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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