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import numpy as np
from sklearn_extra.cluster import KMedoids
from sklearn.svm import OneClassSVM
def featureSelection(x):
return np.percentile(x, np.arange(10,100,10))
def MyFunc(t_end):
print (t_end)
kmedoids = KMedoids (n_clusters = 2, random_state = 0).fit(t_end)
result = kmedoids.labels_
print(result)
return result
def SVM(X):
clf = OneClassSVM(kernel = 'rbf', gamma=0.01, nu=0.4).fit(X)
print(clf.predict(X))
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