5swKSQoQqKh38Sv6AoCn/JaoREdH66yzznKZ1rBhQzVt2rTCdCBQBUu9i7g479uZ0Bem1FHxth6MHTVpgEDh99uTgWAWLPUuBg8uK+bmTWVaE/rClDoq3taDsaMmDRAojEpU1q5d6+8QAFSB8vj28jaJIAlBXeH3OioAAABVIVEBAADGIlEBAADGIlEBAADGMmowLQBzUQMFgD+QqAA+FCz1LuyogWJCX5gQAwDPkKgAPhQs9S7sqIFiQl+YEAMAz5CoAD7GQe9XJvSFCTEAcB+DaQEAgLFIVAAAgLFIVAAAgLFIVAAAgLFIVAAAgLFIVACcFvVHAPiLW7cnL1261OMFDx48WBERER5/DgAAoJxbicrIkSM9WqjD4VBWVpbatWtXk5gAGMaOgm8AUBNuX/rZt2+fSktL3XpFRkb6MmYAAFBHuJWopKWleXQZ56qrrlJMTEyNgwIAAJDcvPSTnp7u0UIXLFhQo2AAAAB+i7t+AACAsTx+KOGJEyf097//XWvWrFFeXp5KS0td5m/evNm24AAAQN3mcaIyefJkffDBBxo9erTOP/98ORwOX8QFIAhlZVV/Z1B0NE83BuDK40TlP//5j9577z317t3bF/EAMJAdBd+ysqTk5NMvY9s2khUAv/I4UWnZsqWiKT8J1CkdO5YlEN6cDaEWC4Ca8DhReeyxxzRz5kw988wzat26tS9iAmAgznIA8AePE5XU1FSdOHFC7dq1U2RkpBo0aOAy/9ChQ7YFBwAA6jaPE5Xx48drz549evDBB9W8eXMG0wIAAJ/xOFH55JNP9Omnn+rss8/2RTwAAABOHhd869y5s44fP+6LWAAAAFx4fEbloYce0u23364HHnhA3bp1qzBGhWf8wCTU7Qg+bFOgbvE4URk6dKgkaeDAgS7TLcuSw+FQSUmJPZEBXqJuh1moxQKgJjxOVNasWeOLOADbUbfDLNRiAVATHicq/fr180UcAOoAznIA8JTHg2nT09P15ptvVpj+5ptv6sUXX7QlKAAAAKkGicqcOXPUrFmzCtPj4uL04IMP2hIUAACAVINEJScnR23btq0wvXXr1srJybElKAAAAKkGiUpcXJy++uqrCtO3bNmipk2b2hIUAACAVINEZfz48Zo2bZrWrFmjkpISlZSU6MMPP9T06dN15ZVX+iJGAABQR3l81899992nnTt3auDAgapfv+zjpaWlmjhxImNUYBQ76nbALGxToO5xWJZl1eSDWVlZyszMVEREhLp166bWrVvbHdtpFRQUKDY2Vvn5+VTERaWoYhp82KZA4PPk+F3jRMUEJCoAAAQeT47fbo1R+dOf/qSjR4+6HcCsWbN06NAht9sDAABUxq1EZf78+Tp27JjbC33qqad0+PDhmsYEAAAgyc3BtJZlKTk5WQ6Hw62FenL2BQAAoCpuJSrp6ekeL7h58+YefwYAAOC33EpU0tLSfB0HUIEdd3dwhwgABDaP66gAtSErS0pOPn27bduqTjTsWAYAwL88rkwL1IbqzoK4286OZQAA/ItEBQAAGItEBQAAGItEBQAAGMvjwbRHjx7VQw89pNWrVysvL0+lpaUu83/88UfbggMAAHWbx4nKlClTtG7dOl199dVKSEhwuwgcAACApzxOVJYvX65ly5apd+/evogHAADAyeMxKo0bN1aTJk18EQvgFB3tfTs7lgEA8C+HZVmWJx94+eWX9c477+jFF19UZGSkr+JyiyePiUbgoTItAAQnT47fbl366dGjh8tYlO3bt6t58+Zq06aNGjRo4NJ28+bNNQgZqMiOBIIkBAACm1uJysiRI30cBgAAQEUeX/oxCZd+AAAIPJ4cvz0eTNuuXTsdPHiwwvTDhw+rXbt2ni4OAACgSh4nKjt37lRJSUmF6UVFRdq9e7ctQQEAAEge1FFZunSp879XrFih2NhY5/uSkhKtXr1abdu2tTc6AABQp7mdqJQPqHU4HEpLS3OZ16BBA7Vp00aPPfaYrcEBAIC6ze1EpfyZPm3bttXGjRvVrFkznwUFAAAg1aCEfnZ2ti/iAAAAqMDjROXJJ5+sdLrD4VB4eLg6dOigvn37KiQkxOvgAABA3eZxovLEE0/owIEDOnbsmBo3bixJ+vnnnxUZGamoqCjl5eWpXbt2WrNmjZKSkmwPGAAA1B0e35784IMP6rzzzlNWVpYOHjyogwcPatu2bbrgggs0f/585eTkKD4+Xn/84x99ES8AAKhDPK5M2759e7311ls655xzXKZ/+eWXGjVqlH788Ud98sknGjVqlHJzc+2MtQIq0wIAEHh8Wpk2NzdXJ0+erDD95MmT2rdvnySpRYsWOlLdI2sBAADc4PEYlQEDBuiGG27Qc889px49ekgqO5ty00036cILL5Qkbd26leJvCApZWVJ1OXd0NE9oBgBf8viMyvPPP68mTZro3HPPVVhYmMLCwpSamqomTZro+eeflyRFRUW5VfxtwYIF6t69u2JiYhQTE6OePXtq+fLlnq8F4ANZWVJysnTuuVW/kpPL2gEAfMPjMyrx8fFauXKlvv/+e23btk2S1KlTJ3Xq1MnZZsCAAW4tKzExUQ899JA6duwoy7L04osvasSIEfryyy/VtWtXT0MDbOXu1UuucgKA73icqJTr3LmzOnfu7NWXX3rppS7vH3jgAS1YsECfffYZiQoAAPA8USkpKVFGRoZWr16tvLw8Z2n9ch9++GGNAikpKdGbb76po0ePqmfPnpW2KSoqUlFRkfN9QUFBjb4LAAAEBo8TlenTpysjI0PDhw/XWWedJYfD4VUAW7duVc+ePXXixAlFRUXp7bffVpcuXSptO2fOHN17771efR8AAAgcHtdRadasmf71r39p2LBhtgRQXFysnJwc5efna9GiRXruuee0bt26SpOVys6oJCUlUUcFPrF5c9mA2dPZtElKSfF9PAAQLDypo+LxGZXQ0FB16NChxsFVt7xzzz1XGzdu1Pz587Vw4cIKbcvvMgIAAHWDx7cn33777Zo/f748PBHjttLSUpezJgAAoO7y+IzKxx9/rDVr1mj58uXq2rWrGjRo4DJ/8eLFbi9r1qxZuvjii9WqVSsdOXJEr776qtauXasVK1Z4GhZgu+hoe9sBADzncaLSqFEjXX755bZ8eV5eniZOnKjc3FzFxsaqe/fuWrFihQYPHmzL8gFvdOwobdtGZVoA8CePB9OahIcSAgAQeHz6UEKp7AGEq1at0sKFC50PH9y7d68KCwtrsjgAAIBKeXzp56efftLQoUOVk5OjoqIiDR48WNHR0Xr44YdVVFSkZ555xhdxAgCAOsjjMyrTp09Xamqqfv75Z0VERDinX3755Vq9erWtwQEAgLrN4zMq69ev1yeffKLQ0FCX6W3atNGePXtsCwwAAMDjMyqlpaUqKSmpMH337t2K5j5NAABgI48TlYsuukjz5s1zvnc4HCosLNTs2bNtK6sPAAAg1eD25N27d2vIkCGyLEtZWVlKTU1VVlaWmjVrpo8++khxcXG+irUCbk8GACDweHL8rlEdlZMnT+r111/XV199pcLCQqWkpGjChAkug2trA4kKAACBx6cPJZSk+vXr66qrrqpRcAAAAO5yK1FZunSp2wu87LLLahwMAADAb7mVqIwcOdKthTkcjkrvCAIAAKgJtxKV0tJSX8cBAABQQY2e9QMAAFAbSFQAAICxSFQAAICxSFQAAICxSFQAAICx3Lrrp6CgwO0FUiEWAADYxa1EpVGjRnI4HNW2sSyLOioAAMBWbiUqa9as8XUcAAAAFbiVqPTr18/XcQAAAFRQo4cSStKxY8eUk5Oj4uJil+ndu3f3OigAAACpBonKgQMHdM0112j58uWVzmeMCgAAsIvHtyffdtttOnz4sD7//HNFRETo/fff14svvqiOHTt69JRlAACA0/H4jMqHH36od955R6mpqapXr55at26twYMHKyYmRnPmzNHw4cN9EScAAKiDPD6jcvToUcXFxUmSGjdurAMHDkiSunXrps2bN9sbHQAAqNM8TlQ6deqkH374QZJ09tlna+HChdqzZ4+eeeYZJSQk2B4gAACouzy+9DN9+nTl5uZKkmbPnq2hQ4fqlVdeUWhoqDIyMuyODwAA1GEOy7IsbxZw7Ngxff/992rVqpWaNWtmV1xuKSgoUGxsrPLz8yndDwBAgPDk+O3xpZ+//e1vOnbsmPN9ZGSkUlJS1LBhQ/3tb3/zPFoAAIAqeHxGJSQkRLm5uc4BteUOHjyouLi4Wq2jwhkVAAACj0/PqJQ/fPBUW7ZsUZMmTTxdHAAAQJXcHkzbuHFjORwOORwOJScnuyQrJSUlKiws1I033uiTIAEAQN3kdqIyb948WZala6+9Vvfee69iY2Od80JDQ9WmTRv17NnTJ0ECAIC6ye1EJS0tTZLUtm1b9e7dW/Xr1/h5hgAAAG7xeIxKv3799NNPP+nuu+/W+PHjlZeXJ0lavny5vvnmG9sDBAAAdZfHicq6devUrVs3ff7551q8eLEKCwsllQ2mnT17tu0BAgCAusvjROXOO+/U/fffr5UrVyo0NNQ5/cILL9Rnn31ma3AAAKBu8zhR2bp1qy6//PIK0+Pi4vS///3PlqAAAACkGiQqjRo1cj7r57e+/PJLtWzZ0pagAAAApBokKldeeaVmzpypffv2yeFwqLS0VBs2bNCMGTM0ceJEX8QIAADqKI8TlQcffFCdO3dWUlKSCgsL1aVLF/Xt21e9evXS3Xff7YsYAQBAHVXjpyfv2rVLW7duVWFhoXr06KGOHTvaHdtp8awfAAACjyfHb7ertpWWluqRRx7R0qVLVVxcrIEDB2r27NmKiIjwOmAAAIDKuH3p54EHHtBf/vIXRUVFqWXLlpo/f75uueUWX8YGAADqOLcTlX/96196+umntWLFCi1ZskTvvvuuXnnlFZWWlvoyPgAAUIe5najk5ORo2LBhzveDBg2Sw+HQ3r17fRIYAACA24nKyZMnFR4e7jKtQYMG+uWXX2wPCgAAQPJgMK1lWZo0aZLCwsKc006cOKEbb7xRDRs2dE5bvHixvRECAIA6y+1EJS0trcK0q666ytZgAAAAfsvtRCU9Pd2XcQAAAFTgcWVaAACA2kKiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjEWiAgAAjOXXRGXOnDk677zzFB0drbi4OI0cOVI//PCDP0MCAAAG8Wuism7dOt1yyy367LPPtHLlSv3yyy+66KKLdPToUX+GBQAADOGwLMvydxDlDhw4oLi4OK1bt059+/Y9bfuCggLFxsYqPz9fMTExtRAhAADwlifH7/q1FJNb8vPzJUlNmjSpdH5RUZGKioqc7wsKCmolLgAA4B/GDKYtLS3Vbbfdpt69e+uss86qtM2cOXMUGxvrfCUlJdVylAAAoDYZc+nnpptu0vLly/Xxxx8rMTGx0jaVnVFJSkri0g8AAAEk4C79TJ06Vf/5z3/00UcfVZmkSFJYWJjCwsJqMTIAAOBPfk1ULMvSrbfeqrfffltr165V27Zt/RkOfKGkRFq/XsrNlRISpD59pJAQf0fluWBZDwAIMH5NVG655Ra9+uqreueddxQdHa19+/ZJkmJjYxUREeHP0GCHxYul6dOl3bt/nZaYKM2fL11xhf/i8lSwrAcABCC/jlFxOByVTk9PT9ekSZNO+3luTzbY4sXS6NHSqbtX+TZftCgwDvLBsh4AYBBPjt/GDKatCRIVQ5WUSG3auJ6B+C2Ho+yMRHa22ZdPgmU9AMAwnhy/jbk9GUFk/fqqD+5S2dmJXbvK2pksWNYDAAIYiQrsl5trbzt/CZb1AIAARqIC+yUk2NvOX4JlPQAggJGowH59+pSN3ahisLQcDikpqaydyYJlPQAggJGowH4hIWW37koVD/Ll7+fNM38AarCsBwAEMBIV+MYVV5Tdutuypev0xMTAuqU3WNYDAAIUtyfDt4KlomuwrAcAGCDgnvWDIBYSIvXv7+8ovBcs6wEAAYZLPwAAwFgkKgAAwFgkKgAAwFgkKgAAwFgkKgAAwFjc9QO4I1huTy4ulp5+WtqxQ2rfXrr5Zik01N9R1UywbBMA1SJRAU5n8WJp+nTXJyknJpZVrQ2kgm9//rP0+ONlB/hyM2ZIf/qTNHeu/+KqiWDZJgBOi0s/QHUWL5ZGj3Y9IErSnj1l0xcv9k9cnvrzn6VHHnFNUqSy9488UjY/UATLNgHgFirTAlUpKZHatKl4QCzncJT9FZ+dbfYlh+JiKTKyYpLyWyEh0rFj5l8GCpZtAtRxnhy/OaMCVGX9+qoPiJJkWdKuXWXtTPb009UnKVLZ/Kefrp14vBEs2wSA20hUgKrk5trbzl927LC3nT8FyzYB4DYSFaAqCQn2tvOX9u3tbedPwbJNALiNMSpAVcrHQ+zZU3ZJ4VSBMh4iGMeoBPo2Aeo4xqjAHiUl0tq10muvlf17unEOlSkulubNk269tezf4mJ7Y3TX8ePS1KnSkCFl/x4/fvrPhISU3e5anXnzzD8ghoaW3YJcnT/9yfwkRXLdJg6H67zy94GwTQC4zwpg+fn5liQrPz/f36EEn7fesqzERMsq+7u17JWYWDbdXXfcYVkhIa7LCAkpm16bRoxwjaH8NWKEe583ZT28FSzrYVmV759JSZ7tnwD8xpPjN5d+UFF5nYpTd43yv1gXLTp9Ua3yuh1VueOO2ikyNnKk9M47Vc8fMUJasqTq+Xb0hUmoTAvAAJ4cv0lU4MqOOhWmjIk4frwsjtM5dkyKiKg4nZodAOATjFFBzdlRp8KUuh133OFdO2p2AIDfkajAlR11Kkyp25GV5V07anYAgN+RqMCVHXUqTKnb0bGjd+2o2QEAfscYFbiyo05FsI1RoWYHANiKMSooU5M6KHbUqbC7bkdNaqBIZcnHiBHVtxkxovIkRbK/ZocdNWW8rW1jSl0bO9hR5weA+Xx6o7SPUUelGt7WQbGjToUddTu8rYFiWZZ13nmVL+O882rn85ZlT194u02DvY6Kp3V+APiNJ8dvEpVg9NZbluVwVDywOhxlL3d/zE+etKw1ayzr1VfL/j150vNYioos64knLGvq1LJ/i4rc/2xVSYonyUpVfVHeH6frCztiuOOO6pfhTqLg7Ta1IwZT2LV/A/AbCr7VZcFS+8Pb8SWS931hRwx2jNfxdj1MGTNkh2DZv4E6jjEqdVmw1P7wtgaK5H1f2BGDHTVlvF0PU+ra2CFY9m8AbiNRCTbBUvvD2xookvd9YUcMdtSU8XY9TKlrY4dg2b8BuI1EJdgES+0Pb2ugSN73hR0x2FFTxtv1MKWujR2CZf8G4DbGqASbYKn9YecYlZr2hWljVGq6HsE4RiXQ92+gjmOMir/5s77Db2t/VMWT2h/eqmndDm9roEje94UdMdhRU8bbei5217UxZf+2o7YNAPP5+A4knzLy9mRT6jvYUX/EW3bU7bCjhom3fdG+feWfb9++9mKwLO9r25hQy8UudtT5AeA31FHxF1PqO5hQM8OXtUPcrYFiRxymrEc5b2vbeFPXxpT9u5wddX4A+AV1VPzBlPoOJoxHMKF2iB1xmLIeJgiW9QBgBMao+IMp9R1MqJlhQu0QO+IwZT1MECzrASDgkKjYxZT6DibUzDChdogdcZiyHiYIlvUAEHBIVOxiSn0HE2pmmFA7xI44TFkPEwTLegAIOIxRsYsp9R2CbYyKN/1p0hgVf+8X3gqW9QBgBMao+IPd9UtqWqvCzpoZ/ozBjnoZ3sZhynrYqabb1LT6PADqDh/fgeRTxt2ebFm+q5fhaa0Kb2tmmBBDVXF4Wi/D221iynp4y5RtCqDO4/Zkf/nzn6VHHql6/h13SHPnVr+MxYul0aMrnl4v/+t70SLpiivci6e4uOyOlB07ysZR3Hyze2dSTIjht0pKyu4myc0tGwPRp4/7f7lXtS5S2fq4uy7+Xg9v2bFN7dwvANRpnhy/SVTsEiw1N0yIwS7BtC7esKMf6EsANmKMij8ES80NE2KwSzCtizfs6Af6EoCfkKjYJVhqbpgQg12CaV28YUc/0JcA/IRExS7BUnPDhBjsEkzr4g07+oG+BOAnjFGxS7DU3DAhBrsE07p4w45+oC8B2IgxKv4QLDU3TIjBLsG0Lt6wox/oSwB+QqJip7lzy25BPvXHOiTEvVuTpbLbOxctklq2dJ2emFh7t3+aEINdqlqXli0Db128Ycc2Dab9Qqp58TsAtYpLP74Q6DU3TIrBDosXS9OmlV22KNeypfTkk4F3cPWWHds0GPaLxYul6dNd72RKTCw7a1TX9gnAD6ijApSjSBlOxT4B+B2JCiBRpAwVsU8ARmAwLSBRpAwVsU8AAYdEBcGLImU4FfsEEHBIVBC8KFKGU7FPAAGHRAXBq0+fsvEGp9b9KOdwSElJZe1QN7BPAAGHRKUy1FcIDhQpw6nYJ4CAQ6JyqsWLy+4KGDBA+sMfyv5t06ZsOgJPsBUpg/fYJ4CAwu3Jv0V9heAVDEXKYC/2CcBvqKNSE9RXAACgVlBHpSaorwAAgHFIVMpRXwEAAOOQqJSjvgIAAMYhUSlHfQUAAIxDolIuGOsrUA8GABDg/JqofPTRR7r00kvVokULORwOLVmyxJ/hBFd9BerBAACCgF8TlaNHj+rss8/WU0895c8wXF1xhbRzp7RmjfTqq2X/ZmcHXpIyenTFu5j27CmbTrICAAgQxtRRcTgcevvttzVy5Ei3P2N7wbdgQD0YAIDhgraOSlFRkQoKClxeOAX1YAAAQSSgEpU5c+YoNjbW+UpKSvJ3SOahHgwAIIgEVKIya9Ys5efnO1+7du3yd0jmoR4MACCI1Pd3AJ4ICwtTWFiYv8MwW3k9mD17Kj5cUfp1jAr1YAAAASCgzqjADcFYDwYAUGf5NVEpLCxUZmamMjMzJUnZ2dnKzMxUTk6OP8MKfMFUDwYAUKf59fbktWvXasCAARWmp6WlKSMj47Sf5/bk0ygpKbu7Jze3bExKnz6cSQEA+J0nx2+/jlHp37+/DCnjEpxCQqT+/f0dBQAANcYYFQAAYCwSFQAAYCwSFQAAYCwSFQAAYCwSFQAAYCwSFQAAYCwSFQAAYCwSFQAAYCwSFQAAYKyAenryqcqr2hYUFPg5EgAA4K7y47Y71ekDOlE5cuSIJCkpKcnPkQAAAE8dOXJEsbGx1bbx60MJvVVaWqq9e/cqOjpaDofD3+HYrqCgQElJSdq1axcPXbQB/Wkf+tJe9Kd96Et7+ao/LcvSkSNH1KJFC9WrV/0olIA+o1KvXj0lJib6Owyfi4mJ4X84G9Gf9qEv7UV/2oe+tJcv+vN0Z1LKMZgWAAAYi0QFAAAYi0TFYGFhYZo9e7bCwsL8HUpQoD/tQ1/ai/60D31pLxP6M6AH0wIAgODGGRUAAGAsEhUAAGAsEhUAAGAsEhUAAGAsEhVDPPTQQ3I4HLrtttuqbJORkSGHw+HyCg8Pr70gDXbPPfdU6JvOnTtX+5k333xTnTt3Vnh4uLp166b33nuvlqI1m6d9yX55env27NFVV12lpk2bKiIiQt26ddMXX3xR7WfWrl2rlJQUhYWFqUOHDsrIyKidYA3naV+uXbu2wv7pcDi0b9++WozaTG3atKm0b2655ZYqP+OP382ArkwbLDZu3KiFCxeqe/fup20bExOjH374wfk+GB8dUFNdu3bVqlWrnO/r16969/7kk080fvx4zZkzR5dccoleffVVjRw5Ups3b9ZZZ51VG+EazZO+lNgvq/Pzzz+rd+/eGjBggJYvX64zzjhDWVlZaty4cZWfyc7O1vDhw3XjjTfqlVde0erVqzVlyhQlJCRoyJAhtRi9WWrSl+V++OEHl8qqcXFxvgw1IGzcuFElJSXO919//bUGDx6sMWPGVNreb7+bFvzqyJEjVseOHa2VK1da/fr1s6ZPn15l2/T0dCs2NrbWYgsks2fPts4++2y3248dO9YaPny4y7QLLrjAuuGGG2yOLPB42pfsl9WbOXOm9fvf/96jz/z5z3+2unbt6jJt3Lhx1pAhQ+wMLeDUpC/XrFljSbJ+/vln3wQVRKZPn261b9/eKi0trXS+v343ufTjZ7fccouGDx+uQYMGudW+sLBQrVu3VlJSkkaMGKFvvvnGxxEGjqysLLVo0ULt2rXThAkTlJOTU2XbTz/9tEKfDxkyRJ9++qmvwwwInvSlxH5ZnaVLlyo1NVVjxoxRXFycevTooWeffbbaz7B/Vq4mfVnunHPOUUJCggYPHqwNGzb4ONLAU1xcrJdfflnXXnttlWdE/bVfkqj40euvv67Nmzdrzpw5brXv1KmTXnjhBb3zzjt6+eWXVVpaql69emn37t0+jtR8F1xwgTIyMvT+++9rwYIFys7OVp8+fXTkyJFK2+/bt0/Nmzd3mda8eXOuW8vzvmS/rN6PP/6oBQsWqGPHjlqxYoVuuukmTZs2TS+++GKVn6lq/ywoKNDx48d9HbKxatKXCQkJeuaZZ/TWW2/prbfeUlJSkvr376/NmzfXYuTmW7JkiQ4fPqxJkyZV2cZvv5s+PV+DKuXk5FhxcXHWli1bnNNOd+nnVMXFxVb79u2tu+++2wcRBraff/7ZiomJsZ577rlK5zdo0MB69dVXXaY99dRTVlxcXG2EF1BO15enYr901aBBA6tnz54u02699Vbrd7/7XZWf6dixo/Xggw+6TFu2bJklyTp27JhP4gwENenLyvTt29e66qqr7Awt4F100UXWJZdcUm0bf/1uckbFTzZt2qS8vDylpKSofv36ql+/vtatW6cnn3xS9evXdxngVJUGDRqoR48e2r59ey1EHFgaNWqk5OTkKvsmPj5e+/fvd5m2f/9+xcfH10Z4AeV0fXkq9ktXCQkJ6tKli8u0M888s9rLaVXtnzExMYqIiPBJnIGgJn1ZmfPPP5/98zd++uknrVq1SlOmTKm2nb9+N0lU/GTgwIHaunWrMjMzna/U1FRNmDBBmZmZCgkJOe0ySkpKtHXrViUkJNRCxIGlsLBQO3bsqLJvevbsqdWrV7tMW7lypXr27Fkb4QWU0/XlqdgvXfXu3dvljihJ2rZtm1q3bl3lZ9g/K1eTvqxMZmYm++dvpKenKy4uTsOHD6+2nd/2S5+er4FHTr30c/XVV1t33nmn8/29995rrVixwtqxY4e1adMm68orr7TCw8Otb775xg/RmuX222+31q5da2VnZ1sbNmywBg0aZDVr1szKy8uzLKtiX27YsMGqX7++9eijj1rfffedNXv2bKtBgwbW1q1b/bUKxvC0L9kvq/ff//7Xql+/vvXAAw9YWVlZ1iuvvGJFRkZaL7/8srPNnXfeaV199dXO9z/++KMVGRlp3XHHHdZ3331nPfXUU1ZISIj1/vvv+2MVjFGTvnziiSesJUuWWFlZWdbWrVut6dOnW/Xq1bNWrVrlj1UwTklJidWqVStr5syZFeaZ8rtJomKQUxOVfv36WWlpac73t912m9WqVSsrNDTUat68uTVs2DBr8+bNtR+ogcaNG2clJCRYoaGhVsuWLa1x48ZZ27dvd84/tS8ty7LeeOMNKzk52QoNDbW6du1qLVu2rJajNpOnfcl+eXrvvvuuddZZZ1lhYWFW586drX/+858u89PS0qx+/fq5TFuzZo11zjnnWKGhoVa7du2s9PT02gvYYJ725cMPP2y1b9/eCg8Pt5o0aWL179/f+vDDD2s5anOtWLHCkmT98MMPFeaZ8rvpsCzL8u05GwAAgJphjAoAADAWiQoAADAWiQoAADAWiQoAADAWiQoAADAWiQoAADAWiQoAADAWiQoAADAWiQoAjzkcDi1ZsqTK+f3799dtt91Wa/FUZ+3atXI4HDp8+LDbn9m5c6ccDoccDofOOeccn8UmSRkZGc7vMqXPAJOQqAAB4sCBA7rpppvUqlUrhYWFKT4+XkOGDNGGDRv8HZox7E6QVq1aVeEhbHYbN26ccnNz6/wDB4Gq1Pd3AADcM2rUKBUXF+vFF19Uu3bttH//fq1evVoHDx70d2hBq2nTpmratKlPvyMiIkIREREKDQ316fcAgYozKkAAOHz4sNavX6+HH35YAwYMUOvWrXX++edr1qxZuuyyy1zaTZkyRWeccYZiYmJ04YUXasuWLc7599xzj8455xwtXLhQSUlJioyM1NixY5Wfn+9ss3HjRg0ePFjNmjVTbGys+vXrp82bN3sVf1FRkWbMmKGWLVuqYcOGuuCCC7R27Vrn/IyMDDVq1EgrVqzQmWeeqaioKA0dOlS5ubnONidPntS0adPUqFEjNW3aVDNnzlRaWppGjhwpSZo0aZLWrVun+fPnOy+l7Ny50/n5TZs2KTU1VZGRkerVq5d++OGHGq3LCy+8oK5duyosLEwJCQmaOnWqc57D4dDChQt1ySWXKDIyUmeeeaY+/fRTbd++Xf3791fDhg3Vq1cv7dixo0bfDdRFJCpAAIiKilJUVJSWLFmioqKiKtuNGTNGeXl5Wr58uTZt2qSUlBQNHDhQhw4dcrbZvn273njjDb377rt6//339eWXX+rmm292zj9y5IjS0tL08ccf67PPPlPHjh01bNgwHTlypMbxT506VZ9++qlef/11ffXVVxozZoyGDh2qrKwsZ5tjx47p0Ucf1UsvvaSPPvpIOTk5mjFjhnP+ww8/rFdeeUXp6enasGGDCgoKXMbJzJ8/Xz179tR1112n3Nxc5ebmKikpyTn/rrvu0mOPPaYvvvhC9evX17XXXuvxeixYsEC33HKLrr/+em3dulVLly5Vhw4dXNrcd999mjhxojIzM9W5c2f94Q9/0A033KBZs2bpiy++kGVZLskNgNPw+fOZAdhi0aJFVuPGja3w8HCrV69e1qxZs6wtW7Y4569fv96KiYmxTpw44fK59u3bWwsXLrQsy7Jmz55thYSEWLt373bOX758uVWvXj0rNze30u8tKSmxoqOjrXfffdc5TZL19ttvVxlrv379rOnTp1uWZVk//fSTFRISYu3Zs8elzcCBA61Zs2ZZlmVZ6enpliRr+/btzvlPPfWU1bx5c+f75s2bW4888ojz/cmTJ61WrVpZI0aMqPR7y61Zs8aSZK1atco5bdmyZZYk6/jx45XGn52dbUmyvvzyS5fpLVq0sO66664q11uSdffddzvff/rpp5Yk6/nnn3dOe+2116zw8PAKn60sdgCWxRkVIECMGjVKe/fu1dKlSzV06FCtXbtWKSkpysjIkCRt2bJFhYWFatq0qfMMTFRUlLKzs10uNbRq1UotW7Z0vu/Zs6dKS0udl0L279+v6667Th07dlRsbKxiYmJUWFionJycGsW9detWlZSUKDk52SWudevWucQVGRmp9u3bO98nJCQoLy9PkpSfn6/9+/fr/PPPd84PCQnRueee63Yc3bt3d1m2JOfy3ZGXl6e9e/dq4MCBbn9P8+bNJUndunVzmXbixAkVFBS4/d1AXcZgWiCAhIeHa/DgwRo8eLD++te/asqUKZo9e7YmTZqkwsJCJSQkuIz9KNeoUSO3vyMtLU0HDx7U/Pnz1bp1a4WFhalnz54qLi6uUcyFhYUKCQnRpk2bFBIS4jIvKirK+d8NGjRwmedwOGRZVo2+szK/Xb7D4ZAklZaWuv35iIiIGn+Pt98N1GWcUQECWJcuXXT06FFJUkpKivbt26f69eurQ4cOLq9mzZo5P5OTk6O9e/c633/22WeqV6+eOnXqJEnasGGDpk2bpmHDhjkHjf7vf/+rcYw9evRQSUmJ8vLyKsQVHx/v1jJiY2PVvHlzbdy40TmtpKSkwiDf0NBQlZSU1DjW6kRHR6tNmzY+v10ZgCvOqAAB4ODBgxozZoyuvfZade/eXdHR0friiy80d+5cjRgxQpI0aNAg9ezZUyNHjtTcuXOVnJysvXv3atmyZbr88suVmpoqqeysTFpamh599FEVFBRo2rRpGjt2rDNp6Nixo1566SWlpqaqoKBAd9xxh9tnEyqTnJysCRMmaOLEiXrsscfUo0cPHThwQKtXr1b37t01fPhwt5Zz6623as6cOerQoYM6d+6sv//97/r555+dZygkqU2bNvr888+1c+dORUVFqUmTJjWOuzL33HOPbrzxRsXFxeniiy/WkSNHtGHDBt166622fg+AX5GoAAEgKipKF1xwgZ544gnt2LFDv/zyi5KSknTdddfpL3/5i6SySwrvvfee7rrrLl1zzTU6cOCA4uPj1bdvX+dYCUnq0KGDrrjiCg0bNkyHDh3SJZdcoqeffto5//nnn9f111+vlJQUJSUl6cEHH3S5+6Ym0tPTdf/99+v222/Xnj171KxZM/3ud7/TJZdc4vYyZs6cqX379mnixIkKCQnR9ddfryFDhrhcTpoxY4bS0tLUpUsXHT9+XNnZ2V7Ffaq0tDSdOHFCTzzxhGbMmKFmzZpp9OjRtn4HAFcOy86LwACMds8992jJkiXKzMz0dyheKy0t1ZlnnqmxY8fqvvvus3XZO3fuVNu2bfXll1/6vIR+uf79++ucc87RvHnzauX7gEDBGBUAAeGnn37Ss88+q23btmnr1q266aablJ2drT/84Q8++85evXqpV69ePlu+JL3yyiuKiorS+vXrffo9QKDi0g+AgFCvXj1lZGRoxowZsixLZ511llatWqUzzzzT9u9KTEx0FqMLCwuzffm/ddlll+mCCy6Q5NndWUBdwaUfAABgLC79AAAAY5GoAAAAY5GoAAAAY5GoAAAAY5GoAAAAY5GoAAAAY5GoAAAAY5GoAAAAY/0/YnIyCacWVsIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot data\n",
    "plt.scatter(X[:50, 0], X[:50, 1],color='red', marker='o',label='Setosa')\n",
    "plt.scatter(X[50:100, 0], X[50:100, 1],color='blue', marker='s',label='Versicolor')\n",
    "plt.xlabel('Sepal length [cm]')\n",
    "plt.ylabel('Petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61fad7d-b3fb-4a37-9c91-3c2fbadec716",
   "metadata": {},
   "source": [
    "### training the perceptron algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "708fb904-3e05-447a-b187-08b608de28ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ppn = Perceptron(eta=0.1, n_iter=10)\n",
    "ppn.fit(X,y)\n",
    "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Number of updates')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6386477f-e0a9-48cc-aa8e-8ddf8c9dd7d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
    "    # setup market generator and color map\n",
    "    markers = ('o','s','^','v','<')\n",
    "    colors = ('red','blue','lightgreen','gray','cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "\n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:,0].max() +1\n",
    "    x2_min, x2_max = X[:,1].min() -1, X[:,1].max() +1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                np.arange(x2_min, x2_max, resolution))\n",
    "    lab = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    lab = lab.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, lab, alpha=0.3, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx2.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y==cl,0],\n",
    "                   y=X[y==cl,1],\n",
    "                   alpha=0.8,\n",
    "                   c=colors[idx],\n",
    "                   marker=markers[idx],\n",
    "                   label=f'Class {cl}',edgecolor='black' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7974cb26-63a2-465e-be8a-bdc8e5d3089a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(X, y, classifier=ppn)\n",
    "plt.xlabel('Sepal length [cm]')\n",
    "plt.ylabel('Petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c5a792f-1bf4-425b-97a0-9ef430e29a99",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}