public bool TestMLMainModelFolderSource(String folderPath)
{
if (FastTreePredictor.IsMLModelExisting(folderPath) &&
LottoMatchCountPredictor.IsMLModelExisting(folderPath) &&
DrawResultWinCountPredictor.IsMLModelExisting(folderPath) &&
SDCARegressionPredictor.IsMLModelExisting(folderPath))
{
return(true);
}
return(false);
}
public List <int[]> GenerateSequence()
{
StartPickGeneration();
int maximumPickCount = GetFieldParamValueForCount(0);
int matchPerc = GetFieldParamValueForCount(1);
int maxLoopBreaker = int.MaxValue - 100;
int maxLoopCtr = 0;
List <int[]> results = new List <int[]>();
LottoMatchCountInputModel sampleData;
LotteryBetSetup lotteryBet = new LotteryBetSetup();
lotteryBet.GameCode = lotteryDataServices.LotteryDetails.GameCode;
Random ran = new Random();
while (results.Count < maximumPickCount)
{
int[] randomSeq = LuckyPickGenerator(ran);
lotteryBet.ResetSequenceToZero();
lotteryBet.FillNumberBySeq(randomSeq);
sampleData = lotteryBet.GetLottoMatchCountInputModel();
LottoMatchCountOutputModel output = LottoMatchCountPredictor.Predict(sampleData);
int score = (int)(output.Score * 100);
PickGenerationProgressEvent.IncrementGenerationAttemptCount();
if (score >= matchPerc)
{
Array.Sort(randomSeq);
results.Add(randomSeq);
PickGenerationProgressEvent.IncrementGeneratedPickCount();
}
if (!IsContinuePickGenerationProgress())
{
break;
}
if (maxLoopCtr++ > maxLoopBreaker)
{
break;
}
}
return(results);
}
