public void Interpolate()
{
var regressorFactory = new LinearRegressorFactory();
foreach (var key in Keys)
{
var readings = this[key];
var regressorPair = regressorFactory.GetAutoCalibrated(key);
if (regressorPair == null)
{
continue;
}
Action <PanTiltAxis> interpolateAxis = (axis) =>
{
var calibrationReadings = readings[axis];
var pixelDeviations = calibrationReadings.Keys.ToList();
pixelDeviations.Sort();
var minimumDeviation = pixelDeviations[0];
var maximumDeviation = pixelDeviations[pixelDeviations.Count - 1];
for (int i = minimumDeviation; i < maximumDeviation; i++)
{
if (!calibrationReadings.ContainsKey(i))
{
var readingSet = new ReadingSet();
readingSet.Accepted = regressorPair[axis].Calculate(i);
readingSet.IsInterpolated = true;
calibrationReadings.Add(i, readingSet);
}
}
};
interpolateAxis(PanTiltAxis.Horizontal);
interpolateAxis(PanTiltAxis.Vertical);
}
}
/// <summary>
/// Perform target alignment
/// </summary>
/// <param name="evidences">Evidences to align</param>
/// <param name="baseline">Baseline evidences</param>
/// <returns></returns>
public LinearRegressionResult AlignTargets(List <Evidence> evidences, List <Evidence> baseline)
{
var observed = new List <double>();
var predicted = new List <double>();
foreach (var evidence in baseline)
{
Predictor = RetentionTimePredictorFactory.CreatePredictor(Options.PredictorType);
evidence.PredictedNet = Predictor.GetElutionTime(Evidence.CleanSequence(evidence.Sequence));
observed.Add(evidence.ObservedNet);
predicted.Add(evidence.PredictedNet);
}
Regressor = LinearRegressorFactory.Create(Options.RegressionType);
var result = Regressor.CalculateRegression(observed, predicted);
foreach (var evidence in evidences)
{
evidence.ObservedNet = Regressor.Transform(result, evidence.ObservedNet);
}
return(result);
}
