In [1]:

    

import pandas as pd
import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions
from sklearn.svm import SVC

%matplotlib inline


    

In [2]:

    

dataset = pd.DataFrame([[1,1,0], [1,0,1], [0,1,1], [0,0,0]], columns=['feature1', 'feature1', 'target'])


    

In [3]:

    

dataset


    

    
Out[3]:







  

    

      

      
feature1
      
feature1
      
target
    
  
  

    

      
0
      
1
      
1
      
0
    
    

      
1
      
1
      
0
      
1
    
    

      
2
      
0
      
1
      
1
    
    

      
3
      
0
      
0
      
0
    
  

In [4]:

    

X = dataset.values[:,:2]
Y = dataset['target'].values


    

In [5]:

    

model = SVC(kernel='rbf').fit(X,Y)
model


    

    
Out[5]:





SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

In [6]:

    

score = model.score(X,Y)
print('Score com dados de teste: ', score)


    

    




Score com dados de teste:  1.0

In [7]:

    

model.predict([[0,0]])


    

    
Out[7]:





array([0])

In [9]:

    

plot_decision_regions(X, Y, clf=model)
plt.xlabel('features')
plt.ylabel('target')
plt.title('XOR')
plt.show()