Iris-SLP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"iris_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BIAS</th>\n",
       "      <th>PL</th>\n",
       "      <th>PW</th>\n",
       "      <th>SL</th>\n",
       "      <th>SW</th>\n",
       "      <th>TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1</td>\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>1</td>\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>1</td>\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>1</td>\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>1</td>\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",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     BIAS   PL   PW   SL   SW            TYPE\n",
       "0       1  5.1  3.5  1.4  0.2     Iris-setosa\n",
       "1       1  4.9  3.0  1.4  0.2     Iris-setosa\n",
       "2       1  4.7  3.2  1.3  0.2     Iris-setosa\n",
       "3       1  4.6  3.1  1.5  0.2     Iris-setosa\n",
       "4       1  5.0  3.6  1.4  0.2     Iris-setosa\n",
       "..    ...  ...  ...  ...  ...             ...\n",
       "145     1  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "146     1  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "147     1  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "148     1  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "149     1  5.9  3.0  5.1  1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.insert(0,'BIAS',1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SLP for IRIS\n",
    "We consider only 2 classes of flower (Iris-setosa and Iris-virgini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = df.iloc[0:100,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BIAS</th>\n",
       "      <th>PL</th>\n",
       "      <th>PW</th>\n",
       "      <th>SL</th>\n",
       "      <th>SW</th>\n",
       "      <th>TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    BIAS   PL   PW   SL   SW             TYPE\n",
       "0      1  5.1  3.5  1.4  0.2      Iris-setosa\n",
       "1      1  4.9  3.0  1.4  0.2      Iris-setosa\n",
       "2      1  4.7  3.2  1.3  0.2      Iris-setosa\n",
       "3      1  4.6  3.1  1.5  0.2      Iris-setosa\n",
       "4      1  5.0  3.6  1.4  0.2      Iris-setosa\n",
       "..   ...  ...  ...  ...  ...              ...\n",
       "95     1  5.7  3.0  4.2  1.2  Iris-versicolor\n",
       "96     1  5.7  2.9  4.2  1.3  Iris-versicolor\n",
       "97     1  6.2  2.9  4.3  1.3  Iris-versicolor\n",
       "98     1  5.1  2.5  3.0  1.1  Iris-versicolor\n",
       "99     1  5.7  2.8  4.1  1.3  Iris-versicolor\n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "outp = inp.iloc[:,5:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               TYPE\n",
       "0       Iris-setosa\n",
       "1       Iris-setosa\n",
       "2       Iris-setosa\n",
       "3       Iris-setosa\n",
       "4       Iris-setosa\n",
       "..              ...\n",
       "95  Iris-versicolor\n",
       "96  Iris-versicolor\n",
       "97  Iris-versicolor\n",
       "98  Iris-versicolor\n",
       "99  Iris-versicolor\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = inp.to_numpy()\n",
    "outp = outp.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp,outp = shuffle(inp,outp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 5.2, 3.5, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.0, 3.4, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.9, 3.0, 4.2, 1.5, 'Iris-versicolor'],\n",
       "       [1, 5.4, 3.7, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [1, 6.0, 3.4, 4.5, 1.6, 'Iris-versicolor'],\n",
       "       [1, 4.6, 3.2, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.0, 3.5, 1.3, 0.3, 'Iris-setosa'],\n",
       "       [1, 5.6, 3.0, 4.1, 1.3, 'Iris-versicolor'],\n",
       "       [1, 4.8, 3.0, 1.4, 0.1, 'Iris-setosa'],\n",
       "       [1, 6.1, 2.8, 4.7, 1.2, 'Iris-versicolor'],\n",
       "       [1, 5.0, 3.3, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [1, 4.8, 3.4, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [1, 4.4, 2.9, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [1, 6.3, 3.3, 4.7, 1.6, 'Iris-versicolor'],\n",
       "       [1, 6.0, 2.2, 4.0, 1.0, 'Iris-versicolor'],\n",
       "       [1, 6.7, 3.1, 4.7, 1.5, 'Iris-versicolor'],\n",
       "       [1, 5.5, 2.4, 3.7, 1.0, 'Iris-versicolor'],\n",
       "       [1, 6.2, 2.2, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [1, 5.4, 3.9, 1.7, 0.4, 'Iris-setosa'],\n",
       "       [1, 4.9, 2.4, 3.3, 1.0, 'Iris-versicolor'],\n",
       "       [1, 5.6, 2.5, 3.9, 1.1, 'Iris-versicolor'],\n",
       "       [1, 5.8, 2.7, 3.9, 1.2, 'Iris-versicolor'],\n",
       "       [1, 5.5, 2.5, 4.0, 1.3, 'Iris-versicolor'],\n",
       "       [1, 6.3, 2.3, 4.4, 1.3, 'Iris-versicolor'],\n",
       "       [1, 5.7, 2.6, 3.5, 1.0, 'Iris-versicolor'],\n",
       "       [1, 5.0, 2.3, 3.3, 1.0, 'Iris-versicolor'],\n",
       "       [1, 5.8, 2.7, 4.1, 1.0, 'Iris-versicolor'],\n",
       "       [1, 5.7, 4.4, 1.5, 0.4, 'Iris-setosa'],\n",
       "       [1, 6.3, 2.5, 4.9, 1.5, 'Iris-versicolor'],\n",
       "       [1, 6.1, 2.8, 4.0, 1.3, 'Iris-versicolor'],\n",
       "       [1, 5.8, 4.0, 1.2, 0.2, 'Iris-setosa'],\n",
       "       [1, 4.3, 3.0, 1.1, 0.1, 'Iris-setosa'],\n",
       "       [1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [1, 6.6, 2.9, 4.6, 1.3, 'Iris-versicolor'],\n",
       "       [1, 4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [1, 4.4, 3.0, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [1, 4.8, 3.4, 1.9, 0.2, 'Iris-setosa'],\n",
       "       [1, 4.8, 3.1, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.7, 3.8, 1.7, 0.3, 'Iris-setosa'],\n",
       "       [1, 6.4, 3.2, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [1, 5.0, 3.0, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.0, 3.5, 1.6, 0.6, 'Iris-setosa'],\n",
       "       [1, 4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [1, 5.1, 3.3, 1.7, 0.5, 'Iris-setosa'],\n",
       "       [1, 4.9, 3.0, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [1, 6.4, 2.9, 4.3, 1.3, 'Iris-versicolor'],\n",
       "       [1, 4.6, 3.4, 1.4, 0.3, 'Iris-setosa'],\n",
       "       [1, 6.2, 2.9, 4.3, 1.3, 'Iris-versicolor'],\n",
       "       [1, 5.7, 2.8, 4.1, 1.3, 'Iris-versicolor'],\n",
       "       [1, 6.1, 2.9, 4.7, 1.4, 'Iris-versicolor'],\n",
       "       [1, 5.4, 3.4, 1.7, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.2, 2.7, 3.9, 1.4, 'Iris-versicolor'],\n",
       "       [1, 5.1, 2.5, 3.0, 1.1, 'Iris-versicolor'],\n",
       "       [1, 4.5, 2.3, 1.3, 0.3, 'Iris-setosa'],\n",
       "       [1, 5.4, 3.0, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [1, 5.0, 2.0, 3.5, 1.0, 'Iris-versicolor'],\n",
       "       [1, 6.7, 3.0, 5.0, 1.7, 'Iris-versicolor'],\n",
       "       [1, 4.7, 3.2, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [1, 6.7, 3.1, 4.4, 1.4, 'Iris-versicolor'],\n",
       "       [1, 5.7, 3.0, 4.2, 1.2, 'Iris-versicolor'],\n",
       "       [1, 6.9, 3.1, 4.9, 1.5, 'Iris-versicolor'],\n",
       "       [1, 5.7, 2.8, 4.5, 1.3, 'Iris-versicolor'],\n",
       "       [1, 6.6, 3.0, 4.4, 1.4, 'Iris-versicolor'],\n",
       "       [1, 4.8, 3.0, 1.4, 0.3, 'Iris-setosa'],\n",
       "       [1, 5.1, 3.5, 1.4, 0.3, 'Iris-setosa'],\n",
       "       [1, 6.1, 3.0, 4.6, 1.4, 'Iris-versicolor'],\n",
       "       [1, 5.6, 3.0, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [1, 5.0, 3.2, 1.2, 0.2, 'Iris-setosa'],\n",
       "       [1, 4.9, 3.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [1, 5.6, 2.9, 3.6, 1.3, 'Iris-versicolor'],\n",
       "       [1, 5.2, 4.1, 1.5, 0.1, 'Iris-setosa'],\n",
       "       [1, 5.4, 3.9, 1.3, 0.4, 'Iris-setosa'],\n",
       "       [1, 4.7, 3.2, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.1, 3.4, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [1, 6.5, 2.8, 4.6, 1.5, 'Iris-versicolor'],\n",
       "       [1, 5.3, 3.7, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [1, 4.6, 3.1, 1.5, 0.2, 'Iris-setosa'],\n",
       "       [1, 6.8, 2.8, 4.8, 1.4, 'Iris-versicolor'],\n",
       "       [1, 5.5, 2.3, 4.0, 1.3, 'Iris-versicolor'],\n",
       "       [1, 5.9, 3.2, 4.8, 1.8, 'Iris-versicolor'],\n",
       "       [1, 5.0, 3.6, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.5, 4.2, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [1, 7.0, 3.2, 4.7, 1.4, 'Iris-versicolor'],\n",
       "       [1, 5.8, 2.6, 4.0, 1.2, 'Iris-versicolor'],\n",
       "       [1, 5.1, 3.8, 1.6, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.1, 3.8, 1.5, 0.3, 'Iris-setosa'],\n",
       "       [1, 5.7, 2.9, 4.2, 1.3, 'Iris-versicolor'],\n",
       "       [1, 5.2, 3.4, 1.4, 0.2, 'Iris-setosa'],\n",
       "       [1, 6.0, 2.9, 4.5, 1.5, 'Iris-versicolor'],\n",
       "       [1, 5.4, 3.4, 1.5, 0.4, 'Iris-setosa'],\n",
       "       [1, 4.4, 3.2, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [1, 6.0, 2.7, 5.1, 1.6, 'Iris-versicolor'],\n",
       "       [1, 4.6, 3.6, 1.0, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'],\n",
       "       [1, 5.5, 3.5, 1.3, 0.2, 'Iris-setosa'],\n",
       "       [1, 5.1, 3.8, 1.9, 0.4, 'Iris-setosa'],\n",
       "       [1, 5.5, 2.4, 3.8, 1.1, 'Iris-versicolor'],\n",
       "       [1, 5.1, 3.7, 1.5, 0.4, 'Iris-setosa'],\n",
       "       [1, 5.6, 2.7, 4.2, 1.3, 'Iris-versicolor'],\n",
       "       [1, 5.0, 3.4, 1.6, 0.4, 'Iris-setosa']], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa'],\n",
       "       ['Iris-versicolor'],\n",
       "       ['Iris-setosa']], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = np.delete(inp,5,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 5.2, 3.5, 1.5, 0.2],\n",
       "       [1, 5.0, 3.4, 1.5, 0.2],\n",
       "       [1, 5.9, 3.0, 4.2, 1.5],\n",
       "       [1, 5.4, 3.7, 1.5, 0.2],\n",
       "       [1, 6.0, 3.4, 4.5, 1.6],\n",
       "       [1, 4.6, 3.2, 1.4, 0.2],\n",
       "       [1, 5.0, 3.5, 1.3, 0.3],\n",
       "       [1, 5.6, 3.0, 4.1, 1.3],\n",
       "       [1, 4.8, 3.0, 1.4, 0.1],\n",
       "       [1, 6.1, 2.8, 4.7, 1.2],\n",
       "       [1, 5.0, 3.3, 1.4, 0.2],\n",
       "       [1, 4.8, 3.4, 1.6, 0.2],\n",
       "       [1, 4.4, 2.9, 1.4, 0.2],\n",
       "       [1, 6.3, 3.3, 4.7, 1.6],\n",
       "       [1, 6.0, 2.2, 4.0, 1.0],\n",
       "       [1, 6.7, 3.1, 4.7, 1.5],\n",
       "       [1, 5.5, 2.4, 3.7, 1.0],\n",
       "       [1, 6.2, 2.2, 4.5, 1.5],\n",
       "       [1, 5.4, 3.9, 1.7, 0.4],\n",
       "       [1, 4.9, 2.4, 3.3, 1.0],\n",
       "       [1, 5.6, 2.5, 3.9, 1.1],\n",
       "       [1, 5.8, 2.7, 3.9, 1.2],\n",
       "       [1, 5.5, 2.5, 4.0, 1.3],\n",
       "       [1, 6.3, 2.3, 4.4, 1.3],\n",
       "       [1, 5.7, 2.6, 3.5, 1.0],\n",
       "       [1, 5.0, 2.3, 3.3, 1.0],\n",
       "       [1, 5.8, 2.7, 4.1, 1.0],\n",
       "       [1, 5.7, 4.4, 1.5, 0.4],\n",
       "       [1, 6.3, 2.5, 4.9, 1.5],\n",
       "       [1, 6.1, 2.8, 4.0, 1.3],\n",
       "       [1, 5.8, 4.0, 1.2, 0.2],\n",
       "       [1, 4.3, 3.0, 1.1, 0.1],\n",
       "       [1, 5.1, 3.5, 1.4, 0.2],\n",
       "       [1, 6.6, 2.9, 4.6, 1.3],\n",
       "       [1, 4.9, 3.1, 1.5, 0.1],\n",
       "       [1, 4.4, 3.0, 1.3, 0.2],\n",
       "       [1, 4.8, 3.4, 1.9, 0.2],\n",
       "       [1, 4.8, 3.1, 1.6, 0.2],\n",
       "       [1, 5.7, 3.8, 1.7, 0.3],\n",
       "       [1, 6.4, 3.2, 4.5, 1.5],\n",
       "       [1, 5.0, 3.0, 1.6, 0.2],\n",
       "       [1, 5.0, 3.5, 1.6, 0.6],\n",
       "       [1, 4.9, 3.1, 1.5, 0.1],\n",
       "       [1, 5.1, 3.3, 1.7, 0.5],\n",
       "       [1, 4.9, 3.0, 1.4, 0.2],\n",
       "       [1, 6.4, 2.9, 4.3, 1.3],\n",
       "       [1, 4.6, 3.4, 1.4, 0.3],\n",
       "       [1, 6.2, 2.9, 4.3, 1.3],\n",
       "       [1, 5.7, 2.8, 4.1, 1.3],\n",
       "       [1, 6.1, 2.9, 4.7, 1.4],\n",
       "       [1, 5.4, 3.4, 1.7, 0.2],\n",
       "       [1, 5.2, 2.7, 3.9, 1.4],\n",
       "       [1, 5.1, 2.5, 3.0, 1.1],\n",
       "       [1, 4.5, 2.3, 1.3, 0.3],\n",
       "       [1, 5.4, 3.0, 4.5, 1.5],\n",
       "       [1, 5.0, 2.0, 3.5, 1.0],\n",
       "       [1, 6.7, 3.0, 5.0, 1.7],\n",
       "       [1, 4.7, 3.2, 1.3, 0.2],\n",
       "       [1, 6.7, 3.1, 4.4, 1.4],\n",
       "       [1, 5.7, 3.0, 4.2, 1.2],\n",
       "       [1, 6.9, 3.1, 4.9, 1.5],\n",
       "       [1, 5.7, 2.8, 4.5, 1.3],\n",
       "       [1, 6.6, 3.0, 4.4, 1.4],\n",
       "       [1, 4.8, 3.0, 1.4, 0.3],\n",
       "       [1, 5.1, 3.5, 1.4, 0.3],\n",
       "       [1, 6.1, 3.0, 4.6, 1.4],\n",
       "       [1, 5.6, 3.0, 4.5, 1.5],\n",
       "       [1, 5.0, 3.2, 1.2, 0.2],\n",
       "       [1, 4.9, 3.1, 1.5, 0.1],\n",
       "       [1, 5.6, 2.9, 3.6, 1.3],\n",
       "       [1, 5.2, 4.1, 1.5, 0.1],\n",
       "       [1, 5.4, 3.9, 1.3, 0.4],\n",
       "       [1, 4.7, 3.2, 1.6, 0.2],\n",
       "       [1, 5.1, 3.4, 1.5, 0.2],\n",
       "       [1, 6.5, 2.8, 4.6, 1.5],\n",
       "       [1, 5.3, 3.7, 1.5, 0.2],\n",
       "       [1, 4.6, 3.1, 1.5, 0.2],\n",
       "       [1, 6.8, 2.8, 4.8, 1.4],\n",
       "       [1, 5.5, 2.3, 4.0, 1.3],\n",
       "       [1, 5.9, 3.2, 4.8, 1.8],\n",
       "       [1, 5.0, 3.6, 1.4, 0.2],\n",
       "       [1, 5.5, 4.2, 1.4, 0.2],\n",
       "       [1, 7.0, 3.2, 4.7, 1.4],\n",
       "       [1, 5.8, 2.6, 4.0, 1.2],\n",
       "       [1, 5.1, 3.8, 1.6, 0.2],\n",
       "       [1, 5.1, 3.8, 1.5, 0.3],\n",
       "       [1, 5.7, 2.9, 4.2, 1.3],\n",
       "       [1, 5.2, 3.4, 1.4, 0.2],\n",
       "       [1, 6.0, 2.9, 4.5, 1.5],\n",
       "       [1, 5.4, 3.4, 1.5, 0.4],\n",
       "       [1, 4.4, 3.2, 1.3, 0.2],\n",
       "       [1, 6.0, 2.7, 5.1, 1.6],\n",
       "       [1, 4.6, 3.6, 1.0, 0.2],\n",
       "       [1, 5.5, 2.6, 4.4, 1.2],\n",
       "       [1, 5.5, 3.5, 1.3, 0.2],\n",
       "       [1, 5.1, 3.8, 1.9, 0.4],\n",
       "       [1, 5.5, 2.4, 3.8, 1.1],\n",
       "       [1, 5.1, 3.7, 1.5, 0.4],\n",
       "       [1, 5.6, 2.7, 4.2, 1.3],\n",
       "       [1, 5.0, 3.4, 1.6, 0.4]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "outp = np.where(outp == 'Iris-setosa', 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outp   #setosa = 0; versicolor = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training:Testing 80:20 \n",
    "#We take the randomly shuffled 80 values for training and 20 for testing\n",
    "\n",
    "inp_training = inp[0:80,:]\n",
    "outp_training = outp[0:80,:]\n",
    "\n",
    "inp_testing  = inp[80:100,:]\n",
    "outp_testing = outp[80:100,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.70714422 -0.60016892 -0.05052126 -0.60394004 -0.68368358]\n"
     ]
    }
   ],
   "source": [
    "weights = np.random.uniform(low=-0.9, high=0.9, size=(5,))\n",
    "np.transpose(weights)\n",
    "print(weights)\n",
    "learning_rate = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    if x<-500: return 0\n",
    "    return 1/(1+math.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feedForward(input_values):\n",
    "    sum_value = (np.dot(input_values,weights))\n",
    "#     print(sum_value)\n",
    "    sum_value = sigmoid(sum_value)\n",
    "#     print(sum_value)\n",
    "    if sum_value > 0:\n",
    "        sum_value = 1\n",
    "    else:\n",
    "        sum_value = 0\n",
    "    return sum_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def costFunction(predicted_output, actual_output):\n",
    "    error = 0\n",
    "    for i in range(0,len(predicted_output)):\n",
    "        error = error + abs(predicted_outp - actual_output[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backPropagate(weights, predicted_output, actual_output, inp):\n",
    "    for i in range(0,len(weights)):\n",
    "        weights[i] = weights[i] + learning_rate * (actual_output - predicted_output) * inp[i]\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch = 100\n",
    "for j in range(0,epoch):\n",
    "    error = 0\n",
    "    total_predicted = []\n",
    "    for i in range(0,len(inp_training)):\n",
    "        predicted_output = feedForward(inp_training[i])\n",
    "        if predicted_output != outp_training[i]:\n",
    "            weights = backPropagate(weights, predicted_output, outp_training[i], inp_training[i])\n",
    "            error = error + 1\n",
    "    if error == 0:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-26.49285578 -74.80016892 -82.28052126  54.81605996  29.70631642]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(weights)\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "setosa = df.iloc[:50,:]\n",
    "versicolor = df.iloc[50:100, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,15))\n",
    "plt.scatter(setosa.PL,setosa.SL, color='red', marker='*', label='setosa')\n",
    "plt.scatter(versicolor.PL, versicolor.SL, color='blue', marker='x', label='versicolor')\n",
    "plt.axis([3.5,8,0,6])\n",
    "plt.xticks(np.arange(3,8,0.1))\n",
    "plt.yticks(np.arange(0,6,0.1))\n",
    "plt.title('PETAL LENGTH VS SEPAL LENGTH')\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('sepal length')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,15))\n",
    "plt.scatter(setosa.PW,setosa.SL, color='green', marker='*', label='setosa')\n",
    "plt.scatter(versicolor.PW, versicolor.SL, color='magenta', marker='x', label='versicolor')\n",
    "plt.axis([2,5,0,6])\n",
    "plt.xticks(np.arange(2,5,0.1))\n",
    "plt.yticks(np.arange(0,6,0.1))\n",
    "plt.title('PETAL WIDTH VS SEPAL WIDTH')\n",
    "plt.xlabel('petal width')\n",
    "plt.ylabel('sepal width')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Testing\n",
    "test_output = []\n",
    "error = 0\n",
    "for i in range(0,len(inp_testing)):\n",
    "    predicted_output = feedForward(inp_testing[i])\n",
    "    test_output.append(predicted_output)\n",
    "    if predicted_output != outp_testing[i]:\n",
    "        error = error+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "[0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0]\n"
     ]
    }
   ],
   "source": [
    "print(error)\n",
    "print(test_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(outp_testing,test_output)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(cm)\n",
    "labels = [0,1]\n",
    "\n",
    "fig.colorbar(cax)\n",
    "ax.set_xticklabels([''] + labels)\n",
    "ax.set_yticklabels([''] + labels)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3.6",
   "language": "python",
   "name": "python3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}