def outlier(TRAIN, contam):
for i in range(TRAIN.shape[1]):
v = TRAIN[:, i]
v_hat = (v - np.median(v))
TRAIN[:, i] = v_hat
# model creation
clf = EllipticEnvelope(support_fraction=1.,
contamination=contam,
assume_centered=True)
clf.fit(TRAIN)
C = clf.correct_covariance(TRAIN)
pred = clf.predict(TRAIN)
# eigen decomposition
E, U = LA.eig(C)
P = U[0:2, :]
X_hat = np.dot(TRAIN, np.transpose(P))
# plotting
pred += 1
for i in range(pred.shape[0]):
pred[i] = pred[i] // 2
plotting(X_hat, pred)
return pred
