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);
}
}