/// <summary>
/// Calc
/// </summary>
/// <param name="ratingthreshold"></param>
/// <param name="prevSplitMaxSof"></param>
/// <returns></returns>
public List <PredictionOfSplits> CuttedVariation(int ratingthreshold, int prevSplitMaxSof, int mincars)
{
List <PredictionOfSplits> ret = new List <PredictionOfSplits>();
foreach (var classDif in RatingDiffPerClassPercent)
{
if (classDif.Value > 50)
{
int classIndex = CurrentSplit.GetClassIndexOfId(classDif.Key);
int mostPopClassIndex = NextSplit.GetLastClassIndex();
if (classIndex != mostPopClassIndex)
{
var classcars = CurrentSplit.GetClassCars(classIndex);
var firstcarRating = classcars.First().rating;
var lastcarRating = classcars.Last().rating;
if (lastcarRating < ratingthreshold && firstcarRating > ratingthreshold * 0.6d)
{
PredictionOfSplits alternative = new PredictionOfSplits();
alternative.CurrentSplit = Data.Tools.SplitCloner(CurrentSplit);
alternative.NextSplit = Data.Tools.SplitCloner(NextSplit);
int cars = alternative.CurrentSplit.CountClassCars(classIndex);
if (cars >= mincars * 2)
{
int carsToMove = Convert.ToInt32(Math.Ceiling(Convert.ToDouble(cars) / 2d));
if (carsToMove >= mincars)
{
// cut this class and move down half
var pick = alternative.CurrentSplit.PickClassCars(classIndex, carsToMove, true);
if (alternative.NextSplit.GetClassId(classIndex) == 0)
{
alternative.NextSplit.SetClass(classIndex, classDif.Key);
}
alternative.NextSplit.AppendClassCars(classIndex, pick);
// fill available slots with most pop
pick = alternative.NextSplit.PickClassCars(mostPopClassIndex, carsToMove, false);
alternative.CurrentSplit.AppendClassCars(mostPopClassIndex, pick);
alternative.CalcStats(prevSplitMaxSof);
ClassesCuttedAroundRatingThreshold.Add(classDif.Key);
ret.Add(alternative);
}
}
}
}
}
}
return(ret);
}
/// <summary>
/// Calc all the statistics needed to take the right decision
/// </summary>
/// <param name="prevSplitMaxSof"></param>
public void CalcStats(int prevSplitMaxSof)
{
RatingDiffPerClassPoints = new Dictionary <int, int>();
RatingDiffPerClassPercent = new Dictionary <int, double>();
ClassesCuttedAroundRatingThreshold = new List <int>();
NumberOfClasses = CurrentSplit.GetClassesCount();
var indexes = CurrentSplit.GetClassesIndex();
int maxIndex = indexes.Max();
int minIndex = indexes.Min();
int c = maxIndex - minIndex + 1;
NoMiddleClassesMissing = NumberOfClasses == c;
DiffBetweenClassesPoints = CurrentSplit.GetMaxClassSof() - CurrentSplit.GetMinClassSof();
DiffBetweenClassesPercent = Calc.SofDifferenceEvaluator.CalcDiff(CurrentSplit.GetMaxClassSof(), CurrentSplit.GetMinClassSof());
AllSofsHigherThanNextSplitMax = CurrentSplit.GetMinClassSof() > NextSplit.GetMaxClassSof();
AllSofsLowerThanPrevSplitMax = CurrentSplit.GetMaxClassSof() > prevSplitMaxSof;
var classesIndex = CurrentSplit.GetClassesIndex();
foreach (int classIndex in classesIndex)
{
int classId = CurrentSplit.GetClassId(classIndex);
var cars = CurrentSplit.GetClassCars(classIndex);
double min = (from r in cars select r.rating).Min();
double max = (from r in cars select r.rating).Max();
RatingDiffPerClassPoints.Add(classId, Convert.ToInt32(max - min));
RatingDiffPerClassPercent.Add(classId, Calc.SofDifferenceEvaluator.CalcDiff(min, max));
}
DiffBetweenMinCurrentSplitSofAndMaxNextSplitSof = Calc.SofDifferenceEvaluator.CalcDiff(CurrentSplit.GetMinClassSof(), NextSplit.GetMaxClassSof(), false);
//if (DiffBetweenMinCurrentSplitSofAndMaxNextSplitSof < 0) DiffBetweenMinCurrentSplitSofAndMaxNextSplitSof = 999;
int mostPopSof = CurrentSplit.GetClassSof(CurrentSplit.GetLastClassIndex());
MostPopulatedClassIsTheMaxSox = mostPopSof == CurrentSplit.GetMaxClassSof();
}