from sklearn import neighbors, linear_model
import numpy as np
 
 
def train_predict():
    X = [[1, 1], [2, 2.5], [2, 6.8], [4, 7]]
    y = [1, 2, 3, 4]
 
 
    pac_clf = linear_model.PassiveAggressiveClassifier()#loss
 
    per_clf = linear_model.Perceptron()
 
    pac_clf.fit(X, y)
 
    per_clf.fit(X,y)
 
    #print(sgd_clf.predict([[6, 9]]))
 
    X.append([6, 9])
    y.append(5)
 
 
    X1 = X[-1:]
    y1 = y[-1:]
 
    classes = np.unique(y)
 
    pac_clf.partial_fit(X1, y1, classes=classes)
 
    per_clf.partial_fit(X1,y1, classes=classes)
 
    print(pac_clf.predict([[6,9]]))
 
    print(per_clf.predict([[6,9]]))
 
 
if __name__ == "__main__":
    train_predict()  

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