public static double CalculateEarlyScore(PointsOfInterest poiT, List<Events> eventsT, int startT, int endT)
{
double timeReduction = 1 - ((double)poiT.GetFlaggedAreas().Where(x=>x.Item2> startT).Sum(x => (x.Item2 - x.Item1)) / (endT - startT));
double eventsHit = (double)eventsT.Where(x => x.isHit).Count() / eventsT.Count;
return ((TIME_WEIGHT * timeReduction) * (HIT_WEIGHT * eventsHit)) / (HIT_WEIGHT + TIME_WEIGHT);
}
public static double CalculateEarlyScore(PointsOfInterest poiT, List <Events> eventsT, int startT, int endT)
{
double timeReduction = 1 - ((double)poiT.GetFlaggedAreas().Where(x => x.Item2 > startT).Sum(x => (x.Item2 - x.Item1)) / (endT - startT));
double eventsHit = (double)eventsT.Where(x => x.isHit).Count() / eventsT.Count;
return(((TIME_WEIGHT * timeReduction) * (HIT_WEIGHT * eventsHit)) / (HIT_WEIGHT + TIME_WEIGHT));
}
public NoveltyResult(PointsOfInterest poi, List<Events> events, int start, int end, LibSVMsharp.SVMParameter parameter, List<OneClassFV> anomalis)
{
this.poi = poi;
this.events = events.Select(x => x.Copy()).ToList();
this.start = start;
this.end = end;
this.parameter = parameter;
this.anomalis = anomalis.ToList();
}
public NoveltyResult(PointsOfInterest poi, List <Events> events, int start, int end, LibSVMsharp.SVMParameter parameter, List <OneClassFV> anomalis)
{
this.poi = poi;
this.events = events.Select(x => x.Copy()).ToList();
this.start = start;
this.end = end;
this.parameter = parameter;
this.anomalis = anomalis.ToList();
}
public static void SavePathPointsOfInterest(PointsOfInterest POIs, string path, string name)
{
if (!Directory.Exists(path + "/POI"))
{
Directory.CreateDirectory(path + "/POI");
}
List <string> data = new List <string>();
var tempAreas = POIs.GetFlaggedAreas();
foreach (Tuple <int, int> area in tempAreas)
{
data.Add($"{area.Item1}, {area.Item2}");
}
File.WriteAllLines(path + "/POI/" + name + ".txt", data);
}
public static Dictionary <SENSOR, PointsOfInterest> LoadPointOfInterest(string path)
{
Dictionary <SENSOR, PointsOfInterest> pois = new Dictionary <SENSOR, PointsOfInterest>();
if (!Directory.Exists(path + "/POI"))
{
return(null);
}
foreach (SENSOR key in Enum.GetValues(typeof(SENSOR)))
{
string[] data = File.ReadAllLines(path + "/Anomalis/" + key.ToString() + ".txt");
List <int> anoma = new List <int>();
for (int i = 1; i < data.Length; i++)
{
anoma.Add(int.Parse(data[i]));
}
PointsOfInterest currentPoi = new PointsOfInterest(anoma);
pois.Add(key, currentPoi);
}
return(pois);
}
public static Dictionary<SENSOR, PointsOfInterest> LoadPointOfInterest(string path)
{
Dictionary<SENSOR, PointsOfInterest> pois = new Dictionary<SENSOR, PointsOfInterest>();
if (!Directory.Exists(path + "/POI"))
{
return null;
}
foreach (SENSOR key in Enum.GetValues(typeof(SENSOR)))
{
string[] data = File.ReadAllLines(path + "/Anomalis/" + key.ToString() + ".txt");
List<int> anoma = new List<int>();
for (int i = 1; i < data.Length; i++)
{
anoma.Add(int.Parse(data[i]));
}
PointsOfInterest currentPoi = new PointsOfInterest(anoma);
pois.Add(key, currentPoi);
}
return pois;
}
private void CreateVotingPOI()
{
anomalies = new List<OneClassFV>();
List<Tuple<int, int>> gsr = pois[SENSOR.GSR].GetFlaggedAreas();
List<Tuple<int, int>> hr = pois[SENSOR.HR].GetFlaggedAreas();
List<Tuple<int, int>> eeg = new List<Tuple<int, int>>();
if (pois.Keys.Contains(SENSOR.EEG))
{
eeg = pois[SENSOR.EEG].GetFlaggedAreas();
}
List<Tuple<int, int>> face = pois[SENSOR.FACE].GetFlaggedAreas();
Dictionary<string, bool> anomaliPresent = new Dictionary<string, bool>();
anomaliPresent.Add("gsr", false);
anomaliPresent.Add("hr", false);
anomaliPresent.Add("eeg", false);
anomaliPresent.Add("face", false);
for (int time = start; time < end; time++)
{
if(gsr.Count > 0 && gsr.First().Item1 < time && time < gsr.First().Item2)
{
anomaliPresent["gsr"] = true;
}
if (hr.Count > 0 && hr.First().Item1 < time && time < hr.First().Item2)
{
anomaliPresent["hr"] = true;
}
if (eeg.Count > 0 && eeg.First().Item1 < time && time < eeg.First().Item2)
{
anomaliPresent["eeg"] = true;
}
if (face.Count > 0 && face.First().Item1 < time && time < face.First().Item2)
{
anomaliPresent["face"] = true;
}
if (anomaliPresent.Where(x=>x.Value == true).Count() >= voteThreashold)
{
List<double> centers = new List<double>();
foreach (var key in anomaliPresent.Keys)
{
if (anomaliPresent[key] == true)
{
if (key == "gsr")
{
centers.Add(((gsr.First().Item2 -gsr.First().Item1)/2) + gsr.First().Item1);
gsr.RemoveAt(0);
}
else if (key == "eeg")
{
centers.Add(((eeg.First().Item2 - eeg.First().Item1) / 2) + eeg.First().Item1);
eeg.RemoveAt(0);
}
else if (key == "face")
{
centers.Add(((face.First().Item2 - face.First().Item1) / 2) + face.First().Item1);
face.RemoveAt(0);
}
else
{
centers.Add(((hr.First().Item2 - hr.First().Item1) / 2) + hr.First().Item1);
hr.RemoveAt(0);
}
}
}
anomalies.Add(new OneClassFV(null, (int)Math.Round(centers.Average())));
}
if (gsr.Count > 0 && time > gsr.First().Item2)
{
gsr.RemoveAt(0);
}
if (hr.Count > 0 && time > hr.First().Item2)
{
hr.RemoveAt(0);
}
if (eeg.Count > 0 && time > eeg.First().Item2)
{
eeg.RemoveAt(0);
}
if (face.Count > 0 && time > face.First().Item2)
{
face.RemoveAt(0);
}
//Clean
anomaliPresent["gsr"] = false;
anomaliPresent["eeg"] = false;
anomaliPresent["hr"] = false;
anomaliPresent["face"] = false;
}
votingPOI = new PointsOfInterest(anomalies);
}
private void CreateVotingPOI()
{
anomalies = new List <OneClassFV>();
List <Tuple <int, int> > gsr = pois[SENSOR.GSR].GetFlaggedAreas();
List <Tuple <int, int> > hr = pois[SENSOR.HR].GetFlaggedAreas();
List <Tuple <int, int> > eeg = new List <Tuple <int, int> >();
if (pois.Keys.Contains(SENSOR.EEG))
{
eeg = pois[SENSOR.EEG].GetFlaggedAreas();
}
List <Tuple <int, int> > face = pois[SENSOR.FACE].GetFlaggedAreas();
Dictionary <string, bool> anomaliPresent = new Dictionary <string, bool>();
anomaliPresent.Add("gsr", false);
anomaliPresent.Add("hr", false);
anomaliPresent.Add("eeg", false);
anomaliPresent.Add("face", false);
for (int time = start; time < end; time++)
{
if (gsr.Count > 0 && gsr.First().Item1 < time && time < gsr.First().Item2)
{
anomaliPresent["gsr"] = true;
}
if (hr.Count > 0 && hr.First().Item1 < time && time < hr.First().Item2)
{
anomaliPresent["hr"] = true;
}
if (eeg.Count > 0 && eeg.First().Item1 < time && time < eeg.First().Item2)
{
anomaliPresent["eeg"] = true;
}
if (face.Count > 0 && face.First().Item1 < time && time < face.First().Item2)
{
anomaliPresent["face"] = true;
}
if (anomaliPresent.Where(x => x.Value == true).Count() >= voteThreashold)
{
List <double> centers = new List <double>();
foreach (var key in anomaliPresent.Keys)
{
if (anomaliPresent[key] == true)
{
if (key == "gsr")
{
centers.Add(((gsr.First().Item2 - gsr.First().Item1) / 2) + gsr.First().Item1);
gsr.RemoveAt(0);
}
else if (key == "eeg")
{
centers.Add(((eeg.First().Item2 - eeg.First().Item1) / 2) + eeg.First().Item1);
eeg.RemoveAt(0);
}
else if (key == "face")
{
centers.Add(((face.First().Item2 - face.First().Item1) / 2) + face.First().Item1);
face.RemoveAt(0);
}
else
{
centers.Add(((hr.First().Item2 - hr.First().Item1) / 2) + hr.First().Item1);
hr.RemoveAt(0);
}
}
}
anomalies.Add(new OneClassFV(null, (int)Math.Round(centers.Average())));
}
if (gsr.Count > 0 && time > gsr.First().Item2)
{
gsr.RemoveAt(0);
}
if (hr.Count > 0 && time > hr.First().Item2)
{
hr.RemoveAt(0);
}
if (eeg.Count > 0 && time > eeg.First().Item2)
{
eeg.RemoveAt(0);
}
if (face.Count > 0 && time > face.First().Item2)
{
face.RemoveAt(0);
}
//Clean
anomaliPresent["gsr"] = false;
anomaliPresent["eeg"] = false;
anomaliPresent["hr"] = false;
anomaliPresent["face"] = false;
}
votingPOI = new PointsOfInterest(anomalies);
}
private void PredictionNuThread(ref int count, SENSOR sensor, int start, int end, ref ConcurrentStack<SVMParameter> svmParams, List<SVMNode[]> data, int svmCount, ref NoveltyResult bestCoveredResult, ref Mutex mutex, ref ConcurrentBag<string> nuResults)
{
OneClassClassifier occ = new OneClassClassifier(data);
List<OneClassFV> anomali = new List<OneClassFV>();
List<Events> eventResult = new List<Events>();
int maxCount = svmCount;
string sensorPath = path + "/" + sensor.ToString();
foreach (Events p in events)
{
var evt = p.Copy();
eventResult.Add(evt);
}
while (!svmParams.IsEmpty)
{
SVMParameter svmParam = null;
svmParams.TryPop(out svmParam);
if (svmParam == null)
{
break;
}
anomali = new List<OneClassFV>();
occ.CreateModel(svmParam);
anomali.AddRange(occ.PredictOutliers(featureVectors[sensor].Where(x => start < x.TimeStamp && x.TimeStamp < end).ToList()));
PointsOfInterest dPointsOfInterest = new PointsOfInterest(anomali);
foreach (Events evt in eventResult)
{
evt.SetPointOfInterest(dPointsOfInterest);
}
NoveltyResult tempResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
/*NoveltyResult.ConfusionMatrix cfm = tempResult.CalculateConfusionMatrix();
decimal tpr = ((decimal)cfm.TruePostive) / ((decimal)cfm.TruePostive + cfm.FalseNegative);
decimal fpr = 1 - ((decimal)cfm.TrueNegative / ((decimal)cfm.TrueNegative + cfm.FalsePostive));
*/
double temp = tempResult.FlaggedAreaSize();
double temp2 = tempResult.CalculateTotalNormalArea();
double areaCovered = ((double)tempResult.FlaggedAreaSize() / tempResult.CalculateTotalNormalArea() > 1) ? 1 : tempResult.FlaggedAreaSize() / (double)tempResult.CalculateTotalNormalArea();
nuResults.Add($"{tempResult.parameter.Nu.ToString()}:"
+ $"{tempResult.CalculateHitResult().eventHits/ (double)tempResult.CalculateHitResult().eventsTotal};"
+ $"{tempResult.CalculateHitResult().hits / ((double)tempResult.CalculateHitResult().misses+ tempResult.CalculateHitResult().hits)};"
+ $"{areaCovered}");
mutex.WaitOne();
count++;
SetProgress(count, sensor);
mutex.ReleaseMutex();
}
Log.LogMessage(sensor + " done!");
}
private void PredictionThread(ref int count, SENSOR sensor, int start, int end, ref ConcurrentStack<SVMParameter> svmParams, List<SVMNode[]> data, int svmCount, ref NoveltyResult bestCoveredResult, ref Mutex mutex)
{
OneClassClassifier occ = new OneClassClassifier(data);
List<OneClassFV> anomali = new List<OneClassFV>();
List<Events> eventResult = new List<Events>();
List<OneClassFV> outliersFromSam = new List<OneClassFV>();
int maxCount = svmCount;
string sensorPath = path + "/" + sensor.ToString();
foreach (Events p in events)
{
var evt = p.Copy();
eventResult.Add(evt);
}
while (!svmParams.IsEmpty)
{
SVMParameter svmParam = null;
svmParams.TryPop(out svmParam);
if (svmParam == null)
{
break;
}
anomali = new List<OneClassFV>();
occ.CreateModel(svmParam);
anomali.AddRange(occ.PredictOutliers(featureVectors[sensor].Where(x => start < x.TimeStamp && x.TimeStamp < end).ToList()));
PointsOfInterest dPointsOfInterest = new PointsOfInterest(anomali);
foreach (Events evt in eventResult)
{
evt.SetPointOfInterest(dPointsOfInterest);
}
NoveltyResult tempResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
/*NoveltyResult.ConfusionMatrix cfm = tempResult.CalculateConfusionMatrix();
decimal tpr = ((decimal)cfm.TruePostive) / ((decimal)cfm.TruePostive + cfm.FalseNegative);
decimal fpr = 1 - ((decimal)cfm.TrueNegative / ((decimal)cfm.TrueNegative + cfm.FalsePostive));
*/
mutex.WaitOne();
if (bestCoveredResult == null)
{
bestCoveredResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
}
else if (tempResult.CalculateCoveredScore() > bestCoveredResult.CalculateCoveredScore())
{
//bestResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali); ;
bestCoveredResult = tempResult;
}
count++;
SetProgress(count, sensor);
mutex.ReleaseMutex();
}
Log.LogMessage(sensor + " done!");
}
public static void SaveVotingPointsOfInterest(PointsOfInterest POIs, string path, string name)
{
if (!Directory.Exists(path + "/POI"))
{
Directory.CreateDirectory(path + "/POI");
}
List<string> data = new List<string>();
var tempAreas = POIs.GetFlaggedAreas();
foreach (Tuple<int, int> area in tempAreas)
{
data.Add($"{area.Item1}, {area.Item2}");
}
File.WriteAllLines(path + "/POI/" + name + ".txt", data);
}
