public static List <DataReading> FaceDataSlice(List <DataReading> data, SAMDataPoint sam)
{
var d = data.SkipWhile(x => x.timestamp < sam.timeOffset + FACE_LATENCY).TakeWhile(x => x.timestamp < sam.timeOffset + FACE_LATENCY + FACE_DURATION).ToList();
if (d.Count == 0)
{
int j = 0;
}
return(d);
}
public List<PredictionResult> DoStacking(SAMDataPoint.FeelingModel feelingsmodel, int nFold, bool useIAPSratings = false, Normalize normalizeFormat = Normalize.OneMinusOne)
{
List<PredictionResult> classifiers = new List<PredictionResult>();
//For each classifier run a crossvalidation and find the best params
foreach (StdClassifier classifier in standardClassifiers)
{
List<PredictionResult> results = classifier.OldCrossValidate(feelingsmodel, 1, useIAPSratings, normalizeFormat);
classifiers.Add(results.OrderBy(x => x.GetAverageFScore()).First());
}
List<List<double>> featureList = new List<List<double>>();
//Create a List of list of answers from each machine
for (int i = 0; i < samData.dataPoints.Count; i++)
{
List<double> featuresToDataPoint = new List<double>();
foreach (PredictionResult classifier in classifiers)
{
featuresToDataPoint.Add(classifier.guesses[i]);
}
featureList.Add(featuresToDataPoint);
}
//Split into nfold problems
List<Tuple<SVMProblem, SVMProblem>> problems = featureList.GetCrossValidationSets<double>(samData, feelingsmodel, nFold, useIAPSratings);
//Get correct results
int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray();
List<PredictionResult> finalResults = new List<PredictionResult>();
//Run for each parameter setting
int cnt = 1;
foreach (SVMParameter SVMpara in Parameters)
{
if (UpdateCallback != null)
{
UpdateCallback(cnt++, Parameters.Count);
}
List<double> guesses = new List<double>();
//model and predict each nfold
foreach (Tuple<SVMProblem, SVMProblem> tupleProblem in problems)
{
guesses.AddRange(tupleProblem.Item2.Predict(tupleProblem.Item1.Train(SVMpara)));
}
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels);
List<double> pres = CalculatePrecision(confus, numberOfLabels);
List<double> recall = CalculateRecall(confus, numberOfLabels);
List<double> fscore = CalculateFScore(pres, recall);
PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, new List<Feature> { }, answers.ToList(), guesses.ConvertAll(x => (int)x));
finalResults.Add(pR);
}
return finalResults;
}
public double GetValue(SAMDataPoint sam)
{
if (dataReadings == null)
{
throw new Exception("ERROR: Du skal fandme lige bruge Feature.SetData() først!!");
}
if (!cachedResults.ContainsKey(sam))
{
cachedResults.Add(sam, featureCalculator(dataReadings, sam));
}
return(cachedResults[sam]);
}
public double GetValue(SAMDataPoint sam)
{
if (dataReadings == null)
{
throw new Exception("ERROR: Du skal fandme lige bruge Feature.SetData() først!!");
}
if (!cachedResults.ContainsKey(sam))
{
cachedResults.Add(sam, featureCalculator(dataReadings, sam));
}
return cachedResults[sam];
}
public static int GetNumberOfLabels(SAMDataPoint.FeelingModel feelingsModel)
{
switch (feelingsModel)
{
case SAMDataPoint.FeelingModel.Arousal2High:
case SAMDataPoint.FeelingModel.Arousal2Low:
case SAMDataPoint.FeelingModel.Valence2High:
case SAMDataPoint.FeelingModel.Valence2Low:
return 2;
case SAMDataPoint.FeelingModel.Valence3:
case SAMDataPoint.FeelingModel.Arousal3:
//case SAMDataPoint.FeelingModel.ValenceArousal3:
return 3;
//case SAMDataPoint.FeelingModel.Valence9:
//case SAMDataPoint.FeelingModel.Arousal9:
// return 9;
default:
return 0;
}
}
public PredictionResult DoBoosting(SAMDataPoint.FeelingModel feelingsmodel, int nFold, bool useIAPSratings = false, Normalize normalizeFormat = Normalize.OneMinusOne)
{
if (boostingOrder.Count != standardClassifiers.Count)
{
//if boosting order and standardClassifier is not the same size an out of bounds is invetatible
Log.LogMessage("The Boosting order list is not the same as the number of classifiers, I'm giving you a null");
return null;
}
PredictionResult prevResult = null;
for (int i = 0; i < boostingOrder.Count; i++)
{
if (i == 0)
{
prevResult = FindBestFScorePrediction(standardClassifiers[boostingOrder[i]].CrossValidate(feelingsmodel, useIAPSratings, normalizeFormat));
}
else
{
prevResult = FindBestFScorePrediction(standardClassifiers[boostingOrder[i]].CrossValidateWithBoosting(feelingsmodel, nFold, prevResult.guesses.ConvertAll(x => (double)x).ToArray(), useIAPSratings, normalizeFormat));
}
}
return prevResult;
}
/// <summary>
/// Run crossvalidation for the feature setup for this machine
/// </summary>
/// <param name="feelingsmodel"></param>
/// <param name="nFold"></param>
/// <param name="useIAPSratings"></param>
public List<PredictionResult> OldCrossValidate(SAMDataPoint.FeelingModel feelingsmodel, int nFold, bool useIAPSratings = false, Normalize normalizationType = Normalize.OneMinusOne)
{
List<PredictionResult> predictedResults = new List<PredictionResult>();
//Split into crossvalidation parts
List<Tuple<SVMProblem, SVMProblem>> problems = GetFeatureValues(features, samData).NormalizeFeatureList<double>(normalizationType).GetCrossValidationSets<double>(samData, feelingsmodel, nFold, useIAPSratings);
//Get correct results
int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray();
int progressCounter = 0;
bool postedOneClassError = false;
if(answers.Distinct().Count() <= 1)
{
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(answers.ToList().ConvertAll(x=>(double)x).ToArray(), answers, numberOfLabels);
List<double> pres = CalculatePrecision(confus, numberOfLabels);
List<double> recall = CalculateRecall(confus, numberOfLabels);
List<double> fscore = CalculateFScore(pres, recall);
PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, new SVMParameter(), features, answers.ToList(), answers.ToList().ConvertAll(x => (int)x));
predictedResults.Add(pR);
progressCounter++;
Log.LogMessage(ONLY_ONE_CLASS);
Log.LogMessage("");
return predictedResults;
}
foreach (SVMParameter SVMpara in Parameters)
{
if (UpdateCallback != null)
{
UpdateCallback(progressCounter, Parameters.Count);
}
List<double> guesses = new List<double>();
//model and predict each nfold
try
{
foreach (Tuple<SVMProblem, SVMProblem> tupleProblem in problems)
{
SVMModel trainingModel = tupleProblem.Item1.Train(SVMpara);
if (trainingModel.ClassCount <= 1)
{
if (!postedOneClassError)
{
Log.LogMessage(ONLY_ONE_CLASS_IN_TRAINING);
postedOneClassError = true;
}
guesses.AddRange(tupleProblem.Item1.Y.ToList().Take(tupleProblem.Item2.Y.Count()).ToList());
}
else
{
double[] d = tupleProblem.Item2.Predict(trainingModel);
guesses.AddRange(d);
}
}
}
catch(Exception e)
{
for (int i = 0; i < samData.dataPoints.Count; i++)
{
guesses.Add(-1);
}
}
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels);
List<double> pres = CalculatePrecision(confus, numberOfLabels);
List<double> recall = CalculateRecall(confus, numberOfLabels);
List<double> fscore = CalculateFScore(pres, recall);
PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, features, answers.ToList(), guesses.ConvertAll(x => (int)x));
predictedResults.Add(pR);
progressCounter++;
if (UpdateCallback != null)
{
UpdateCallback(progressCounter, Parameters.Count);
}
}
return predictedResults;
}
public void AddDataToPerson(string name, Book book, PredictionResult predictResult, SAMDataPoint.FeelingModel model)
{
Log.LogMessage("Writing results from " + name + " to excel files");
foreach (Excel.Worksheet ws in books[book].Sheets)
{
if (ws.Name == name)
{
WriteResult(ws, predictResult, model);
}
}
}
public PredictionResult DoVoting(SAMDataPoint.FeelingModel feelingsmodel, int nFold, bool useIAPSratings = false, Normalize normalizeFormat = Normalize.OneMinusOne)
{
List<PredictionResult> classifiers = new List<PredictionResult>();
//For each classifier run a crossvalidation and find the best params
int prg = 0;
foreach (StdClassifier classifier in standardClassifiers)
{
if (UpdateCallback != null)
{
UpdateCallback(prg++, standardClassifiers.Count);
}
List<PredictionResult> results = classifier.OldCrossValidate(feelingsmodel, 1, useIAPSratings, normalizeFormat);
classifiers.Add(results.OrderBy(x => x.GetAverageFScore()).First());
}
if (UpdateCallback != null)
{
UpdateCallback(standardClassifiers.Count, standardClassifiers.Count);
}
int labelCount = SAMData.GetNumberOfLabels(feelingsmodel);
//Full List of indicies
List<int> counter = new List<int>();
for (int k = 0; k < samData.dataPoints.Count(); k++)
{
counter.Add(k);
}
//Divide indicies into correct nfold
List<List<int>> trainIndicies = new List<List<int>>();
List<List<int>> predictIndicies = new List<List<int>>();
for (int i = 0; i < samData.dataPoints.Count(); i += nFold)
{
var temp = counter.Skip(i).Take(nFold).ToList();
predictIndicies.Add(temp);
trainIndicies.Add(counter.Except(temp).ToList());
}
List<Dictionary<int, double>> weightedGuesses = new List<Dictionary<int, double>>();
//Fill up weightedGuesses List
for (int nGuesses = 0; nGuesses < samData.dataPoints.Count; nGuesses++)
{
Dictionary<int, double> tempGuess = new Dictionary<int, double>();
for (int indexClass = 0; indexClass < labelCount; indexClass++)
{
tempGuess.Add(indexClass, 0);
}
weightedGuesses.Add(tempGuess);
}
//Split classifiers
for (int i = 0; i < trainIndicies.Count; i++)
{
foreach (PredictionResult predictResult in classifiers)
{
double correct = 0;
//calculate weights
for (int trainingIndex = 0; trainingIndex < trainIndicies[i].Count; trainingIndex++)
{
if (predictResult.guesses[trainIndicies[i][trainingIndex]] == samData.dataPoints[trainIndicies[i][trainingIndex]].ToAVCoordinate(feelingsmodel))
{
correct++;
}
}
//Add weight from the trainingset to each of the guesses
weightedGuesses[i][predictResult.guesses[i]] += (correct / trainIndicies.Count);
}
}
//Calculate final answers
List<double> guesses = new List<double>();
foreach (Dictionary<int, double> answer in weightedGuesses)
{
int tempKey = -1;
double tempMax = -1;
foreach (int key in answer.Keys)
{
if (answer[key] > tempMax)
{
tempKey = key;
tempMax = answer[key];
}
}
guesses.Add(tempKey);
}
//Get correct results
int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray();
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels);
List<double> pres = CalculatePrecision(confus, numberOfLabels);
List<double> recall = CalculateRecall(confus, numberOfLabels);
List<double> fscore = CalculateFScore(pres, recall);
return new PredictionResult(confus, recall, pres, fscore, new SVMParameter(), new List<Feature> { }, answers.ToList(), guesses.ConvertAll(x => (int)x));
}
public static List<Feature> GetFeatures(string machine, SAMDataPoint.FeelingModel feel)
{
bool valence = ((int)feel) < 3;
if (valence)
{
if (machine == "HR") return HRValenceOptimizationFeatures;
if (machine == "EEG") return EEGValenceOptimizationFeatures;
if (machine == "FACE") return FACEValenceOptimizationFeatures;
}
else
{
if (machine == "GSR") return GSRArousalOptimizationFeatures;
if (machine == "HR") return HRArousalOptimizationFeatures;
if (machine == "EEG") return EEGArousalOptimizationFeatures;
if (machine == "FACE") return FACEArousalOptimizationFeatures;
}
return null;
}
public static List<DataReading> EEGDataSlice(List<DataReading> data, SAMDataPoint sam)
{
return data.SkipWhile(x => x.timestamp < sam.timeOffset + EEG_LATENCY).TakeWhile(x => x.timestamp < sam.timeOffset + EEG_LATENCY + EEG_DURATION).ToList();
}
public List<PredictionResult> CrossValidate(SAMDataPoint.FeelingModel feelingsmodel, bool useIAPSratings = false, Normalize normalizationType = Normalize.OneMinusOne)
{
List<PredictionResult> predictedResults = new List<PredictionResult>();
//Split into crossvalidation parts
SVMProblem problems = GetFeatureValues(features, samData).NormalizeFeatureList<double>(normalizationType).CreateCompleteProblem(samData, feelingsmodel);
//Get correct results
int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray();
int progressCounter = 0;
if (answers.Distinct().Count() <= 1)
{
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(answers.ToList().ConvertAll(x => (double)x).ToArray(), answers, numberOfLabels);
List<double> pres = CalculatePrecision(confus, numberOfLabels);
List<double> recall = CalculateRecall(confus, numberOfLabels);
List<double> fscore = CalculateFScore(pres, recall);
PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, new SVMParameter(), features, answers.ToList(), answers.ToList().ConvertAll(x => (int)x));
predictedResults.Add(pR);
progressCounter++;
Log.LogMessage(ONLY_ONE_CLASS);
Log.LogMessage("");
return predictedResults;
}
else if(problems.X.Count == 0)
{
Log.LogMessage("Empty problem in "+ Name);
return null;
}
foreach (SVMParameter SVMpara in Parameters)
{
if (UpdateCallback != null)
{
UpdateCallback(progressCounter, Parameters.Count);
}
double[] guesses = new double[samData.dataPoints.Count];
//model and predict each nfold
try
{
problems.CrossValidation(SVMpara, samData.dataPoints.Count, out guesses);
}
catch (Exception e)
{
for (int i = 0; i < samData.dataPoints.Count; i++)
{
guesses[i] = -1;
}
}
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels);
List<double> pres = CalculatePrecision(confus, numberOfLabels);
List<double> recall = CalculateRecall(confus, numberOfLabels);
List<double> fscore = CalculateFScore(pres, recall);
PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, features, answers.ToList(), Array.ConvertAll(guesses, (x=> (int)x)).ToList());
predictedResults.Add(pR);
progressCounter++;
if (UpdateCallback != null)
{
UpdateCallback(progressCounter, Parameters.Count);
}
}
return predictedResults;
}
public static List<DataReading> FaceDataSlice(List<DataReading> data, SAMDataPoint sam)
{
var d = data.SkipWhile(x => x.timestamp < sam.timeOffset + FACE_LATENCY).TakeWhile(x => x.timestamp < sam.timeOffset + FACE_LATENCY + FACE_DURATION).ToList();
if (d.Count == 0)
{
int j = 0;
}
return d;
}
private void WriteResult(Excel.Worksheet workSheet, PredictionResult pResult, SAMDataPoint.FeelingModel feelingModel)
{
int counter = 0;
switch (feelingModel)
{
case SAMDataPoint.FeelingModel.Arousal2High:
counter = A2HighStart;
break;
case SAMDataPoint.FeelingModel.Arousal2Low:
counter = A2LowStart;
break;
case SAMDataPoint.FeelingModel.Arousal3:
counter = A3Start;
break;
case SAMDataPoint.FeelingModel.Valence2High:
counter = V2HighStart;
break;
case SAMDataPoint.FeelingModel.Valence2Low:
counter = V2LowStart;
break;
case SAMDataPoint.FeelingModel.Valence3:
counter = V3Start;
break;
}
workSheet.Cells[counter, 3] = pResult.GetAccuracy();
counter++;
workSheet.Cells[counter, 3] = pResult.GetAverageFScore();
for (int f = 0; f < pResult.fscores.Count; f++)
{
counter++;
if (double.IsNaN(pResult.fscores[f]))
{
workSheet.Cells[counter, 3] = "NaN";
}
else
{
workSheet.Cells[counter, 3] = pResult.fscores[f];
}
}
for (int p = 0; p < pResult.precisions.Count; p++)
{
counter++;
if (double.IsNaN(pResult.precisions[p]))
{
workSheet.Cells[counter, 3] = "NaN";
}
else
{
workSheet.Cells[counter, 3] = pResult.precisions[p];
}
}
for (int r = 0; r < pResult.recalls.Count; r++)
{
counter++;
if (double.IsNaN(pResult.recalls[r]))
{
workSheet.Cells[counter, 3] = "NaN";
}
else
{
workSheet.Cells[counter, 3] = pResult.recalls[r];
}
}
counter++;
for (int i = 3; i < pResult.features.Count + 3; i++)
{
workSheet.Cells[counter, i] = pResult.features[i - 3].name;
}
counter++;
workSheet.Cells[counter, 3] = pResult.svmParams.C;
counter++;
workSheet.Cells[counter, 3] = pResult.svmParams.Gamma;
counter++;
workSheet.Cells[counter, 3] = pResult.svmParams.Kernel;
}
public List<PredictionResult> CrossValidateWithBoosting(SAMDataPoint.FeelingModel feelingsmodel, int nFold, double[] answersFromPrevious, bool useIAPSratings = false, Normalize normalizationType = Normalize.OneMinusOne)
{
List<PredictionResult> predictedResults = new List<PredictionResult>();
List<List<double>> tempFeatuers = GetFeatureValues(features, samData);
if (answersFromPrevious.Length != tempFeatuers.Count)
{
//answers from previous is not the same size as current feature list, e.g. something is wrong
Log.LogMessage("The number of guessses from previous machine is the same as number of datapoints in this");
return null;
}
//Split into crossvalidation parts
List<List<double>> tempFeatures = tempFeatuers.NormalizeFeatureList<double>(normalizationType).ToList();
for (int i = 0; i < tempFeatuers.Count; i++)
{
tempFeatuers[i].Add(answersFromPrevious[i]);
}
List<Tuple<SVMProblem, SVMProblem>> problems = tempFeatuers.GetCrossValidationSets<double>(samData, feelingsmodel, nFold, useIAPSratings);
//Get correct results
int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray();
foreach (SVMParameter SVMpara in Parameters)
{
List<double> guesses = new List<double>();
//model and predict each nfold
foreach (Tuple<SVMProblem, SVMProblem> tupleProblem in problems)
{
guesses.AddRange(tupleProblem.Item2.Predict(tupleProblem.Item1.Train(SVMpara)));
}
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels);
List<double> pres = CalculatePrecision(confus, numberOfLabels);
List<double> recall = CalculateRecall(confus, numberOfLabels);
List<double> fscore = CalculateFScore(pres, recall);
PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, features, answers.ToList(), guesses.ConvertAll(x => (int)x).ToList());
predictedResults.Add(pR);
}
return predictedResults;
}
/// <summary>
/// Run crossvalidation for each combination of the features for this machine
/// </summary>
/// <param name="feelingsmodel"></param>
/// <param name="nFold"></param>
/// <param name="useIAPSratings"></param>
public List<PredictionResult> CrossValidateCombinations(SAMDataPoint.FeelingModel feelingsmodel, int nFold, bool useIAPSratings = false, Normalize normalizationType = Normalize.OneMinusOne)
{
List<List<bool>> combinations = CalculateCombinations(new List<bool>() { }, features.Count);
//Get different combination of problems
List<Tuple<List<Tuple<SVMProblem, SVMProblem>>, List<Feature>>> featureCombinationProblems = new List<Tuple<List<Tuple<SVMProblem, SVMProblem>>, List<Feature>>>();
for (int i = 0; i < combinations.Count; i++)
{
List<Feature> tempFeatures = new List<Feature>();
for (int j = 0; j < combinations[i].Count; j++)
{
// For each feature combination save the different problems for crossvalidation
if (combinations[i][j] == true)
{
tempFeatures.Add(features[j]);
}
}
featureCombinationProblems.Add
(
new Tuple<List<Tuple<SVMProblem, SVMProblem>>, List<Feature>>
(
GetFeatureValues(tempFeatures, samData).NormalizeFeatureList<double>(normalizationType).GetCrossValidationSets<double>(samData, feelingsmodel, nFold, useIAPSratings),
tempFeatures
)
);
}
//Get correct results
int[] answers = samData.dataPoints.Select(x => x.ToAVCoordinate(feelingsmodel, useIAPSratings)).ToArray();
int progressCounter = 0;
List<PredictionResult> predictionResults = new List<PredictionResult>();
foreach (SVMParameter SVMpara in Parameters)
{
//For each feature setup
for (int n = 0; n < featureCombinationProblems.Count; n++)
{
if (UpdateCallback != null)
{
UpdateCallback(progressCounter, Parameters.Count * featureCombinationProblems.Count);
}
//PrintProgress(progressCounter, featureCombinationProblems.Count);
List<double> guesses = new List<double>();
//model and predict each nfold
foreach (var tupleProblem in featureCombinationProblems[n].Item1)
{
guesses.AddRange(tupleProblem.Item2.Predict(tupleProblem.Item1.Train(SVMpara)));
}
int numberOfLabels = SAMData.GetNumberOfLabels(feelingsmodel);
//Calculate scoring results
double[,] confus = CalculateConfusion(guesses.ToArray(), answers, numberOfLabels);
List<double> pres = CalculatePrecision(confus, numberOfLabels);
List<double> recall = CalculateRecall(confus, numberOfLabels);
List<double> fscore = CalculateFScore(pres, recall);
PredictionResult pR = new PredictionResult(confus, recall, pres, fscore, SVMpara, featureCombinationProblems[n].Item2, answers.ToList(), guesses.ConvertAll(x => (int)x));
predictionResults.Add(pR);
progressCounter++;
}
}
if (UpdateCallback != null)
{
UpdateCallback(progressCounter, Parameters.Count * featureCombinationProblems.Count);
}
return predictionResults;
}
Thread CreateMachineThread(string name, List<SVMParameter> pars, List<Feature> feats, SAMDataPoint.FeelingModel feels, Action<int, int> UpdateCallback, bool useControlSAM)
{
return new Thread(() =>
{
StdClassifier mac = new StdClassifier(name, pars, feats, samData);
mac.UpdateCallback = UpdateCallback;
var res = mac.OldCrossValidate(feels, 1, useControlSAM);
SaveBestResult(res, mac.Name + "_" + feels);
});
}
public static List <DataReading> HRDataSlice(List <DataReading> data, SAMDataPoint sam)
{
return(data.SkipWhile(x => x.timestamp < sam.timeOffset + HR_LATENCY).TakeWhile(x => x.timestamp < sam.timeOffset + HR_LATENCY + HR_DURATION).ToList());
}