private IEnumerable<Tuple<List<double>, int>> GetTrainingData(SENSOR machine, int start, int trainingEnd) { //Split into training & prediction set List<List<double>> featureVectors = new List<List<double>>(); List<int> timeStamps = new List<int>(); if (machine == SENSOR.GSR) { int stepSize = 100; for (int i = 0; i < _fdAnomaly.gsrData.Last().timestamp - _fdAnomaly.gsrData.First().timestamp - GSR_DURATION + GSR_DELAY; i += stepSize) { List<double> featureVector = new List<double>(); List<double> data = _fdAnomaly.gsrData.SkipWhile(x => (x.timestamp - _fdAnomaly.gsrData.First().timestamp) < i + GSR_DELAY).TakeWhile(x => i + GSR_DURATION > (x.timestamp - _fdAnomaly.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); return trainingSet; }
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; }