public static NNData XMLToDataByName(string filepath, string Name, string InNode)
{
/* Sucht im XML-File in filepath, in der Node InNode (muss direkt am document-root von filepath hängen), das Neural Network mit dem Namen Name)
* und Speichert dies in einer NNData-Klasse. Aus dieser kann mit dem Konstruktor NeuralNetwork(NNData data) in ein
* Neural Network mit den im XML-file gespeichtern eigenschaften erzeugt werden */
NNData data = new NNData();
XmlNode Weights_node_xml;
string filesep = @"/";
XmlDocument doc = new XmlDocument();
doc.Load(filepath);
XmlElement root = doc.DocumentElement;
Weights_node_xml = doc.SelectSingleNode(InNode + filesep + Name + @"/Weights");
var Weights_nodeList_xml = Weights_node_xml.ChildNodes;
int size = Weights_nodeList_xml.Count;
string NNname = Name;
data.Weights = new float[size];
XmlNode NumIp_xml, NumHd_xml, NumOp_xml;
NumIp_xml = doc.SelectSingleNode(root.Name + "/" + NNname + "/NumberOfIpNeurons");
int NIP = new int();
NIP = Convert.ToInt32(NumIp_xml.InnerText);
data.NumIpNeur = NIP;
NumHd_xml = doc.SelectSingleNode(root.Name + "/" + NNname + "/NumberOfHdNeurons");
int NHD = new int();
NHD = Convert.ToInt32(NumHd_xml.InnerText);
data.NumHdNeur = NHD;
NumOp_xml = doc.SelectSingleNode(root.Name + "/" + NNname + "/NumberOfOpNeurons");
int NOP = new int();
NOP = Convert.ToInt32(NumOp_xml.InnerText);
data.NumOpNeur = NOP;
var checkchildnodes = Weights_node_xml.ChildNodes;
int i = 0;
foreach (XmlNode w in Weights_node_xml.ChildNodes)
{
data.Weights[i] = float.Parse(w.InnerText);
i++;
}
return(data);
}
public NeuralNetwork(NNData Data) : this(Data.NumIpNeur, Data.NumHdNeur, Data.NumOpNeur, Data.Weights, Data.ActivFct)
{
}
