import numpy as np

aaa = np.array([[1,     2, 10000, 3, 4,   6,   7,   8, 90, 100,  5000],
                [1000,2000,3,   4000,5000,6000,7000,8,9000,10000,1001]])

# (2, 10) -> (10, 2)

aaa = aaa.transpose()
print(aaa.shape)

from sklearn.covariance import EllipticEnvelope

outliers = EllipticEnvelope(contamination=.2)
outliers.fit(aaa)

results = outliers.predict(aaa)

print(results)
