public void Train(double[] values, ref double[][] results)
{
if (results == null)
{
results = new double[values.Length][];
for (int i = 0; i < results.Length; ++i)
{
results[i] = new double[_outputLengthArray[(int)_detectMode]];
}
}
else if (results.Length > values.Length)
{
Array.Resize <double[]>(ref results, values.Length);
}
_minimumOriginValue = Double.MaxValue;
_maximumOriginValue = Double.MinValue;
Array.Resize(ref _seriesToDetect, values.Length);
for (int i = 0; i < values.Length; ++i)
{
_seriesToDetect[i] = values[i];
_minimumOriginValue = Math.Min(_minimumOriginValue, values[i]);
_maximumOriginValue = Math.Max(_maximumOriginValue, values[i]);
}
if (_period > 0)
{
_deseasonalityFunction.Deseasonality(ref values, _period, ref _seriesToDetect);
}
SpectralResidual(_seriesToDetect, results, _threshold);
//Optional Steps
if (_detectMode == SrCnnDetectMode.AnomalyAndMargin)
{
if (_period > 0)
{
GetMarginPeriod(values, results, _seriesToDetect, _sensitivity);
}
else
{
GetMargin(values, results, _sensitivity);
}
}
else if (_detectMode == SrCnnDetectMode.AnomalyAndExpectedValue)
{
if (_period > 0)
{
GetExpectedValuePeriod(values, results, _seriesToDetect);
}
else
{
GetExpectedValue(values, results);
}
}
}
public void Deseasonality(ref double[] values, int period, ref double[] results)
{
bool success = _stl.Decomposition(values, period);
if (success)
{
for (int i = 0; i < _stl.Residual.Count; ++i)
{
results[i] = _stl.Residual[i];
}
}
// invoke the back up deseasonality method if stl decompose fails.
else
{
_backupFunc.Deseasonality(ref values, period, ref results);
}
}