private void LearnHelper(IDictionary <string, List <float> > table, float maxCore, string f1, string f2)
{
CorrelatedFeatures cfs = new CorrelatedFeatures();
cfs.Feature1 = f1;
cfs.Feature2 = f2;
cfs.Corr = maxCore;
cfs.Lin_Reg = AnomalyUtils.LinearReg(table[f1], table[f2]);
cfs.Threshold = FindThreshold(table[f1], table[f2], cfs.Lin_Reg) * 1.1f;
this.cf.Add(cfs);
}
public void LearnNormal(IDictionary <string, List <float> > table)
{
int columnSize = table.ElementAt(0).Value.Count; //table.Keys.ElementAt(0)
int rowSize = table.Count;
for (int i = 0; i < table.Count; i++)
{
CorrelatedFeatures cfs = new CorrelatedFeatures();
cfs.Corr = -1;
cfs.Feature1 = table.ElementAt(i).Key;
for (int j = i + 1; j < table.Count; j++)
{
float correlation = AnomalyUtils.Pearson(table.ElementAt(i).Value, table.ElementAt(j).Value);
if (correlation > correlationThreshold)
{
if (correlation > cfs.Corr)
{
cfs.Feature2 = table.ElementAt(j).Key;
cfs.Corr = correlation;
cfs.Lin_Reg = AnomalyUtils.LinearReg(table.ElementAt(i).Value,
table.ElementAt(j).Value);
cfs.Threshold = HighestDev(table.ElementAt(i).Value,
table.ElementAt(j).Value, cfs.Lin_Reg);
List <Point> pointsList = new List <Point>();
for (int k = 1; k < columnSize; k++)
{
pointsList.Add(new Point(table[cfs.Feature1].ElementAt(k), table[cfs.Feature2].ElementAt(k)));
}
cfs.CircleThreshold = MinCircle.FindMinCircle(pointsList);
cfs.CircleThreshold.radius *= 1.1;
}
}
}
if (cfs.Corr > -1)
{
cf.Add(cfs);
}
}
}