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 CreateSVM(SENSOR machine, List<OneClassFV> trainingSet)
{
var data = trainingSet.Select(x => x.Features).ToList();
OneClassClassifier occ = new OneClassClassifier(data);
SVMParameter svmP = new SVMParameter();
svmP.Kernel = SVMKernelType.RBF;
svmP.C = 100;
svmP.Gamma = 0.01;
svmP.Nu = 0.01;
svmP.Type = SVMType.ONE_CLASS;
occ.CreateModel(svmP);
machines.Add(machine, occ);
}
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!");
}
private void button1_Click(object sender, EventArgs e)
{
noveltyChart.ChartAreas.First().BackColor = Color.DarkGray;
FolderBrowserDialog fbd = new FolderBrowserDialog();
if (fbd.ShowDialog() == DialogResult.
OK)
{
string path = fbd.SelectedPath;
string testSubjectId = path.Split('\\')[path.Split('\\').Length - 2];
fdNovelty.LoadFromFile(new string[] { path + @"\EEG.dat", path + @"\GSR.dat", path + @"\HR.dat", path + @"\KINECT.dat" }, DateTime.Now, false);
events = File.ReadAllLines(path + @"\SecondTest.dat");
string[] tmpSevents = File.ReadAllLines(path + @"\sam.dat");
foreach (string ev in tmpSevents)
{
sEvents.Add(new samEvents(int.Parse(ev.Split(':')[0]), int.Parse(ev.Split(':')[1]), int.Parse(ev.Split(':')[2])));
}
}
if (events.Length == 0)
{
//if events is not assigned with second test data
return;
}
int start = (useRestInTraining.Checked) ? 180000 : 0;
int trainingEnd = int.Parse(events[2].Split('#')[0]);
int windowSize = 5000;
int stepSize = 100;
int delay = 2000;
//Split into training & prediction set
List<List<double>> featureVectors = new List<List<double>>();
List<int> timeStamps = new List<int>();
for (int i = 0; i < fdNovelty.gsrData.Last().timestamp - fdNovelty.gsrData.First().timestamp - windowSize; i += stepSize)
{
List<double> featureVector = new List<double>();
List<double> data = fdNovelty.gsrData.SkipWhile(x => (x.timestamp - fdNovelty.gsrData.First().timestamp) < i).TakeWhile(x => i + windowSize > (x.timestamp - fdNovelty.gsrData.First().timestamp)).Select(x => (double)x.resistance).ToList();
if (data.Count == 0) continue;
featureVector.Add(data.Average());
featureVector.Add(data.Max());
featureVector.Add(data.Min());
double avg = data.Average();
double sd = Math.Sqrt(data.Average(x => Math.Pow(x - avg, 2)));
featureVector.Add(sd);
featureVectors.Add(featureVector);
timeStamps.Add(i);
}
featureVectors = featureVectors.NormalizeFeatureList<double>(Normalize.OneMinusOne).ToList();
var dataSet = featureVectors.Zip(timeStamps, (first, second) => { return Tuple.Create(first, second); });
var trainingSet = dataSet.SkipWhile(x => x.Item2 < start).TakeWhile(x => x.Item2 < trainingEnd);
var predictionSet = dataSet.SkipWhile(x => x.Item2 < trainingEnd);
int count = predictionSet.Count();
int firstPredcition = predictionSet.First().Item2;
OneClassClassifier occ = new OneClassClassifier(trainingSet.Select(x => x.Item1).ToList());
SVMParameter svmP = new SVMParameter();
svmP.Kernel = SVMKernelType.RBF;
svmP.C = 100;
svmP.Gamma = 0.01;
svmP.Nu = 0.01;
svmP.Type = SVMType.ONE_CLASS;
occ.CreateModel(svmP);
/* List<int> indexes = occ.PredictOutliers(predictionSet.Select(x => x.Item1).ToList());
foreach (int index in indexes)
{
timestampsOutliers.Add(predictionSet.ElementAt(index).Item2 - firstPredcition + 180000 + 4000);
}
*/
updateChart();
int k = 0;
}