def is_anomaly(adl_dataset, adl_data_test):
# At train time lsanomaly calculates parameters rho and sigma
lsanomaly = LSAnomaly(sigma=3, rho=0.1)
res = np.reshape((adl_dataset), (-1, 1))
lsanomaly.fit(res)
res_test = np.reshape((adl_data_test), (-1, 1))
predit_res = lsanomaly.predict(res_test)
return predit_res, lsanomaly.predict_proba(
res_test) # predicted result and probability of the result
def test_example_doc(doc_arrays, check_ndarray):
test_pt = np.array([[0]])
x_train, predict_prob = doc_arrays
anomaly_model = LSAnomaly(sigma=3, rho=0.1, seed=42)
anomaly_model.fit(x_train)
expected = [0.0]
p = anomaly_model.predict(test_pt)
assert p == expected
expected = np.array([[0.7231233, 0.2768767]])
p = anomaly_model.predict_proba(test_pt)
logger.debug("p = {}".format(p))
check_ndarray(expected, p)
