static bool CreateDatabase(string fileDB, out LottoListResults dbl)
{
dbl = new LottoListResults();
using (var reader = File.OpenText(fileDB))
{
var line = string.Empty;
while ((line = reader.ReadLine()) != null)
{
var values = line.Split(' ', '\t')[2].Split(',');
var res = new LottoResult(values.Select(s => double.Parse(s)).ToArray());
dbl.Add(res);
}
}
dbl.Reverse();
return(true);
}
static void Main(string[] args)
{
var app = new CommandLineApplication();
app.Name = "LotteryPredictor";
app.Description = "ML/Encog C# Lotto (MegaSena) Predictor";
app.HelpOption("-? | -h | --help");
var dataOption = app.Option("-d | --data", "training data (tsv file)", CommandOptionType.SingleValue);
app.OnExecute(() =>
{
if (!dataOption.HasValue())
{
app.ShowHint();
return(1);
}
var fileDB = dataOption.Value();
try
{
var deep = 20;
LottoListResults dbl = null;
if (CreateDatabase(fileDB, out dbl))
{
var network = CreateNetwork(deep);
var learningInput = BuildLearningInput(dbl, deep);
var learningOutput = BuildLearningOutput(dbl, deep);
var trainingSet = new BasicMLDataSet(learningInput, learningOutput);
var train = new ResilientPropagation(network, trainingSet);
train.NumThreads = Environment.ProcessorCount;
START:
network.Reset();
RETRY:
// train step...
var step = 0;
do
{
train.Iteration();
Console.WriteLine("Train Error: {0}", train.Error);
++step;
} while (train.Error > 0.001 && step < 20);
// validate...
var passedCount = 0;
for (var i = 0; i < deep; ++i)
{
var should = new LottoResult(learningOutput[i]);
var inputn = new BasicMLData(6 * deep);
Array.Copy(learningInput[i], inputn.Data, inputn.Data.Length);
var comput = new LottoResult(((BasicMLData)network.Compute(inputn)).Data);
var passed = should.ToString() == comput.ToString();
if (passed)
{
Console.ForegroundColor = ConsoleColor.Green;
++passedCount;
}
else
{
Console.ForegroundColor = ConsoleColor.Red;
}
Console.WriteLine("{0} {1} {2} {3}",
should.ToString().PadLeft(17, ' '),
passed ? "==" : "!=",
comput.ToString().PadRight(17, ' '),
passed ? "PASS" : "FAIL");
Console.ResetColor();
}
// predict!
var input = new BasicMLData(6 * 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;
input.Data[k++] = (double)data.V6;
}
var perfect = dbl[0];
var predict = new LottoResult(((BasicMLData)network.Compute(input)).Data);
Console.ForegroundColor = ConsoleColor.Yellow;
Console.WriteLine("Predict: {0}", predict);
Console.ResetColor();
// restart if values out of range (pass test)
if (predict.IsOut())
{
goto START;
}
// retry if evaluation is less than 90% and predicted is invalid!
if ((double)passedCount < (deep * (double)9 / (double)10) ||
!predict.IsValid())
{
goto RETRY;
}
}
}
catch (Exception exception)
{
Console.WriteLine(exception.ToString());
}
return(0);
});
app.Execute(args);
}
