public List <AnomalyReport> detect(Timeseries ts)
{
List <AnomalyReport> vector = new List <AnomalyReport>();
int i = 0;
foreach (correlatedFeatures var in cf)
{
for (int j = 0; j < ts.table.First().Value.Count(); ++j)
{ // Loop through the map.
int iter = -1;
// Loop through the cf vector.
iter++;
float floatA = ts.table[var.feature1][j];
float floatB = ts.table[var.feature2][j];
dynamic p = new Point(floatA, floatB); // A point represent the two current features.
//float correlation = dev(*p, i->lin_reg);
// Case the correlation is bigger then the threshold determined by the current correlation deviation.
if (isAnomalous(var, p))
{
string description = var.feature1 + "-" + var.feature2;
long timeStep = j + 1; // For time step to start with 1 instead of 0.
AnomalyReport ar = new AnomalyReport(description, timeStep); // Build a anomaly report with those features.
vector.Add(ar);
}
}
i++;
}
return(vector);
}
public List <AnomalyReport> detect(Timeseries ts)
{
List <AnomalyReport> anomalies = new List <AnomalyReport>();
int numOfRows = ts.NumOfRows;
int cfLen = cf.Count;
for (int cfIndex = 0; cfIndex < cfLen; cfIndex++)
{
for (int timeStep = 0; timeStep < numOfRows; timeStep++)
{
if (isAnomaly(ts, cf[cfIndex], timeStep))
{
//cout << "Anomaly found at " << timeStep + 1 << ": " << currDev << " crossed " << cf[cfIndex].threshold << std::endl;
AnomalyReport report = new AnomalyReport(cf[cfIndex].feature1 + "-" + cf[cfIndex].feature2, timeStep + 1);
anomalies.Add(report);
}
}
}
return(anomalies);
}
