基于随机神经元和逻辑函数的二进制神经网络的来临

using System;
using System.Collections.Generic;
using System.Linq;

namespace RndNeuroNet
{
  #region   
  /// <summary>
  ///       
  /// </summary>
  public class RandomNeuralNetwork
  {
    private DistributionFunc[] pDistributionDataFuncs;
    private RandomDiscrimBase pNeuroLogicDiscrim;

    /// <summary>
    /// 
    /// </summary>
    public RandomNeuralNetwork()
    {
    }

    /// <summary>
    ///    
    /// </summary>
    /// <param name="pSourceTermsData"></param>
    /// <param name="pTargetFeature"></param>
    public void CreateNetwork(double[][] pSourceData, bool[] pTargetFeature,
      int iFromPos, int iTargetPos, int iWindowSize,
      int iAmountLogicDiscrims, int iLogicDiscrimsMaxTrainRestarts = 1,
      int iMaxAmountDistributionPoints = 1000, bool bIsNorming = true)
    {
      //  
      double[][] pSrcArray = new double[iTargetPos - iFromPos][];
      bool[] pDstArray = new bool[iTargetPos - iFromPos];
      for (int i = 0, t = iFromPos; t < iTargetPos; t++, i++)
      {
        //  
        pSrcArray[i] = (bIsNorming ? NormingE(pSourceData[t], iWindowSize, iWindowSize) : pSourceData[t]);
        pDstArray[i] = pTargetFeature[t];
      }
      //    
      pDistributionDataFuncs = new DistributionFunc[iWindowSize];
      for (int i = 0; i < iWindowSize; i++)
      {
        //   
        double[] pTrend2Recalc = pSrcArray.Select(p => p != null ? p[i] : 0).ToArray();
        // 
        pDistributionDataFuncs[i] = new DistributionFunc(pTrend2Recalc, iMaxAmountDistributionPoints);
        //     
        pTrend2Recalc = pDistributionDataFuncs[i].RecalcTrend(pTrend2Recalc);
        //    
        for (int t = 0; t < pSrcArray.Length; t++) pSrcArray[t][i] = pTrend2Recalc[t];
      }
      // 
      pNeuroLogicDiscrim = (RandomDiscrimBase)new RandomLogicDiscrim();
      pNeuroLogicDiscrim.CreateDiscrim(pSrcArray, pDstArray, 0, pSrcArray.Length,
        iWindowSize, iAmountLogicDiscrims, iLogicDiscrimsMaxTrainRestarts);
    }

    public double CalcResponce(double[] pSourceData, int iWindowSize = 0, bool bIsNorming = true)
    {
      // 
      if (iWindowSize <= 0) iWindowSize = pSourceData.Length;
      //  
      pSourceData = (bIsNorming ? NormingE(pSourceData, iWindowSize, iWindowSize) : pSourceData);
      //  
      for (int i = 0; i < iWindowSize; i++)
      {
        pSourceData[i] = pDistributionDataFuncs[i].RecalcTrend(pSourceData[i]);
      }
      //  
      return pNeuroLogicDiscrim.GetPrognos(pSourceData, iWindowSize);
    }

    /// <summary>
    ///     
    /// </summary>
    /// <param name="pTrend"></param>
    /// <param name="iTargetPosition"></param>
    /// <param name="iVectorSize"></param>
    /// <returns></returns>
    public static double[] NormingE(double[] pTrend, int iTargetPosition,
      int iVectorSize, double[] pResultVector = null, bool bIsMinusOffset = false)
    {
      if (pResultVector == null) pResultVector = new double[iVectorSize];
      double dNorming = 0;
      for (int i = 0, t = iTargetPosition - iVectorSize; i < iVectorSize; i++, t++)
      {
        dNorming += pTrend[t];
      }
      dNorming /= iVectorSize;
      double dOffset = (bIsMinusOffset ? 1 : 0);
      for (int i = 0, t = iTargetPosition - iVectorSize; i < iVectorSize; i++, t++)
      {
        pResultVector[i] = (pTrend[t] / dNorming) - dOffset;
      }
      return pResultVector;
    }
  }

  /// <summary>
  ///     
  /// </summary>
  public class DistributionFunc
  {
    private class DataCont
    {
      public int Counter;
      public double SumSrc;
      public double ValueP;
    }

    private readonly SortedDictionary<int, DataCont> pDistribution =
      new SortedDictionary<int, DataCont>();
    private int m_iMaxAmountDistributionPoints;
    private double dAreaMin, dAreaMax;

    /// <summary>
    /// 
    /// </summary>
    public DistributionFunc()
    {
    }

    /// <summary>
    /// 
    /// </summary>
    public DistributionFunc(double[] pTrend, int iMaxAmountDistributionPoints = 1000)
    {
      CreateDistribution(pTrend, iMaxAmountDistributionPoints);
    }

    //  
    public void CreateDistribution(double[] pTrend, int iMaxAmountDistributionPoints = 1000)
    {
      m_iMaxAmountDistributionPoints = iMaxAmountDistributionPoints;
      dAreaMin = double.MaxValue; dAreaMax = 0;
      //      
      for (int t = 0; t < pTrend.Length; t++)
      {
        double dTrendVal = pTrend[t];
        if (dTrendVal == 0) continue;
        dAreaMin = Math.Min(dAreaMin, dTrendVal);
        dAreaMax = Math.Max(dAreaMax, dTrendVal);
      }
      //  
      for (int t = 0; t < pTrend.Length; t++)
      {
        double dTrendVal = pTrend[t];
        if (dTrendVal == 0) continue;
        int iIndex = (int)(((dTrendVal - dAreaMin) / (dAreaMax - dAreaMin)) * m_iMaxAmountDistributionPoints);
        DataCont pKeyVal = null;
        pDistribution.TryGetValue(iIndex, out pKeyVal);
        if (pKeyVal == null) pDistribution.Add(iIndex, pKeyVal = new DataCont());
        pKeyVal.Counter++;
        pKeyVal.SumSrc += dTrendVal;
      }
      //   
      double dSumP = 0;
      foreach (KeyValuePair<int, DataCont> dataValue in pDistribution)
      {
        dataValue.Value.SumSrc /= dataValue.Value.Counter;
        dSumP += (double)dataValue.Value.Counter / (double)pTrend.Length;
        dataValue.Value.ValueP = dSumP;
      }
    }

    //    ,   
    public double[] RecalcTrend(double[] pTrend, double[] pNewTrend = null)
    {
      if (pNewTrend == null) pNewTrend = new double[pTrend.Length];
      for (int t = 0; t < pTrend.Length; t++)
      {
        pNewTrend[t] = RecalcTrend(pTrend[t]);
      }
      return pNewTrend;
    }

    //    ,   
    public double RecalcTrend(double dTrendVal)
    {
      int iIndex = (int)(((dTrendVal - dAreaMin) / (dAreaMax - dAreaMin)) * m_iMaxAmountDistributionPoints);
      if (iIndex < 0) iIndex = pDistribution.Keys.Min();
      if (iIndex > pDistribution.Keys.Max()) iIndex = pDistribution.Keys.Max();
      if (pDistribution.Keys.Contains(iIndex))
      {
        dTrendVal = pDistribution[iIndex].ValueP;
      }
      else
      {
        int iDnIndex = pDistribution.Keys.Max<int>(p => p < iIndex ? p : 0);
        double dDnVal = pDistribution[iDnIndex].ValueP;
        int iUpIndex = pDistribution.Keys.Min<int>(p => p > iIndex ? p : int.MaxValue);
        double dUpVal = pDistribution[iUpIndex].ValueP;
        dTrendVal = (dUpVal - dDnVal) * ((double)(iIndex - iDnIndex) / (double)(iUpIndex - iDnIndex)) + dDnVal;
      }
      return dTrendVal;
    }
  }

  /// <summary>
  ///     
  /// </summary>
  public class RandomDiscrimBase
  {
    public static int GetTermPoint(double[] pNeuronWeights,
      double[] pData, int iDataCortegeLength = 0)
    {
      double dVal = pNeuronWeights[iDataCortegeLength]; //  
      for (int i = 0; i < iDataCortegeLength; i++) dVal += (pNeuronWeights[i] * pData[i]);
      return Math.Sign(dVal);
    }

    public static int[] GetTermPoint(IList<double[]> pNeuroSurfaces,
      double[] pData, int iDataCortegeLength = 0)
    {
      if (iDataCortegeLength <= 0) iDataCortegeLength = pData.Length;
      int[] pLogicData = new int[pNeuroSurfaces.Count];
      for (int n = 0; n < pNeuroSurfaces.Count; n++)
      {
        pLogicData[n] = GetTermPoint(pNeuroSurfaces[n], pData, iDataCortegeLength);
      }
      return pLogicData;
    }

    /// <summary>
    ///   
    /// </summary>
    public virtual void CreateDiscrim(double[][] pSrcData, bool[] pDstData,
      int iFromPos, int iToPos, int iDataCortegeLength, int iAmountLogicDiscrims,
      int iLogicDiscrimsMaxTrainRestarts)
    {
      throw new MissingMethodException("  ");
    }

    /// <summary>
    ///  
    ///           -1  +1
    /// </summary>
    public virtual double GetPrognos(double[] pTermPointData, int iDataCortegeLength = -1)
    {
      throw new MissingMethodException("  ");
    }
  }

  /// <summary>
  ///        
  /// </summary>
  public class RandomLogicDiscrim : RandomDiscrimBase
  {
    private IList<double[]> pLogicDiscrims = null;
    public LogicFunc.Quine_McCluskey neuronLogic = null;

    /// <summary>
    /// 
    /// </summary>
    public RandomLogicDiscrim()
    {
    }

    /// <summary>
    ///   
    /// </summary>
    public override void CreateDiscrim(double[][] pSrcArray, bool[] pTargetData,
      int iFromPos, int iToPos, int iDataCortegeLength, int iAmountLogicDiscrims,
      int iLogicDiscrimsMaxTrainRestarts)
    {
      Random RndFabric = new Random(Environment.TickCount);
      //  
      int iMaxCounter = 0;
      //   
      neuronLogic = null;
      pLogicDiscrims = new List<double[]>();
      do
      {
        //    
        // -   ,  iAmountLogicDiscrims,   
        while (pLogicDiscrims.Count < iAmountLogicDiscrims)
        {
          double[] pNewSurface = new double[iDataCortegeLength + 1];
          for (int i = 0; i < iDataCortegeLength; i++)
          {
            //    
            double dNormal = RndFabric.NextDouble() - 0.5;
            //   ,    (0...1)
            double dPoint = RndFabric.NextDouble();
            //  
            pNewSurface[i] = dNormal;
            //  
            pNewSurface[iDataCortegeLength] -= dNormal * dPoint;
          }
          pLogicDiscrims.Add(pNewSurface);
        }
        //  
        ICollection<byte[]> TermInputUp = new LinkedList<byte[]>();
        ICollection<byte[]> TermInputDn = new LinkedList<byte[]>();
        for (int t = iFromPos; t < iToPos; t++)
        {
          byte[] pSrcData = GetTermPoint(pLogicDiscrims, pSrcArray[t], iDataCortegeLength)
            .Select(p => (byte)(p > 0 ? 1 : 0)).ToArray();
          if (pTargetData[t])
            TermInputUp.Add(pSrcData);
          else
            TermInputDn.Add(pSrcData);
        }
        //  
        neuronLogic = new LogicFunc.Quine_McCluskey();
        neuronLogic.Start(TermInputUp, TermInputDn);
        //   , . .
        //   ,     
        if ((iMaxCounter + 1) < iLogicDiscrimsMaxTrainRestarts)
        {
          Dictionary<int, int> TermStars = new Dictionary<int, int>(iAmountLogicDiscrims);
          for (int i = 0; i < iAmountLogicDiscrims; i++) TermStars.Add(i, 0);
          foreach (byte[] pTerm in neuronLogic.Result.Terms)
          {
            for (int i = 0; i < iAmountLogicDiscrims; i++)
            {
              if (pTerm[i] != LogicFunc.LogicFunction.cStarSymb) TermStars[i]++;
            }
          }
          foreach (byte[] pTerm in neuronLogic.ResultNeg.Terms)
          {
            for (int i = 0; i < iAmountLogicDiscrims; i++)
            {
              if (pTerm[i] != LogicFunc.LogicFunction.cStarSymb) TermStars[i]++;
            }
          }
          foreach (KeyValuePair<int, int> p in TermStars)
          {
            //    -   
            if (p.Value <= 0) pLogicDiscrims[p.Key] = null;
          }
          pLogicDiscrims = pLogicDiscrims.Where(p => p != null).ToList();
        }
      } while ((pLogicDiscrims.Count < iAmountLogicDiscrims) && (iMaxCounter++ < iLogicDiscrimsMaxTrainRestarts));
    }

    /// <summary>
    ///  
    /// </summary>
    /// <param name="pTermPointData"></param>
    /// <returns></returns>
    public override double GetPrognos(double[] pTermPointData, int iDataCortegeLength = -1)
    {
      if (iDataCortegeLength <= 0) iDataCortegeLength = pTermPointData.Length;
      byte[] pSrcData = GetTermPoint(pLogicDiscrims, pTermPointData, iDataCortegeLength).Select(p => (byte)(p > 0 ? 1 : 0)).ToArray();
      int iPrognos = 0;
      iPrognos += (neuronLogic.Result.Calculate(pSrcData)    ? +1 : -1);
      iPrognos += (neuronLogic.ResultNeg.Calculate(pSrcData) ? -1 : +1);
      return (iPrognos / 2);
    }
  }
  #endregion
}

namespace LogicFunc
{
  #region  
  /// <summary>
  ///     
  /// </summary>
  public abstract class LogicFunction
  {
    // "" 
    public const byte cStarSymb = 2;

    //   
    public readonly ICollection<byte[]> Terms = new LinkedList<byte[]>();
    //  
    public abstract bool Calculate(bool[] X);
    //  
    public abstract bool Calculate(char[] X);
    //  
    public abstract bool Calculate(byte[] X);
  }

  /// <summary>
  ///   
  /// </summary>
  public class Dnf : LogicFunction
  {
    public static bool Calculate(byte[] X, byte[] term)
    {
      bool bResult = true;
      for (int i = 0; i < term.Length; i++)
      {
        if ((term[i] == cStarSymb) || (term[i] == X[i])) continue;
        bResult = false;
        break;
      }
      return bResult;
    }

    public override bool Calculate(byte[] X)
    {
      bool bResult = false;
      foreach (byte[] term in Terms)
      {
        bool bTermVal = true;
        for (int i = 0; i < term.Length; i++)
        {
          if ((term[i] >= cStarSymb) || (term[i] == X[i])) continue;
          bTermVal = false;
          break;
        }
        //bResult |= bTermVal;
        if (bTermVal)
        {
          bResult = true;
          break;
        }
      }
      return bResult;
    }

    public override bool Calculate(char[] X)
    {
      bool bResult = false;
      foreach (byte[] term in Terms)
      {
        bool bTermVal = true;
        for (int i = 0; i < term.Length; i++)
        {
          if ((term[i] >= cStarSymb) || (term[i] == (byte)(X[i] == '0' ? 0 : 1))) continue;
          bTermVal = false;
          break;
        }
        //bResult |= bTermVal;
        if (bTermVal)
        {
          bResult = true;
          break;
        }
      }
      return bResult;
    }

    public override bool Calculate(bool[] X)
    {
      bool bResult = false;
      foreach (byte[] term in Terms)
      {
        bool bTermVal = true;
        for (int i = 0; i < term.Length; i++)
        {
          if ((term[i] >= cStarSymb) || ((term[i] != 0) == X[i])) continue;
          bTermVal = false;
          break;
        }
        //bResult |= bTermVal;
        if (bTermVal)
        {
          bResult = true;
          break;
        }
      }
      return bResult;
    }
  }

  /// <summary>
  ///  
  /// </summary>
  public class TreeFuncTerm
  {
    //    
    public class TreeNodeEnd { }
    //   
    private readonly TreeNodeEnd pCommonTreeNodeEnd = new TreeNodeEnd();

    //
    private readonly object[] rootNode = new object[3];
    //      
    private readonly bool IsNewTreeNodeEndMode = false;
    // () 
    private int _rang = 0;
    public int Rang
    {
      get { return _rang; }
    }
    //   
    private int enumerationPos = 0;
    private object[][] enumerationBuf;
    //,     
    private byte[] enumerationTerm;
    public byte[] EnumerationTerm
    {
      get { return enumerationTerm; }
    }
    // ,    
    private TreeNodeEnd enumerationNode;
    public TreeNodeEnd EnumerationNode
    {
      get { return enumerationNode; }
    }
    //    
    private UInt32 _count = 0;
    public UInt32 Count
    {
      get { return _count; }
    }

    //
    public TreeFuncTerm(bool bNewTreeNodeEndMode = false)
    {
      IsNewTreeNodeEndMode = bNewTreeNodeEndMode;
      Clear();
    }

    // 
    public void Clear()
    {
      _count = 0;
      _rang = 0;
      enumerationPos = 0;
      enumerationBuf = null;
      enumerationTerm = null;
      enumerationNode = null;
      rootNode[0] = rootNode[1] = rootNode[2] = null;
    }

    //     
    public TreeNodeEnd EnumerationInit()
    {
      enumerationPos = 0;
      enumerationTerm = new byte[_rang];
      enumerationTerm[0] = 0;
      enumerationNode = null;
      enumerationBuf = new object[_rang][];
      enumerationBuf[0] = rootNode;
      //   
      return EnumerationNextNode();
    }

    //    
    public TreeNodeEnd EnumerationNextNode()
    {
      int iIsNext = (enumerationNode != null ? 1 : 0);
      enumerationNode = null;
      while ((enumerationNode == null) && (enumerationPos >= 0))
      {
        object pNextNode = null;
        int iSymb = enumerationTerm[enumerationPos] + iIsNext;
        for (object[] pNodes = enumerationBuf[enumerationPos]; iSymb < 3; iSymb++)
        {
          if ((pNextNode = pNodes[iSymb]) != null) break;
        }
        if (pNextNode == null)
        {
          //   
          enumerationPos--;
          iIsNext = 1;
        }
        else
        {
          enumerationTerm[enumerationPos] = (byte)iSymb;
          if (pNextNode is TreeNodeEnd)
          {
            //  
            enumerationNode = (TreeNodeEnd)pNextNode;
          }
          else
          {
            //   
            enumerationPos++;
            enumerationBuf[enumerationPos] = (object[])pNextNode;
            enumerationTerm[enumerationPos] = 0;
            iIsNext = 0;
          }
        }
      }
      return enumerationNode;
    }

    //       
    public TreeNodeEnd Add(byte[] term)
    {
      _rang = Math.Max(_rang, term.Length);
      object[] pCurrNode = rootNode;
      int iTermLength1 = term.Length - 1;
      for (int i = 0; i < iTermLength1; i++)
      {
        byte cSymb = term[i];
        object pNextNode = pCurrNode[cSymb];
        if (pNextNode == null)
        {
          pNextNode = new object[3];
          pCurrNode[cSymb] = pNextNode;
        }
        pCurrNode = (object[])pNextNode;
      }
      object pNewNode = pCurrNode[term[iTermLength1]];
      if (pNewNode == null)
      {
        pNewNode = (IsNewTreeNodeEndMode ? new TreeNodeEnd() : pCommonTreeNodeEnd);
        pCurrNode[term[iTermLength1]] = pNewNode;
        _count++;
      }
      return (TreeNodeEnd)pNewNode;
    }

    //     
    public TreeNodeEnd Remove(byte[] term)
    {
      object[] pCurrNode = rootNode;
      int iTermLength1 = term.Length - 1;
      for (int i = 0; i < iTermLength1; i++)
      {
        pCurrNode = (object[])pCurrNode[term[i]];
        if (pCurrNode == null) break;
      }
      TreeNodeEnd pRemovedNode = null;
      if (pCurrNode != null)
      {
        //     
        pRemovedNode = (TreeNodeEnd)pCurrNode[term[iTermLength1]];
        if (pRemovedNode != null)
        {
          //    
          pCurrNode[term[iTermLength1]] = null;
          // - 
          _count--;
        }
      }
      return pRemovedNode;
    }

    //     
    public void Remove(IEnumerable<byte[]> RemovedTerms)
    {
      if ((RemovedTerms == null) || (RemovedTerms.Count() == 0)) return;
      foreach (byte[] x1 in RemovedTerms)
      {
        //-       Remove
        //   IsContains
        Remove(x1);
      }
    }

    //    
    public bool Contains(byte[] term)
    {
      object pCurrNode = rootNode;
      for (int i = 0; i < term.Length; i++)
      {
        pCurrNode = ((object[])pCurrNode)[term[i]];
        if (pCurrNode == null) break;
      }
      return ((pCurrNode != null) && (pCurrNode is TreeNodeEnd));
    }

    //    ,
    //     
    public bool IsCalculateTrue(byte[] term)
    {
      return IsCalculateTrue(rootNode, term, 0);
    }

    //   
    private static bool IsCalculateTrue(object[] pCurrNode,
      byte[] term, int iStartPos)
    {
      int iTermLength1 = term.Length - 1;
      while ((pCurrNode != null) && (iStartPos < iTermLength1))
      {
        byte cSymb = term[iStartPos++];
        if (cSymb != LogicFunction.cStarSymb)
        {
          pCurrNode = (object[])pCurrNode[cSymb];
        }
        else
        {
          if ((pCurrNode[0] != null) && (pCurrNode[1] != null))
          {
            if (IsCalculateTrue((object[])pCurrNode[1], term, iStartPos)) return true;
            pCurrNode = (object[])pCurrNode[0];
          }
          else
          {
            pCurrNode = (object[])(pCurrNode[0] != null ? pCurrNode[0] : pCurrNode[1]);
          }
        }
      }
      TreeNodeEnd pEndNode = null;
      if (pCurrNode != null)
      {
        byte cSymb = term[iTermLength1];
        if (cSymb != LogicFunction.cStarSymb)
        {
          pEndNode = (TreeNodeEnd)pCurrNode[cSymb];
        }
        else
        {
          pEndNode = (TreeNodeEnd)(pCurrNode[0] != null ? pCurrNode[0] : pCurrNode[1]);
        }
      }
      return (pEndNode != null);
    }

    //    ,
    //     
    public void GetAllCalculateTrueTerms(byte[] term,
      ICollection<TreeNodeEnd> pAllCalculateTrueTermsList)
    {
      pAllCalculateTrueTermsList.Clear();
      GetAllCalculateTrueTerms(rootNode, term, 0, pAllCalculateTrueTermsList);
    }

    //   
    private static void GetAllCalculateTrueTerms(object[] pCurrNode,
      byte[] term, int iStartPos, ICollection<TreeNodeEnd> pAllCalculateTrueTermsList)
    {
      int iTermLength1 = term.Length - 1;
      while ((pCurrNode != null) && (iStartPos < iTermLength1))
      {
        byte cSymb = term[iStartPos++];
        if (cSymb != LogicFunction.cStarSymb)
        {
          pCurrNode = (object[])pCurrNode[cSymb];
        }
        else
        {
          if ((pCurrNode[0] != null) && (pCurrNode[1] != null))
          {
            GetAllCalculateTrueTerms((object[])pCurrNode[1], term, iStartPos,
              pAllCalculateTrueTermsList);
            pCurrNode = (object[])pCurrNode[0];
          }
          else
          {
            pCurrNode = (object[])(pCurrNode[0] != null ? pCurrNode[0] : pCurrNode[1]);
          }
        }
      }
      if (pCurrNode != null)
      {
        byte cSymb = term[iTermLength1];
        if (cSymb != LogicFunction.cStarSymb)
        {
          TreeNodeEnd pEndNode = (TreeNodeEnd)pCurrNode[cSymb];
          if (pEndNode != null) pAllCalculateTrueTermsList.Add(pEndNode);
        }
        else
        {
          if (pCurrNode[0] != null) pAllCalculateTrueTermsList.Add((TreeNodeEnd)pCurrNode[0]);
          if (pCurrNode[1] != null) pAllCalculateTrueTermsList.Add((TreeNodeEnd)pCurrNode[1]);
        }
      }
    }
  }

  /// <summary>
  ///     ----
  /// </summary>
  public class Quine_McCluskey
  {
    //   
    private readonly Dnf _result = new Dnf();
    public Dnf Result
    {
      get { return _result; }
    }
    //   
    private readonly Dnf _resultNeg = new Dnf();
    public Dnf ResultNeg
    {
      get { return _resultNeg; }
    }

    //    
    private static void Skleivanie(TreeFuncTerm X1Tree,
      TreeFuncTerm X2Tree, TreeFuncTerm NegativTree,
      TreeFuncTerm InpNegTerms, TreeFuncTerm AllOutTerms)
    {
      bool IsVirtSkleivOn = ((NegativTree != null) &&
        (InpNegTerms != null) && (InpNegTerms.Count != 0));
      for (TreeFuncTerm.TreeNodeEnd x1 = X1Tree.EnumerationInit();
        x1 != null; x1 = X1Tree.EnumerationNextNode())
      {
        bool bIsSkleiv = false;
        byte[] pCurrTerm = X1Tree.EnumerationTerm;
        for (int iPos = 0; iPos < pCurrTerm.Length; iPos++)
        {
          byte cSymbSav = pCurrTerm[iPos];
          if (cSymbSav == LogicFunction.cStarSymb) continue;
          //     
          pCurrTerm[iPos] = (byte)(1 - cSymbSav);
          if (X1Tree.Contains(pCurrTerm))
          {
            bIsSkleiv = true;
            if (cSymbSav == 0)
            {
              pCurrTerm[iPos] = LogicFunction.cStarSymb; // 
              X2Tree.Add(pCurrTerm);
            }
          }
          //    ,    NegativTree
          else if (IsVirtSkleivOn && !NegativTree.Contains(pCurrTerm))
          {
            pCurrTerm[iPos] = LogicFunction.cStarSymb; // 
            if (!InpNegTerms.IsCalculateTrue(pCurrTerm))
            {
              bIsSkleiv = true;
              X2Tree.Add(pCurrTerm);
            }
          }
          pCurrTerm[iPos] = cSymbSav;
        }
        //    ,      
        if (!bIsSkleiv && (AllOutTerms != null)) AllOutTerms.Add(pCurrTerm);
      }
    }

    //     
    //      
    private static void DeleteDublicatingTerms(
      IEnumerable<byte[]> InX1, TreeFuncTerm OutX2Tree)
    {
      OutX2Tree.Clear();
      foreach (byte[] x1 in InX1) OutX2Tree.Add(x1);
    }

    //           .
    // ,   ,       (),
    //    “ – ”
    // (http://www.studfiles.ru/preview/5175815/page:4/)
    private static void ReduceRedundancyTerms(TreeFuncTerm AllOutputTerms,
      TreeFuncTerm AllInputTerms, ICollection<byte[]> ResultTerms)
    {
      //  
      ResultTerms.Clear();
      //        ,   
      Dictionary<byte[], HashSet<TreeFuncTerm.TreeNodeEnd>> Outputs2Inputs =
        new Dictionary<byte[], HashSet<TreeFuncTerm.TreeNodeEnd>>();
      //        ,   
      Dictionary<TreeFuncTerm.TreeNodeEnd, HashSet<byte[]>> Inputs2Outputs =
        new Dictionary<TreeFuncTerm.TreeNodeEnd, HashSet<byte[]>>();
      //      
      for (TreeFuncTerm.TreeNodeEnd pNode = AllOutputTerms.EnumerationInit();
        pNode != null; pNode = AllOutputTerms.EnumerationNextNode())
      {
        byte[] outTerm = (byte[])AllOutputTerms.EnumerationTerm.Clone();
        //  ,      term
        HashSet<TreeFuncTerm.TreeNodeEnd> InpTermsLst = new HashSet<TreeFuncTerm.TreeNodeEnd>();
        AllInputTerms.GetAllCalculateTrueTerms(outTerm, InpTermsLst);
        Outputs2Inputs.Add(outTerm, InpTermsLst);
        foreach (TreeFuncTerm.TreeNodeEnd inputTerm in InpTermsLst)
        {
          if (!Inputs2Outputs.ContainsKey(inputTerm)) Inputs2Outputs.Add(inputTerm, new HashSet<byte[]>());
          Inputs2Outputs[inputTerm].Add(outTerm);
        }
      }
      //      -   
      Inputs2Outputs = Inputs2Outputs.OrderBy(p => p.Value.Count).ToDictionary(p => p.Key, v => v.Value);
      //   ,   -   
      while (Inputs2Outputs.Count > 0)
      {
        byte[] outTerm = Inputs2Outputs.First().Value.OrderByDescending(q => Outputs2Inputs[q].Count()).First();
        ResultTerms.Add(outTerm);
        foreach (TreeFuncTerm.TreeNodeEnd inTerm in Outputs2Inputs[outTerm].ToArray())
        {
          foreach (byte[] outTerm2Del in Inputs2Outputs[inTerm]) Outputs2Inputs[outTerm2Del].Remove(inTerm);
          Inputs2Outputs.Remove(inTerm);
        }
      }
    }

    //   
    public static void LogicFuncMinimize(
      IEnumerable<byte[]> PositivTerms, ICollection<byte[]> OutPos,
      IEnumerable<byte[]> NegativTerms, ICollection<byte[]> OutNeg)
    {
      TreeFuncTerm InpPosTerms = new TreeFuncTerm(true);
      DeleteDublicatingTerms(PositivTerms, InpPosTerms);

      int iTotalLevels = InpPosTerms.Rang;
      if (iTotalLevels <= 0) return;

      TreeFuncTerm OutPosTerms = new TreeFuncTerm();
      TreeFuncTerm OutNegTerms = null;

      TreeFuncTerm InpNegTerms = null;
      if ((NegativTerms != null) && (NegativTerms.Count() != 0))
      {
        InpNegTerms = new TreeFuncTerm(true);
        DeleteDublicatingTerms(NegativTerms, InpNegTerms);
        OutNegTerms = new TreeFuncTerm();

        //        
        for (TreeFuncTerm.TreeNodeEnd pNode = InpPosTerms.EnumerationInit();
          pNode != null; pNode = InpPosTerms.EnumerationNextNode())
        {
          if (!InpNegTerms.Contains(InpPosTerms.EnumerationTerm)) continue;
          // -    X1   NegativTerms
          int iPos_Count = PositivTerms.Count(p => Enumerable.SequenceEqual(p, InpPosTerms.EnumerationTerm));
          int iNeg_Count = NegativTerms.Count(p => Enumerable.SequenceEqual(p, InpPosTerms.EnumerationTerm));
          if (iPos_Count > iNeg_Count)
          {
            InpNegTerms.Remove(InpPosTerms.EnumerationTerm);
          }
          else if (iPos_Count < iNeg_Count)
          {
            InpPosTerms.Remove(InpPosTerms.EnumerationTerm);
          }
          else //if (iX1_Count == iNeg_Count)
          {
            InpPosTerms.Remove(InpPosTerms.EnumerationTerm);
            InpNegTerms.Remove(InpPosTerms.EnumerationTerm);
          }
        }
      }

      // 
      TreeFuncTerm X1PositivTree = InpPosTerms;
      TreeFuncTerm X1NegativTree = InpNegTerms;

      int iLevelCounter = 0;
      //       
      while ((X1PositivTree.Count != 0) && (iLevelCounter < iTotalLevels))
      {
        TreeFuncTerm X2PositivTree = new TreeFuncTerm();
        Skleivanie(X1PositivTree, X2PositivTree, X1NegativTree, InpNegTerms, OutPosTerms);

        if ((X1NegativTree != null) && (X1NegativTree.Count != 0))
        {
          TreeFuncTerm X2NegativTree = new TreeFuncTerm();
          Skleivanie(X1NegativTree, X2NegativTree, X1PositivTree, InpPosTerms, OutNegTerms);

          //  
          if (iLevelCounter > 0) X1NegativTree.Clear();

          X1NegativTree = X2NegativTree;
        }

        //  
        if (iLevelCounter > 0) X1PositivTree.Clear();

        X1PositivTree = X2PositivTree;

        iLevelCounter++;

        GC.Collect();
      }

      if (OutPosTerms.Count > 0)
      {
        // ,     cStarSymb
        OutPosTerms.Remove(Enumerable.Repeat(LogicFunction.cStarSymb, iTotalLevels).ToArray());
      }
      //   
      ReduceRedundancyTerms(OutPosTerms, InpPosTerms, OutPos);

      if ((OutNeg != null) && (OutNegTerms != null))
      {
        if (OutNegTerms.Count > 0)
        {
          // ,     cStarSymb
          OutNegTerms.Remove(Enumerable.Repeat(LogicFunction.cStarSymb, iTotalLevels).ToArray());
        }
        //   
        ReduceRedundancyTerms(OutNegTerms, InpNegTerms, OutNeg);
      }
    }

    // 
    public void Start(IEnumerable<byte[]> TermsInput)
    {
      LogicFuncMinimize(TermsInput, _result.Terms, null, null);
    }

    // 
    public void Start(IEnumerable<byte[]> TermsInput, IEnumerable<byte[]> NegativTerms)
    {
      LogicFuncMinimize(TermsInput, _result.Terms, NegativTerms, _resultNeg.Terms);
    }

    // 
    public void Start(IEnumerable<char[]> TermsInput)
    {
      Start(TermsInput.Select(t => t.Select(p => (byte)(p == '0' ? 0 : 1)).ToArray()));
    }

    // 
    public void Start(IEnumerable<char[]> TermsInput, IEnumerable<char[]> NegativTerms)
    {
      Start(TermsInput.Select(t => t.Select(p => (byte)(p == '0' ? 0 : 1)).ToArray()),
          NegativTerms.Select(t => t.Select(p => (byte)(p == '0' ? 0 : 1)).ToArray()));
    }

    // 
    public void Start(IEnumerable<bool[]> TermsInput)
    {
      Start(TermsInput.Select(t => t.Select(p => (byte)(p ? 1 : 0)).ToArray()));
    }

    // 
    public void Start(IEnumerable<bool[]> TermsInput, IEnumerable<bool[]> NegativTerms)
    {
      Start(TermsInput.Select(t => t.Select(p => (byte)(p ? 1 : 0)).ToArray()),
          NegativTerms.Select(t => t.Select(p => (byte)(p ? 1 : 0)).ToArray()));
    }
  }
  #endregion
}

    public static void TestRandomNeuralNetwork()
    {
      //:   
      const int iTrendSize = 2000;
      //:   
      const int iTestTrendSize = 1000;
      //:   
      const int iWindowSize = 10;
      //   
      const int iOmega = 100;
      //-   
      const int iHarmonics = 5;
      //    
      const int iMinAmpl = 50;
      //    
      const int iMaxAmpl = 100;
      //  /
      const double dNoise2SignalRatio = 0;
      //:   
      const int iAmountLogicDiscrims = 20;
      //:  -    
      const int iMaxAmountDistributionPoints = 1000;
      //:  -    
      const int iLogicDiscrimsMaxTrainRestarts = 1;

      //   
      int iPrepTrendLength = iTrendSize - iTestTrendSize;
      double[] pTrend = new double[iTrendSize];
      // -
      //for (int t = 0; t < iTrendSize; t++) pTrend[t] = Math.Sin((t * Math.PI) / iOmega) + 200;
      //  
      int[] pFreq = new int[iHarmonics];
      int[] pPhase = new int[iHarmonics];
      double[] pAmpl = new double[iHarmonics];
      //  . 
      Random RndFabric = new Random(Environment.TickCount);
      for (int h = 0; h < iHarmonics; h++)
      {
        pFreq[h] = RndFabric.Next(iOmega/*iPrepTrendLength*/) + 1;
        pPhase[h] = RndFabric.Next(iPrepTrendLength);
        pAmpl[h] = RndFabric.NextDouble();
      }
      double iMinValue = double.MaxValue, iMaxValue = 0;
      for (int t = 0; t < iTrendSize; t++)
      {
        double dValue = 0; //iMinAmpl + ((iMaxAmpl - iMinAmpl) * RndFabric.NextDouble());
        for (int h = 0; h < iHarmonics; h++)
        {
          dValue += ((pAmpl[h] * (iMaxAmpl - iMinAmpl)) + iMinAmpl) * Math.Sin(((t + pPhase[h]) * Math.PI) / pFreq[h]);
        }
        pTrend[t] = dValue;
        iMinValue = Math.Min(iMinValue, dValue);
        iMaxValue = Math.Max(iMaxValue, dValue);
      }
      // 
      if (dNoise2SignalRatio > 0)
      {
        double dNoiseAmp = (iMaxValue - iMinValue) * dNoise2SignalRatio;
        for (int t = 0; t < iTrendSize; t++)
        {
          pTrend[t] += dNoiseAmp * 2.0 * (RndFabric.NextDouble() - 0.5);
          iMinValue = Math.Min(iMinValue, pTrend[t]);
        }
      }
      // ,     
      if (iMinValue < 0)
      {
        for (int t = 0; t < iTrendSize; t++) pTrend[t] -= iMinValue;
      }
      //  
      double[][] pSourceTrendData = new double[iTrendSize][];
      bool[] pTargetFeature = new bool[iTrendSize];
      // 
      for (int t = iWindowSize; t < iTrendSize; t++)
      {
        // 
        double[] pNormData = new double[iWindowSize];
        for (int i = 0; i < iWindowSize; i++) pNormData[i] = pTrend[t - iWindowSize + i];
        pSourceTrendData[t] = pNormData;
        //  
        pTargetFeature[t] = (pTrend[t] >= pTrend[t - 1]);
      }
      DateTime dtStartDt = DateTime.Now;
      RndNeuroNet.RandomNeuralNetwork randomNeuralNetwork = new RndNeuroNet.RandomNeuralNetwork();
      randomNeuralNetwork.CreateNetwork(pSourceTrendData, pTargetFeature, iWindowSize, iPrepTrendLength, iWindowSize, iAmountLogicDiscrims,
        //iClustersAmount, iClustersRestart,
        iLogicDiscrimsMaxTrainRestarts, iMaxAmountDistributionPoints);
      //  
      TimeSpan tsTime = (DateTime.Now - dtStartDt);
      //     
      int iUpGapCounterTrain = 0, iDnGapCounterTrain = 0, iSuccessCounterTrain = 0, iErrorCounterTrain = 0, iIgnoreCounterTrain = 0;
      for (int t = iWindowSize; t < iPrepTrendLength; t++)
      {
        double dPrognos = randomNeuralNetwork.CalcResponce(pSourceTrendData[t]);
        if (dPrognos == 0)
        {
          iIgnoreCounterTrain++;
        }
        else
        {
          if (((pTargetFeature[t] ? +1 : -1) * dPrognos) > 0)
          {
            iSuccessCounterTrain++;
          }
          else
          {
            iErrorCounterTrain++;
          }
        }
        // GAP-   
        if (pTargetFeature[t])
        {
          iUpGapCounterTrain++;
        }
        else
        {
          iDnGapCounterTrain++;
        }
      }
      //     
      int iUpGapCounterTest = 0, iDnGapCounterTest = 0, iSuccessCounterTest = 0, iErrorCounterTest = 0, iIgnoreCounterTest = 0;
      for (int t = iPrepTrendLength; t < iTrendSize; t++)
      {
        double dPrognos = randomNeuralNetwork.CalcResponce(pSourceTrendData[t]);
        if (dPrognos == 0)
        {
          iIgnoreCounterTest++;
        }
        else
        {
          if (((pTargetFeature[t] ? +1 : -1) * dPrognos) > 0)
          {
            iSuccessCounterTest++;
          }
          else
          {
            iErrorCounterTest++;
          }
        }
        // GAP-   
        if (pTargetFeature[t])
        {
          iUpGapCounterTest++;
        }
        else
        {
          iDnGapCounterTest++;
        }
      }
      //
      Console.WriteLine("-      = " + iUpGapCounterTrain + Environment.NewLine +
                      "-      = " + iDnGapCounterTrain + Environment.NewLine +
                      "-     = " + iSuccessCounterTrain + Environment.NewLine +
                      "-     = " + iErrorCounterTrain + Environment.NewLine +
                      "-     = " + iIgnoreCounterTrain + Environment.NewLine +
                      "-      = " + iUpGapCounterTest + Environment.NewLine +
                      "-      = " + iDnGapCounterTest + Environment.NewLine +
                      "-     = " + iSuccessCounterTest + Environment.NewLine +
                      "-     = " + iErrorCounterTest + Environment.NewLine +
                      "-     = " + iIgnoreCounterTest + Environment.NewLine +
                      "  = " + (int)tsTime.TotalMinutes + " . " + tsTime.Seconds + " ."
      );
      Console.WriteLine("Press Enter to exit ...");
      Console.ReadLine();
    }