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);
}
}
}
private void initRegressionLinesList()
{
for (int i = 0; i < xmlDict.Count; i++)
{
regressionLinesDict[i] = new List <DataPoint>();
}
List <DataPoint> tempList = new List <DataPoint>();
for (int i = 0; i < this.csvLinesNumber; i++)
{
tempList.Add(new DataPoint(0, 0));
}
for (int i = 0; i < xmlDict.Count; i++)
{
if (findCorrelativeAttribute(xmlDict[i]) == "")
{
regressionLinesPointsDict[i] = tempList;
continue;
}
// for each attribute we need to find two points of the line.
List <DataPoint> currentList = new List <DataPoint>();
List <DataPoint> currentRegressionLinesPointsList =
regressionLinesPointsDict[findIndexByAttribute(xmlDict[i])];
AnomalyDetector.Line pointsLine;
anomaly_detection_util adu = new anomaly_detection_util();
AnomalyDetector.Point[] pointsList = new AnomalyDetector.Point[csvLinesNumber];
List <double> xs = new List <double>();
for (int j = 0; j < csvLinesNumber; j++)
{
AnomalyDetector.Point point
= new AnomalyDetector.Point(
(float)currentRegressionLinesPointsList[j].X, (float)currentRegressionLinesPointsList[j].Y);
pointsList[j] = point;
xs.Add(currentRegressionLinesPointsList[j].X);
}
double minX = double.MaxValue;
double maxX = double.MinValue;
foreach (double x in xs)
{
if (x < minX)
{
minX = x;
}
if (x > maxX)
{
maxX = x;
}
}
pointsLine = adu.linear_reg(pointsList, csvLinesNumber);
currentList.Add(new DataPoint(minX, minX * pointsLine.a + pointsLine.b));
currentList.Add(new DataPoint(maxX, maxX * pointsLine.a + pointsLine.b));
regressionLinesDict[i] = currentList;
}
}