Answer by myself:
I analyzed the codes (anomaly_likelihood.py) and found that there is a block that investigate the varience of actualValue with comments like followings:
# HACK ALERT! The CLA model currently does not handle constant metric values
# very well (time of day encoder changes sometimes lead to unstable SDR's
# even though the metric is constant). Until this is resolved, we explicitly
# detect and handle completely flat metric values by reporting them as not
# anomalous.
s = [r[1] for r in aggRecordList]
metricValues = numpy.array(s)
print metricValues
metricDistribution = estimateNormal(metricValues[skipRecords:],
performLowerBoundCheck=False)
if metricDistribution["variance"] < 1.5e-5:
distributionParams = nullDistribution(verbosity = verbosity)
I guess this part is used for the cases where the numeric input data shows very little variation.
However, this part does not seems to properly deal with categorical input data.
Assuming that my categorical input data is sufficiently not static, I removed the block above and runs well.
Please let me know if I misunderstood.