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;
}
