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'); var res = new LottoResult( int.Parse(values[2]), int.Parse(values[3]), int.Parse(values[4]), int.Parse(values[5]), int.Parse(values[6]), int.Parse(values[7]) ); dbl.Add(res); } } dbl.Reverse(); return(true); }
static void Main(string[] args) { try { LottoListResults dbl = null; string fileDB = ".\\Data.txt"; 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()); } }