In [1]:

    

import os
import pandas as pd

s = os.path.join('https://archive.ics.uci.edu', 'ml', 'machine-learning-databases', 'iris', 'iris.data')
print(f'URL: {s}')

df = pd.read_csv(s, header=None, encoding='utf-8')

df.describe()


    

    




URL: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data






    
Out[1]:







  

    

      

      
0
      
1
      
2
      
3
    
  
  

    

      
count
      
150.000000
      
150.000000
      
150.000000
      
150.000000
    
    

      
mean
      
5.843333
      
3.054000
      
3.758667
      
1.198667
    
    

      
std
      
0.828066
      
0.433594
      
1.764420
      
0.763161
    
    

      
min
      
4.300000
      
2.000000
      
1.000000
      
0.100000
    
    

      
25%
      
5.100000
      
2.800000
      
1.600000
      
0.300000
    
    

      
50%
      
5.800000
      
3.000000
      
4.350000
      
1.300000
    
    

      
75%
      
6.400000
      
3.300000
      
5.100000
      
1.800000
    
    

      
max
      
7.900000
      
4.400000
      
6.900000
      
2.500000
    
  

In [2]:

    

import matplotlib.pyplot as plt
import numpy as np

# select setosa and versicolor
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', 1, -1)

x = df.iloc[0:100, [0, 2]].values

plt.scatter(x[:50, 0], x[:50, 1], color='red', marker='o', label='setosa')
plt.scatter(x[50:100, 0], x[50:100, 1], color='blue', marker='x', label='versicolor')
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')
plt.show()


    

In [3]:

    

from perceptron import Perceptron

ppn = Perceptron(eta=0.1, n_iter=10)
ppn.fit(x, y)

plt.plot(range(1, len(ppn._errors) + 1), ppn._errors, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Number of updates')
plt.show()


    

In [4]:

    

from helper import plot_decision_regions

plot_decision_regions(x, y, classifier=ppn)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')
plt.show()