public DataProcessorConf GetDataProcessor()
{
XElement dataProcessor = xmlNeuroNet.Element("DataProcessor")
.Elements().Skip(1).First();
DataProcessorConf res = new DataProcessorConf()
{
IsUsed = bool.Parse(dataProcessor.Attribute("IsUsed").Value),
A = int.Parse(dataProcessor.Attribute("A").Value),
B = int.Parse(dataProcessor.Attribute("B").Value),
Type = dataProcessor.Attribute("Type").Value
};
var inArr = dataProcessor.Elements("InC").ToArray();
int sizeIn = inArr.Count();
res.InCC = new double[sizeIn];
res.InCD = new double[sizeIn];
//System.Globalization.NumberFormatInfo.
for (int i = 0; i < sizeIn; i++)
{
res.InCC[i] = double.Parse(inArr[i].Attribute("C").Value.Replace('.', ','));
res.InCD[i] = double.Parse(inArr[i].Attribute("D").Value.Replace('.', ','));
}
var outArr = dataProcessor.Elements("OutC").ToArray();
int sizeOut = outArr.Count();
res.OutCC = new double[sizeOut];
res.OutCD = new double[sizeOut];
for (int i = 0; i < sizeOut; i++)
{
res.OutCC[i] = double.Parse(outArr[i].Attribute("C").Value.Replace('.', ','));
res.OutCD[i] = double.Parse(outArr[i].Attribute("D").Value.Replace('.', ','));
}
return res;
}
public void DataProcessorM(DataProcessorConf confDP)
{
_confDP = confDP;
if (confDP.Type == "LinearScale")
{
linearScale = new myNsim4.LinearScale();
linearScale.A = confDP.A;
linearScale.B = confDP.B;
linearScale.IsUsed = confDP.IsUsed;
linearScale.InC = this.ConvertToScalerC(confDP.InCC, confDP.InCD);
linearScale.OutC = this.ConvertToScalerC(confDP.OutCC, confDP.OutCD);
}
}
