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(' ')[2].Split(',');
var res = new LottoResult(
int.Parse(values[0]),
int.Parse(values[1]),
int.Parse(values[2]),
int.Parse(values[3]),
int.Parse(values[4]),
int.Parse(values[5])
);
dbl.Add(res);
}
}
dbl.Reverse();
return(true);
}
static void Main(string[] args)
{
var wc = new WebClient();
var fileDB = Path.GetTempFileName();
try
{
wc.DownloadFile("http://www.mbnet.com.pl/dl.txt", fileDB);
LottoListResults dbl = null;
if (CreateDatabase(fileDB, out dbl))
{
var deep = 20;
var network = new BasicNetwork();
network.AddLayer(
new BasicLayer(null, true, 6 * deep));
network.AddLayer(
new BasicLayer(
new ActivationSigmoid(), true, 5 * 6 * deep));
network.AddLayer(
new BasicLayer(
new ActivationSigmoid(), true, 5 * 6 * deep));
network.AddLayer(
new BasicLayer(
new ActivationLinear(), true, 6));
network.Structure.FinalizeStructure();
var learningInput = new double[deep][];
for (int i = 0; i < deep; ++i)
{
learningInput[i] = new double[deep * 6];
for (int j = 0, k = 0; j < deep; ++j)
{
var idx = 2 * deep - i - j;
var 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;
learningInput[i][k++] = (double)data.V6;
}
}
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[6]
{
(double)data.V1,
(double)data.V2,
(double)data.V3,
(double)data.V4,
(double)data.V5,
(double)data.V6
};
}
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 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();
}
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();
if (predict.IsOut())
{
goto START;
}
if ((double)passedCount < (deep * (double)9 / (double)10) ||
!predict.IsValid())
{
goto RETRY;
}
Console.WriteLine("Press any key for close...");
Console.ReadKey(true);
}
}
catch (Exception exception)
{
Console.WriteLine(exception.ToString());
}
finally
{
File.Delete(fileDB);
}
}
