public bool isAnomalous(correlatedFeatures cf, Point point)
{
float correlation = adu.dev(point, cf.lin_reg);
if (correlation > cf.threshold)
{
return(true);
}
return(false);
}
public void learnNormal(Timeseries ts)
{
anomaly_detection_util adu = new anomaly_detection_util();
Dictionary <string, List <float> > tsMap = ts.table;
List <string> tsFeaturesVector = ts.features;
int featuresVectorSize = tsFeaturesVector.Count();
int valueVectorSize = tsMap[tsFeaturesVector[0]].Count();
for (int i = 0; i < featuresVectorSize; ++i)
{
string fiName = tsFeaturesVector[i];
float[] fiData = tsMap[fiName].ToArray();
float maxPearson = 0;
correlatedFeatures currentCF = new correlatedFeatures();
currentCF.feature1 = fiName;
for (int j = i + 1; j < featuresVectorSize; ++j)
{
string fjName = tsFeaturesVector[j];
float[] fjData = tsMap[fjName].ToArray();
float currentPearson = Math.Abs(adu.pearson(fiData, fjData, valueVectorSize));
if (currentPearson > maxPearson)
{
maxPearson = currentPearson;
currentCF.feature2 = fjName;
}
}
if (maxPearson > this.threshold)
{
currentCF.corrlation = maxPearson;
Point[] points = new Point[valueVectorSize];
for (int j = 0; j < valueVectorSize; ++j)
{
points[j] = new Point(tsMap[currentCF.feature1][j], tsMap[currentCF.feature2][j]);
}
currentCF.lin_reg = adu.linear_reg(points, valueVectorSize);
currentCF.threshold = 0;
for (int j = 0; j < valueVectorSize; ++j)
{
currentCF.threshold = Math.Max(currentCF.threshold, Math.Abs(adu.dev(points[j], currentCF.lin_reg)));
}
currentCF.threshold *= (float)1.1;
cf.Add(currentCF);
}
}
}
