def forecast_lstm(actions):
lstm = LSTM(CONFIG)
model = lstm.load_model()
vocabulary = restore_vocabulary()
actions_scores = readScores(CONFIG)
previous_action = None
for action in actions:
if (action != ''):
# Compare previous action with the incoming action.
action = json.loads(action)
incoming_action = action_to_vector(action, vocabulary)
if (previous_action == None):
previous_action = incoming_action
continue
score = getScore(actions_scores, action['name'])
previous_action_transformed = lstm.pretransform_dataset(
[previous_action], reshape=True)
incoming_action_transformed = lstm.pretransform_dataset(
[incoming_action], reshape=True)
predicted = lstm.forecast(model, previous_action_transformed,
incoming_action_transformed)
# Print anomaly score and set the new one now as previous action.
print lstm.calculate_score(incoming_action_transformed, predicted,
score)
previous_action = incoming_action
