/// <summary>
/// The by average steps execute.
/// </summary>
/// <returns>
/// The <see cref="LotteryModel"/>.
/// </returns>
public LotteryModel ByAverageStepsExecute()
{
LotteryModel lm = new LotteryModel(lotteryRule);
int start2 = GetRandomNumber(lotteryRule);
lm.AddNumber(start2);
foreach (double d in this.LotteryStatistic.AvarageStepByStep)
{
lm.AddNumber(lm.Numbers.Last() + (int)Math.Round(d, 0));
}
return(lm);
}
/// <summary>
/// The by occurrence execute.
/// </summary>
/// <returns>
/// The <see cref="LotteryModel"/>.
/// </returns>
public LotteryModel ByOccurrenceExecute()
{
LotteryModel lm = new LotteryModel(lotteryRule);
var getSections = this.NumberSections.Last(x => this.lotteryCollection.Last().Numbers.First() == x.ActualNumber);
Random random = new Random();
int start2 = random.Next(0, getSections.SpecifyNumberList.Count);
var generateNumber = (SpecifyNumber)getSections.SpecifyNumberList[start2].Clone();
lm.AddNumber(generateNumber.NextNumber);
for (int k = 0; k < lotteryRule.PiecesOfDrawNumber - 1; k++)
{
getSections = this.NumberSections.First(x => lm.Numbers.Last() == x.ActualNumber);
while (true)
{
random = new Random();
if (getSections.SpecifyNumberList.Count > 10)
{
start2 = random.Next(0, getSections.SpecifyNumberList.Count);
generateNumber = (SpecifyNumber)getSections.SpecifyNumberList[start2].Clone();
}
else
{
start2 = GetRandomNumber(lotteryRule);
generateNumber = new SpecifyNumber(start2);
}
if (!lm.Numbers.Contains(generateNumber.NextNumber))
{
break;
}
}
lm.AddNumber(generateNumber.NextNumber);
}
return(lm);
}
/// <summary>
/// The by average randoms execute.
/// </summary>
/// <returns>
/// The <see cref="LotteryModel"/>.
/// </returns>
public LotteryModel ByAverageRandomsExecute()
{
LotteryModel lm = new LotteryModel(lotteryRule);
for (int i = 0; i < lotteryRule.PiecesOfDrawNumber; i++)
{
var goal = this.LotteryStatistic.AvarageRandom[i];
int id = 0;
while (true)
{
if (id == (int)goal)
{
lm.AddNumber(GetRandomNumber(lotteryRule));
break;
}
id++;
}
}
return(lm);
}
/// <summary>
/// The by sums execute.
/// </summary>
/// <returns>
/// The <see cref="LotteryModel"/>.
/// </returns>
public LotteryModel BySumsExecute()
{
var getLastSum = this.lotteryCollection.Last().Sum;
// find another same sum in list
LotteryModel getPenultimateSumId;
while (true)
{
Random rnd = new Random();
var foundLastSum = lotteryCollection.Select((v, i) => new { Index = i, Value = v }).Where(x => x.Value.Sum == getLastSum)
.Select(x => x.Value.Id)
.ToList();
if (foundLastSum.Count > 2)
{
var tek = rnd.Next(1, foundLastSum.Count - 1);
getPenultimateSumId = this.lotteryCollection.FirstOrDefault(x => x.Id == foundLastSum[tek] + 1);
break;
}
getLastSum++;
}
LotteryModel lm = new LotteryModel(lotteryRule);
for (int i = 0; i < lotteryRule.PiecesOfDrawNumber; i++)
{
lm.AddNumber(GetRandomNumber(lotteryRule));
}
if (getPenultimateSumId != null && (lm.Sum >= getPenultimateSumId.Sum - 10 && lm.Sum <= getPenultimateSumId.Sum + 10))
{
return(lm);
}
return(null);
}
public LotteryModel ByMachineLearningExecute()
{
try
{
if (this.lotteryRule.LotteryType != Enums.LotteryType.TheFiveNumberDraw)
{
return(null); //TODO: Make this for 6 and custom lottery draw
}
LotteryModel lm = new LotteryModel(lotteryRule);
var dbl = new List <LotteryResult>();
foreach (LotteryModel lotteryModel in this.lotteryCollection)
{
var res = new LotteryResult(
lotteryModel.Numbers[0],
lotteryModel.Numbers[1],
lotteryModel.Numbers[2],
lotteryModel.Numbers[3],
lotteryModel.Numbers[4]
);
dbl.Add(res);
}
dbl.Reverse();
var deep = 20;
var network = new BasicNetwork();
network.AddLayer(
new BasicLayer(null, true, 5 * deep));
network.AddLayer(
new BasicLayer(
new ActivationSigmoid(), true, 4 * 5 * deep));
network.AddLayer(
new BasicLayer(
new ActivationSigmoid(), true, 4 * 5 * deep));
network.AddLayer(
new BasicLayer(
new ActivationLinear(), true, 5));
network.Structure.FinalizeStructure();
var learningInput = new double[deep][];
for (int i = 0; i < deep; ++i)
{
learningInput[i] = new double[deep * 5];
for (int j = 0, k = 0; j < deep; ++j)
{
var idx = 2 * deep - i - j;
LotteryResult data = dbl[idx];
learningInput[i][k++] = (double)data.V1;
learningInput[i][k++] = (double)data.V2;
learningInput[i][k++] = (double)data.V3;
learningInput[i][k++] = (double)data.V4;
learningInput[i][k++] = (double)data.V5;
}
}
var learningOutput = new double[deep][];
for (int i = 0; i < deep; ++i)
{
var idx = deep - 1 - i;
var data = dbl[idx];
learningOutput[i] = new double[5]
{
(double)data.V1,
(double)data.V2,
(double)data.V3,
(double)data.V4,
(double)data.V5
};
}
var trainingSet = new BasicMLDataSet(
learningInput,
learningOutput);
var train = new ResilientPropagation(
network, trainingSet);
train.NumThreads = Environment.ProcessorCount;
START:
network.Reset();
RETRY:
var step = 0;
do
{
train.Iteration();
Console.WriteLine("Train Error: {0}", train.Error);
++step;
}while (train.Error > 0.001 && step < 20);
var passedCount = 0;
for (var i = 0; i < deep; ++i)
{
var should =
new LotteryResult(learningOutput[i]);
var inputn = new BasicMLData(5 * deep);
Array.Copy(
learningInput[i],
inputn.Data,
inputn.Data.Length);
var comput =
new LotteryResult(
((BasicMLData)network.
Compute(inputn)).Data);
var passed = should.ToString() == comput.ToString();
if (passed)
{
Console.ForegroundColor = ConsoleColor.Green;
++passedCount;
}
}
var input = new BasicMLData(5 * deep);
for (int i = 0, k = 0; i < deep; ++i)
{
var idx = deep - 1 - i;
var data = dbl[idx];
input.Data[k++] = (double)data.V1;
input.Data[k++] = (double)data.V2;
input.Data[k++] = (double)data.V3;
input.Data[k++] = (double)data.V4;
input.Data[k++] = (double)data.V5;
}
var perfect = dbl[0];
LotteryResult predict = new LotteryResult(
((BasicMLData)network.Compute(input)).Data);
Console.WriteLine("Predict: {0}", predict);
if (predict.IsOut())
{
goto START;
}
var t = passedCount < (deep * (double)9 / (double)10);
var isvalid = predict.IsValid();
if (t ||
!isvalid)
{
goto RETRY;
}
lm.AddNumber(predict.V1);
lm.AddNumber(predict.V2);
lm.AddNumber(predict.V3);
lm.AddNumber(predict.V4);
lm.AddNumber(predict.V5);
return(lm);
}
catch (Exception exception)
{
Console.WriteLine(exception.ToString());
return(null);
}
}
