/// <summary>
/// this function parses resource csv file in order to set list of CorrelatedFeatures.
/// </summary>
void learnNormal()
{
// read CorrelatedFeatures from resource csv
using (var stream = Assembly
.GetExecutingAssembly()
.GetManifestResourceStream("LinearRegressionDLL.regFlightModelCsv.resources"))
using (StreamReader csvReader = new StreamReader(stream))
{
// skip the first row, where the columns names are mentioned.
string currLine = csvReader.ReadLine();
string[] columns = { };
List <string> lineData = new List <string>();
char colSeparator = ',';
// for each row in the csv file
while ((currLine = csvReader.ReadLine()) != null)
{
lineData = currLine.Split(colSeparator).Select(x => x.ToString()).ToList();
// if the new line is empty
if (lineData[0] == "" || lineData.Count != 6)
{
break;
}
// parse the row into a CorrelatedFeatures object
CorrelatedFeatures corrFeature = new CorrelatedFeatures();
corrFeature.feature1 = lineData[0];
corrFeature.feature2 = lineData[1];
corrFeature.corrlation = double.Parse(lineData[2]);
corrFeature.threshold = double.Parse(lineData[3]);
corrFeature.regression_line = new Line(double.Parse(lineData[4]), double.Parse(lineData[5]));
this.cf.Add(corrFeature);
}
}
}
/// <summary>
/// this function checks if there is a significant deviation between the given point and the regression line of the correalted feature.
/// </summary>
/// <param name="corrFeature"> a correlated feature </param>
/// <param name="p"> a point </param>
/// <returns> true - if there is a significant deviation, false if not. </returns>
bool detectedDev(CorrelatedFeatures corrFeature, Point p)
{
double detected_dev = AnomalyDetectionUtil.dev(p, corrFeature.regression_line);
return(detected_dev > corrFeature.threshold);
}