public void ProcessOutliers()
{
if (OutlierTracker.Count < this.OutlierTrackingInterval)
{
return;
}
List <int> Outliers = new List <int>();
DateTime StartTime = new DateTime();
while (this.OutlierTracker.Count > 0) // Loop process samples
{
if ((Outliers.Count + this.OutlierTracker.Count) < this.OutlierTrackingInterval)
{
return;
} // not enough for another sample
Feature outlier = this.OutlierTracker.Dequeue();
if (StartTime == DateTime.MinValue)
{
StartTime = outlier.Keylog.Timestamp;
}
Outliers.Add(outlier.IsOutlier ? 1:0); // 1 if outlier
if (Outliers.Count == OutlierTrackingInterval)
{
Tuple <double, double> ret = MeanHelper.FindMAD(Outliers.ToArray());
OutliersAverages.Add((float)ret.Item1);
//Build our own sample as this feature is derived from a feature, not a keylog
Sample FakeSample = new Sample($"{this.Name}_Outliers", ret.Item1, ret.Item2, StartTime, outlier.Keylog.Timestamp, OutliersAverages.Count(), OutlierTrackingInterval);
this.CurrentOutlierSampleCollection.AddFakeSample(FakeSample);
Outliers.Clear();
StartTime = DateTime.MinValue;
}
}
}
public void ProcessFeatures()
{
Tuple <double, double> ret = MeanHelper.FindMAD(this.Features.ToArray());
this.MAD = ret.Item1;
this.Mean = ret.Item2;
}
public String PrintStats(int[] SessionAggregationCount)
{
//if (SessionAggregationCount.Length < 2)
//{ throw new System.Exception("Need multiple Aggs to calc some stats"); }
string ret = this.FeatureName + "," + this.Samples.Count + ",";
foreach (int SampleSize in SessionAggregationCount)
{
List <Sample> buff = new List <Sample>();
List <Sample> aggregatedSamples = new List <Sample>();
foreach (Sample sample in Samples)
{
buff.Add(sample);
if (buff.Count == SampleSize)
{
Sample Agg = new Sample(sample.FeatureName,
MeanHelper.FindMean((buff.ToArray().Select(x => x.MAD)).ToArray()),
MeanHelper.FindMean((buff.ToArray().Select(x => x.Mean)).ToArray()),
buff.First().StartTime,
buff.Last().EndTime,
buff.Last().SampleID / SampleSize,
buff.Last().SampleSize *SampleSize
);
aggregatedSamples.Add(Agg);
buff.Clear();
}
}
// aggregatedSamples is a list of our bigger samples, reasdy for num crunching
double[] Values = aggregatedSamples.ToArray().Select(x => x.Mean).ToArray();
double min = Values.Min();
double max = Values.Max();
double range = max - min;
double midpoint = (max + min) / 2;
double variation = (100 / midpoint) * range;
ret += midpoint.ToString("N1") + ",";
ret += variation.ToString("N1") + ",";
}
ret.TrimEnd(',');
ret += Environment.NewLine;
return(ret);
// StatsDict contains our midpoint and range
}
public String Print(string format, int SessionAggregationCount, bool fromRange = false)
{
String ret = "";
if (!fromRange)
{
this.Close();
ret = CSV_Header + Environment.NewLine;
}
if (SessionAggregationCount > this.Samples.Count())
{
return("");
} // Too big!
List <Sample> buff = new List <Sample>();
List <Sample> aggregatedSamples = new List <Sample>();
foreach (Sample sample in Samples)
{
buff.Add(sample);
if (buff.Count == SessionAggregationCount)
{
Sample Agg = new Sample(sample.FeatureName,
MeanHelper.FindMean((buff.ToArray().Select(x => x.MAD)).ToArray()),
MeanHelper.FindMean((buff.ToArray().Select(x => x.Mean)).ToArray()),
buff.First().StartTime,
buff.Last().EndTime,
buff.Last().SampleID / SessionAggregationCount,
buff.Last().SampleSize *SessionAggregationCount
);
aggregatedSamples.Add(Agg);
buff.Clear();
}
}
foreach (Sample Sample in aggregatedSamples)
{
ret += $"{this.FeatureName},{Sample.SampleID},{SessionAggregationCount},{Sample.SampleID * SessionAggregationCount},{Sample.Mean},{Sample.MAD}";
ret += Environment.NewLine;
}
return(ret);
}
