def checkSeverity(self, target, prediction):
error = prediction - target
Sn, Zn = np.nan_to_num(severityMetric(error, self.mu, self.sigma, self.w, self.Sn_1))
# Uses two-in-a-row counter similar to branch prediction
if np.abs(Sn) <= self.THRESHOLD:
self.alert_counter = 0
anomaly_found = False
elif np.abs(Sn) > self.THRESHOLD and self.alert_counter == 0:
#print "Severity: %.3f" %(np.abs(Sn))
self.alert_counter = 1
anomaly_found = False
Sn = self.Sn_1
elif np.abs(Sn) > self.THRESHOLD and self.alert_counter == 1:
#print "Severity: %.3f" %(np.abs(Sn))
Sn = 0
anomaly_found = True
#print "ERROR: ANOMALY"
else:
print error
print np.abs(Sn)
print self.THRESHOLD
print self.alert_counter
self.Sn_1 = Sn
return anomaly_found
def checkSeverity(self, target, prediction):
error = prediction - target
Sn, Zn = severityMetric(error, self.mu, self.sigma, self.w, self.Sn_1)
# Uses two-in-a-row counter similar to branch prediction
if np.abs(Sn) <= self.THRESHOLD:
self.alert_counter = 0
anomaly_found = False
elif np.abs(Sn) > self.THRESHOLD and self.alert_counter == 0:
#print "Severity: %.3f" %(np.abs(Sn))
self.alert_counter = 1
anomaly_found = False
Sn = self.Sn_1
elif np.abs(Sn) > self.THRESHOLD and self.alert_counter == 1:
#print "Severity: %.3f" %(np.abs(Sn))
Sn = 0
anomaly_found = True
#print "ERROR: ANOMALY"
self.Sn_1 = Sn
return anomaly_found
#log the new result
logging.info("Target: " + str(target) + ", Prediction: " + str(prediction))
# Not currently used but will be necessary to flag user:
error = (prediction-target)
sigma = np.sqrt(1/b_opt + np.dot(np.transpose(x_n),np.dot(S_N, x_n)))
# Catching pathogenic cases where variance (ie, sigma)
# gets really really small
if sigma < 1:
sigma = 1
# Update severity metric
mu = mu; sigma = sigma
Sn, Zn = severityMetric(error, mu, sigma, w, Sn_1)
#flag the user if necessary (error is greater than allowance)
#two-in-a-row counter, much like branch prediction
anomaly_found = False
if np.abs(Sn) <= THRESHOLD:
alert_counter = 0
elif np.abs(Sn) > THRESHOLD and alert_counter == 0:
alert_counter = 1
Sn = Sn_1
elif np.abs(Sn) > THRESHOLD and alert_counter == 1:
Sn = 0
anomaly_found = True
logging.error("ANOMALY FOUND!")
if __debug__:
print "ERROR: ANOMALY"
str(prediction))
# Not currently used but will be necessary to flag user:
error = (prediction - target)
sigma = np.sqrt(1 / b_opt +
np.dot(np.transpose(x_n), np.dot(S_N, x_n)))
# Catching pathogenic cases where variance (ie, sigma)
# gets really really small
if sigma < 1:
sigma = 1
# Update severity metric
mu = mu
sigma = sigma
Sn, Zn = severityMetric(error, mu, sigma, w, Sn_1)
#flag the user if necessary (error is greater than allowance)
#two-in-a-row counter, much like branch prediction
anomaly_found = False
if np.abs(Sn) <= THRESHOLD:
alert_counter = 0
elif np.abs(Sn) > THRESHOLD and alert_counter == 0:
alert_counter = 1
Sn = Sn_1
elif np.abs(Sn) > THRESHOLD and alert_counter == 1:
Sn = 0
anomaly_found = True
logging.error("ANOMALY FOUND!")
if __debug__:
print "ERROR: ANOMALY"
