public АverageMethod(XLSReader Reader)
 {
     Init(Reader);
     NetWorker = new NeuroNetWorker("AverageMethod" + Reader.FileName, consider.Column, edge: false);
     statCount = 0;
     statAll   = 0;
 }
 public Case(XLSReader Reader)
 {
     objPreparator = new Preparator();
     Init(Reader);
     NetWorker = new NeuroNetWorker("CaseMethod" + Reader.FileName, objPreparator.Count, edge: false);
     P = 0;
     N = 0;
     TP = 0;
     FP = 0;
     TN = 0;
     FN = 0;
 }
 public Simple(XLSReader Reader)
 {
     foreach (var obj in Reader.ReadHeaders(4))
     {
         continue;
     }
     countParams = 20;
     Valuyer     = new Dictionary <int, Dictionary <string, int> >();
     NetWorker   = new NeuroNetWorker("SimpleMethod" + Reader.FileName, countParams, edge: false);
     P           = 0;
     N           = 0;
     TP          = 0;
     FP          = 0;
     TN          = 0;
     FN          = 0;
 }
 /// <summary>
 /// Подготавливает входные данные для нейронных
 /// сетей и сами нейронные сети
 /// </summary>
 public void PrepareValue()
 {
     if (type == TypeBLO.String)
     {
         PrepareString();
         netWorker = new NeuroNetWorker(BLObj, map.Count);
     }
     if (type == TypeBLO.Number)
     {
         netWorker = new NeuroNetWorker(BLObj, 5, lowerEdge: minValue, upperEdge: maxValue);
     }
     if (type == TypeBLO.Boolean)
     {
         PrepareBoolean();
         netWorker = new NeuroNetWorker(BLObj, 3);
     }
     if (type == TypeBLO.Percent)
     {
         netWorker = new NeuroNetWorker(BLObj, 5, lowerEdge: 0, upperEdge: 100);
     }
 }