//create the CorrelatedFeatures List.
public void CreateCf(TimeSeries ts)
{
float biggestPearson;
CorrelatedFeatures currentStruct = new CorrelatedFeatures();
int i;
for (i = 0; i < ts.GetColumnSize(); i++)
{
currentStruct.Feature1 = ts.GetFeatures()[i];
currentStruct.Feature2 = "";
biggestPearson = Threshold; //minimum for correlation
for (int j = 0; j < ts.GetColumnSize(); j++)
{
if (j == i)
{
continue;
}
float absPearson = AnomalyDetectionUtil.Pearson(ts.GetColumn(currentStruct.Feature1).ToArray(),
ts.GetColumn(ts.GetFeatures()[j]).ToArray());
absPearson = absPearson > 0 ? absPearson : -absPearson;
if (absPearson >= biggestPearson)
{
biggestPearson = absPearson;
currentStruct.Feature2 = ts.GetFeatures()[j];
currentStruct.Correlation = biggestPearson;
}
}
if (currentStruct.Feature2 != "")
{
currentStruct.AllPoints = new List <Point>();
Cf.Add(currentStruct);
}
}
/* //for the last item
* i = ts.GetColumnSize() - 1;
* currentStruct.Feature1 = ts.GetFeatures()[i];
* currentStruct.Feature2 = "";
* biggestPearson = Threshold; //minimum for correlation
* for (int j = 0; j < i; j++)
* {
* float absPearson = AnomalyDetectionUtil.Pearson(ts.GetColumn(currentStruct.Feature1).ToArray(),
* ts.GetColumn(ts.GetFeatures()[j]).ToArray());
* absPearson = absPearson > 0 ? absPearson : -absPearson;
* if (absPearson >= biggestPearson)
* {
* biggestPearson = absPearson;
* currentStruct.Feature2 = ts.GetFeatures()[j];
* currentStruct.Correlation = biggestPearson;
* }
* }
*
* if (currentStruct.Feature2 != "")
* {
* currentStruct.AllPoints = new List<Point>();
* Cf.Add(currentStruct);
* }*/
}
//return the _test TimeSeries column value at index.
public float GetTestAt(string column, int index)
{
if (_test != null && _test.GetColumn(column) != null)
{
try { return(_test.GetColumn(column)[index]); }
catch (Exception)
{
}
}
return(0);
}
//calculating the Line for each element in the List.
private void CreateForm(TimeSeries ts)
{
for (var i = 0; i < _cf.Count; i++)
{
var array = new Point[ts.GetRowSize()];
for (var j = 0; j < ts.GetRowSize(); j++)
{
array[j] = new Point(ts.GetColumn(_cf[i].Feature1)[j], ts.GetColumn(_cf[i].Feature2)[j]);
_cf[i].AllPoints.Add(array[j]);
}
var line = CreateCorrelativeForm(array);
_cf[i] = new CorrelatedFeatures(_cf[i].Feature1, _cf[i].Feature2, _cf[i].Correlation, line);
//float th = FindThreshold(array, Cf[i]);
//Cf[i] = new CorrelatedFeatures(Cf[i].Feature1, Cf[i].Feature2, Cf[i].Correlation, th, line);
}
}
//calculating the Line for each element in the List.
public void CreateForm(TimeSeries ts)
{
for (var i = 0; i < Cf.Count; i++)
{
Point[] array = new Point[ts.GetRowSize()];
for (int j = 0; j < ts.GetRowSize(); j++)
{
array[j] = new Point(ts.GetColumn(Cf[i].Feature1)[j], ts.GetColumn(Cf[i].Feature2)[j]);
Cf[i].AllPoints.Add(array[j]);
}
Line line = CreateCorrelativeForm(array);
Cf[i] = new CorrelatedFeatures(Cf[i].Feature1, Cf[i].Feature2, Cf[i].Correlation, 0, line);
//float th = FindThreshold(array, Cf[i]);
//Cf[i] = new CorrelatedFeatures(Cf[i].Feature1, Cf[i].Feature2, Cf[i].Correlation, th, line);
}
}
//create the CorrelatedFeatures List.
private void CreateCf(TimeSeries ts)
{
var currentStruct = new CorrelatedFeatures();
int i;
for (i = 0; i < ts.GetColumnSize(); i++)
{
currentStruct.Feature1 = ts.GetFeatures()[i];
currentStruct.Feature2 = "";
var biggestPearson = _threshold;
for (var j = 0; j < ts.GetColumnSize(); j++)
{
if (j == i)
{
continue;
}
var absPearson = AnomalyDetectionUtil.Pearson(ts.GetColumn(currentStruct.Feature1).ToArray(),
ts.GetColumn(ts.GetFeatures()[j]).ToArray());
absPearson = absPearson > 0 ? absPearson : -absPearson;
if (!(absPearson >= biggestPearson))
{
continue;
}
biggestPearson = absPearson;
currentStruct.Feature2 = ts.GetFeatures()[j];
currentStruct.Correlation = biggestPearson;
}
if (currentStruct.Feature2 == "")
{
continue;
}
currentStruct.AllPoints = new List <Point>();
_cf.Add(currentStruct);
}
}