public override bool Equals(object obj)
{
if (this == obj)
{
return(true);
}
if (obj == null)
{
return(false);
}
if (GetType() != obj.GetType())
{
return(false);
}
AveragedAnomalyRecordList other = (AveragedAnomalyRecordList)obj;
if (AveragedRecords == null)
{
if (other.AveragedRecords != null)
{
return(false);
}
}
else if (!AveragedRecords.Equals(other.AveragedRecords))
{
return(false);
}
if (HistoricalValues == null)
{
if (other.HistoricalValues != null)
{
return(false);
}
}
else if (!HistoricalValues.Equals(other.HistoricalValues))
{
return(false);
}
if (BitConverter.DoubleToInt64Bits(Total) != BitConverter.DoubleToInt64Bits(other.Total))
{
return(false);
}
return(true);
}
/**
* Given a series of anomaly scores, compute the likelihood for each score. This
* function should be called once on a bunch of historical anomaly scores for an
* initial estimate of the distribution. It should be called again every so often
* (say every 50 records) to update the estimate.
*
* @param anomalyScores
* @param averagingWindow
* @param skipRecords
* @return
*/
public AnomalyLikelihoodMetrics EstimateAnomalyLikelihoods(List <Sample> anomalyScores, int averagingWindow, int skipRecords)
{
if (anomalyScores.Count == 0)
{
throw new ArgumentException("Must have at least one anomaly score.");
}
// Compute averaged anomaly scores
AveragedAnomalyRecordList records = AnomalyScoreMovingAverage(anomalyScores, averagingWindow);
// Estimate the distribution of anomaly scores based on aggregated records
Statistic distribution;
if (records.AveragedRecords.Count <= skipRecords)
{
distribution = NullDistribution();
}
else
{
List <double> samples = records.GetMetrics();
distribution = EstimateNormal(samples.Skip(skipRecords).Take(samples.Count).ToArray(), true);
/* Taken from the Python Documentation
*
# 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.
#
*/
samples = records.GetSamples();
Statistic metricDistribution = EstimateNormal(samples.Skip(skipRecords).Take(samples.Count).ToArray(), false);
if (metricDistribution.variance < 1.5e-5)
{
distribution = NullDistribution();
}
}
// Estimate likelihoods based on this distribution
int i = 0;
double[] likelihoods = new double[records.AveragedRecords.Count];
foreach (Sample sample in records.AveragedRecords)
{
likelihoods[i++] = NormalProbability(sample.score, distribution);
}
// Filter likelihood values
double[] filteredLikelihoods = FilterLikelihoods(likelihoods);
int len = likelihoods.Length;
Parameters anomalyParameters = Parameters.Empty();
anomalyParameters.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_DIST, distribution);
anomalyParameters.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MVG_AVG, new MovingAverage(records.HistoricalValues, records.Total, averagingWindow));
anomalyParameters.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_HIST_LIKE, len > 0 ? Arrays.CopyOfRange(likelihoods, len - Math.Min(averagingWindow, len), len) : new double[0]);
AnomalyParams @params = new AnomalyParams(anomalyParameters);
//AnomalyParams @params = new AnomalyParams(
// new string[] { "distribution", "movingAverage", "historicalLikelihoods" },
// distribution,
// new MovingAverage(records.historicalValues, records.total, averagingWindow),
// len > 0 ?
// Arrays.CopyOfRange(likelihoods, len - Math.Min(averagingWindow, len), len) :
// new double[0]);
if (LOG.IsDebugEnabled)
{
LOG.Debug(string.Format("Discovered params={0} Number of likelihoods:{1} First 20 likelihoods:{2}",
@params, len, Arrays.CopyOfRange(filteredLikelihoods, 0, 20)));
}
return(new AnomalyLikelihoodMetrics(filteredLikelihoods, records, @params));
}
