namespace PowerBallPredictor
    {
        using System;
        using System.Collections.Generic;
        using System.Net;
        using System.IO;
        using Encog.Neural.Networks;
        using Encog.ML.Data.Basic;
        using Encog.Neural.Networks.Layers;
        using Encog.Engine.Network.Activation;
        using Encog.Neural.Networks.Training.Propagation.Resilient;
        class Result
        {
            public int V1 { get; private set; }
            public int V2 { get; private set; }
            public int V3 { get; private set; }
            public int V4 { get; private set; }
            public int V5 { get; private set; }
            public int V6 { get; private set; }
            public Result(int v1, int v2, int v3, int v4, int v5, int v6)
            {
                V1 = v1;
                V2 = v2;
                V3 = v3;
                V4 = v4;
                V5 = v5;
                V6 = v6;
            }
            public Result(double[] values)
            {
                V1 = (int)Math.Round(values[0]);
                V2 = (int)Math.Round(values[1]);
                V3 = (int)Math.Round(values[2]);
                V4 = (int)Math.Round(values[3]);
                V5 = (int)Math.Round(values[4]);
                V6 = (int)Math.Round(values[5]);
            }
            public bool IsValid()
            {
                return
                V1 >= 1 && V1 <= 69 &&
                V2 >= 1 && V2 <= 69 &&
                V3 >= 1 && V3 <= 69 &&
                V4 >= 1 && V4 <= 69 &&
                V5 >= 1 && V5 <= 69 &&
                V6 >= 1 && V6 <= 69 &&
                V1 != V2 &&
                V1 != V3 &&
                V1 != V4 &&
                V1 != V5 &&
                V1 != V6 &&
                V2 != V3 &&
                V2 != V4 &&
                V2 != V5 &&
                V2 != V6 &&
                V3 != V4 &&
                V3 != V5 &&
                V3 != V6 &&
                V4 != V5 &&
                V4 != V6 &&
                V5 != V6;
            }
            public bool IsOut()
            {
                return
                !(
                V1 >= 1 && V1 <= 69 &&
                V2 >= 1 && V2 <= 69 &&
                V3 >= 1 && V3 <= 69 &&
                V4 >= 1 && V4 <= 69 &&
                V5 >= 1 && V5 <= 69 &&
                V6 >= 1 && V6 <= 69);
            }
            public override string ToString()
            {
                return string.Format(
                "{0},{1},{2},{3},{4},{5}",
                V1, V2, V3, V4, V5, V6);
            }
        }
        class ListResults : List<Result> { }
        class Program
        {
            static void Main(string[] args)
            {
                var fileDB = @"C:ProjectsPredictordbwinnums-text.txt";
                try
                {
                    ListResults dbl = null;
                    if (CreateDatabases(fileDB, out dbl))
                    {
                        var deep = 27;
                        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 Result(learningOutput[i]);
                            var inputn = new BasicMLData(6 * deep);
                            Array.Copy(
                            learningInput[i],
                            inputn.Data,
                            inputn.Data.Length);
                            var comput =
                            new Result(
                            ((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 Result(
                        ((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)3 / (double)10) ||
                            !predict.IsValid())
                            goto RETRY;
                        Console.WriteLine("Press any key for close...");
                        Console.ReadKey(true);
                    }
                }
                catch (Exception exception)
                {
                    Console.WriteLine(exception.ToString());
                }
            }
            static bool CreateDatabases(
            string fileDB,
            out ListResults dbl)
            {
                dbl = new ListResults();
                using (var reader = File.OpenText(fileDB))
                {
                    var line = string.Empty;
                    var separator = new string[] { "  " };
                    while ((line = reader.ReadLine()) != null)
                    {
                        if (line == "Draw Date   WB1 WB2 WB3 WB4 WB5 PB  PP") continue;