internal static List<double> NormalizedInputForMemoryLearning(StarterInfo si, int ppCount)
{
var input = new List<double>();
if (si.CountPreceding >= ppCount)
{
si.GetPreviousPerformances(ppCount).ToList().ForEach(si2 => input.Add(si2.GoldenFigure));
double min = input.Min();
double max = input.Max();
for (int i = 0; i < input.Count; ++i)
{
input[i] = (input[i] - min) / max;
}
PopulateNormalizedDataForMemoryLearning(si, input, ppCount);
input.Add(NormalizeDaysOff(si));
input.Add(NormalizePostPosition(si));
input.Add(NormalizeDistance(si));
input.Add(NormalizeBoolean(si.RestrictedToFemalesOnly));
input.Add(NormalizeBoolean(si.RestrictedToStateBredsOnly));
input.Add(NormalizeBoolean(si.WasClaimed));
input.Add(NormalizeBoolean(si.BlinkersOff));
input.Add(NormalizeBoolean(si.FirstTimeLasix));
input.Add(NormalizeBoolean(si.RestrictedToTwoYearsOnly));
input.Add(NormalizeBoolean(si.RestrictedToThreeYearsOnly));
double desiredValue = si.GoldenFigure > max ? 1.0 : 0.0;
input.Add(desiredValue);
}
return input;
}
internal static List<double> NormalizedInputForImprovementDetection(StarterInfo si, int ppCount)
{
var input = new List<double>();
var gf = new List<double>();
si.GetPreviousPerformances(ppCount).ToList().ForEach(si2 => gf.Add(si2.GoldenFigure));
double min = gf.Min();
double max = gf.Max();
PopulateNormalizedData(si, input, min, max, ppCount);
input.Add(NormalizeDaysOff(si));
input.Add(NormalizePostPosition(si));
input.Add(NormalizeDistance(si));
return input;
}
private static void PopulateNormalizedDataForMemoryLearning(StarterInfo starterInfo, List<double> input, int count)
{
starterInfo.GetPreviousPerformances(count).ToList().ForEach(
si =>
{
input.Add(NormalizeTrackCondition(si));
input.Add(NormalizeOdds(si));
input.Add(NormalizeDistance(si));
input.Add(NormalizePosition(si.FinishPosition));
input.Add(NormalizePosition(si.SecondCallPosition));
input.Add(NormalizeDaysOff(si));
input.Add(NormalizeRaceType(si));
input.Add(NormalizePostPosition(si));
input.Add(NormalizeBoolean(si.RestrictedToFemalesOnly));
input.Add(NormalizeBoolean(si.RestrictedToStateBredsOnly));
input.Add(NormalizeBoolean(si.WasClaimed));
input.Add(NormalizeBoolean(si.BlinkersOff));
input.Add(NormalizeBoolean(si.FirstTimeLasix));
input.Add(NormalizeBoolean(si.RestrictedToTwoYearsOnly));
input.Add(NormalizeBoolean(si.RestrictedToThreeYearsOnly));
});
}
private static void PopulateNormalizedData(StarterInfo starterInfo, List<double> normalizedData, double min, double max, double average, int count)
{
starterInfo.GetPreviousPerformances(count).ToList().ForEach(
si =>
{
normalizedData.Add(NormalizeGoldenFigure(si.GoldenFigure, min, max, average));
// normalizedData.Add(NormalizeGoldenFigure(si.WinnersGoldenFigure, min, max, average));
normalizedData.Add(NormalizeTrackCondition(si));
// normalizedData.Add(NormalizeOdds(si));
normalizedData.Add(NormalizeDistance(si));
//normalizedData.Add(NormalizePosition(si.FinishPosition));
/*
normalizedData.Add(NormalizePosition(si.SecondCallPosition));
normalizedData.Add(NormalizeDaysOff(si));
normalizedData.Add(NormalizeRaceType(si));
normalizedData.Add(NormalizePostPosition(si));
normalizedData.Add(NormalizeBoolean(si.RestrictedToFemalesOnly));
normalizedData.Add(NormalizeBoolean(si.RestrictedToStateBredsOnly));
normalizedData.Add(NormalizeBoolean(si.WasClaimed));
normalizedData.Add(NormalizeBoolean(si.BlinkersOff));
normalizedData.Add(NormalizeBoolean(si.FirstTimeLasix));
normalizedData.Add(NormalizeBoolean(si.RestrictedToTwoYearsOnly));
normalizedData.Add(NormalizeBoolean(si.RestrictedToThreeYearsOnly));
*/
});
}
internal static List<double> NormalizedInputToChooseBetweenFirstAndSecondFavorite(StarterInfo favorite, StarterInfo secondFavorite, StarterInfo thirdFavorite, int ppCount)
{
var input = new List<double>();
var gf = new List<double>();
favorite.GetPreviousPerformances(ppCount).ToList().ForEach(si => gf.Add(si.GoldenFigure));
// favorite.GetPreviousPerformances(ppCount).ToList().ForEach(si => gf.Add(si.WinnersGoldenFigure));
secondFavorite.GetPreviousPerformances(ppCount).ToList().ForEach(si => gf.Add(si.GoldenFigure));
//secondFavorite.GetPreviousPerformances(ppCount).ToList().ForEach(si => gf.Add(si.WinnersGoldenFigure));
thirdFavorite.GetPreviousPerformances(ppCount).ToList().ForEach(si => gf.Add(si.GoldenFigure));
double min = gf.Min();
double max = gf.Max();
PopulateNormalizedData(favorite, input, min, max, gf.Average(),ppCount);
PopulateNormalizedData(secondFavorite, input, min, max, gf.Average(), ppCount);
PopulateNormalizedData(thirdFavorite, input, min, max, gf.Average(), ppCount);
input.Add(NormalizeDaysOff(favorite));
input.Add(NormalizeDaysOff(secondFavorite));
input.Add(NormalizeDaysOff(thirdFavorite));
//input.Add(NormalizeOdds(favorite));
//input.Add(NormalizeOdds(secondFavorite));
//input.Add(NormalizeOdds(thirdFavorite));
//input.Add(NormalizePostPosition(favorite));
//input.Add(NormalizePostPosition(secondFavorite));
//input.Add(NormalizePostPosition(thirdFavorite));
//input.Add(NormalizeDistance(favorite));
return input;
}