lower_bound_list = []
actual_list = []
outputs = outputs.reshape([-1])
Yfull_ = Yfull.reshape([-1])
for output, actual in zip(outputs, Yfull_):
upper_bound = float(output + (ADConfig.confidance_interval * eta))
lower_bound = float(output - (ADConfig.confidance_interval * eta))
if (actual > output):
anomaly_type = "upward"
else:
anomaly_type = "downward"
if (actual <= upper_bound and actual >= lower_bound):
isanomaly = "N"
anomaly_counter = anomaly_counter + 1
anomaly_score = PreProcessing.AnomalyScore(actual, output, eta,
anomaly_counter,
isanomaly)
else:
isanomaly = "Y"
anomaly_counter = 0
anomaly_score = PreProcessing.AnomalyScore(actual, output, eta,
anomaly_counter,
isanomaly)
output_ = PreProcessing.rescalig(output, Xmax, Xmin)
upper_bound_ = PreProcessing.rescalig(upper_bound, Xmax, Xmin)
lower_bound_ = PreProcessing.rescalig(lower_bound, Xmax, Xmin)
actual_ = PreProcessing.rescalig(actual, Xmax, Xmin)
if (lower_bound_ < 0):
lower_bound_ = 0
anomaly_type_list.append(anomaly_type)
isanomaly_list.append(isanomaly)
