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); } }