public static void ProtoSaveToFile(object sender, DoWorkEventArgs e)
{
try
{
worker = sender as BackgroundWorker;
string filename = e.Argument as string;
SaveLoadData data = new SaveLoadData();
data.railml = XML.ToXElement <railml>(Data.DataContainer.model).ToString();
data.NN = DataContainer.NeuralNetwork;
if (DataContainer.NeuralNetwork.Network != null)
{
data.network = SerializeNetwork();
}
if (DataContainer.NeuralNetwork.Data != null)
{
data.trainingset = SerializeDataSet();
}
MyStream stream = new MyStream(filename, FileMode.Create, FileAccess.Write);
stream.ProgressChanged += new ProgressChangedEventHandler(Save_ProgressChanged);
Serializer.Serialize(stream, data);
stream.Close();
}
catch (Exception ex)
{
MessageBox.Show(ex + " Inner Exception: " + ex.InnerException);
worker.ReportProgress(1, ex + " Inner Exception: " + ex.InnerException);
}
}
private static void Load_ProgressChanged(object sender, ProgressChangedEventArgs e)
{
MyStream stream = sender as MyStream;
long percentage = ((long)e.UserState) * 100 / stream.Length;
worker.ReportProgress(0, percentage);
}
public static void LoadFile(object sender, DoWorkEventArgs e)
{
worker = sender as BackgroundWorker;
string filename = e.Argument as string;
MyStream stream = new MyStream(filename, FileMode.Open, FileAccess.Read);
stream.ProgressChanged += new ProgressChangedEventHandler(Load_ProgressChanged);
IFormatter formatter = new BinaryFormatter();
SaveLoadData data = (SaveLoadData)formatter.Deserialize(stream);
XElement elem = XElement.Parse(data.railml);
DataContainer.model = XML.FromXElement <railml>(elem);
DataContainer.NeuralNetwork = data.NN;
stream.Close();
}
public static void SaveToFile(object sender, DoWorkEventArgs e)
{
worker = sender as BackgroundWorker;
string filename = e.Argument as string;
SaveLoadData data = new SaveLoadData();
data.railml = XML.ToXElement <railml>(Data.DataContainer.model).ToString();
data.NN = DataContainer.NeuralNetwork;
MyStream stream = new MyStream(filename, FileMode.Create, FileAccess.Write);
stream.ProgressChanged += new ProgressChangedEventHandler(Save_ProgressChanged);
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(stream, data);
stream.Close();
}
public static void ProtoLoadFile(object sender, DoWorkEventArgs e)
{
worker = sender as BackgroundWorker;
string filename = e.Argument as string;
MyStream stream = new MyStream(filename, FileMode.Open, FileAccess.Read);
stream.ProgressChanged += new ProgressChangedEventHandler(Load_ProgressChanged);
SaveLoadData data = Serializer.Deserialize <SaveLoadData>(stream);
XElement elem = XElement.Parse(data.railml);
DataContainer.model = XML.FromXElement <railml>(elem);
DataContainer.NeuralNetwork = data.NN;
if (data.trainingset != null)
{
DataContainer.NeuralNetwork.Data = DeserializeDataSet(data.trainingset);
}
if (data.network != null)
{
DataContainer.NeuralNetwork.Network = DeserializeNetwork(data.network);
}
stream.Close();
}
public static void SaveToFile(object sender, DoWorkEventArgs e )
{
worker = sender as BackgroundWorker;
string filename = e.Argument as string;
SaveLoadData data = new SaveLoadData();
data.railml = XML.ToXElement<railml>(Data.DataContainer.model).ToString();
data.NN = DataContainer.NeuralNetwork;
MyStream stream = new MyStream(filename, FileMode.Create, FileAccess.Write);
stream.ProgressChanged += new ProgressChangedEventHandler(Save_ProgressChanged);
IFormatter formatter = new BinaryFormatter();
formatter.Serialize(stream, data);
stream.Close();
}
public static void ProtoSaveToFile(object sender, DoWorkEventArgs e)
{
try
{
worker = sender as BackgroundWorker;
string filename = e.Argument as string;
SaveLoadData data = new SaveLoadData();
data.railml = XML.ToXElement<railml>(Data.DataContainer.model).ToString();
data.NN = DataContainer.NeuralNetwork;
if (DataContainer.NeuralNetwork.Network != null) { data.network = SerializeNetwork(); }
if (DataContainer.NeuralNetwork.Data != null) { data.trainingset = SerializeDataSet(); }
MyStream stream = new MyStream(filename, FileMode.Create, FileAccess.Write);
stream.ProgressChanged += new ProgressChangedEventHandler(Save_ProgressChanged);
Serializer.Serialize(stream, data);
stream.Close();
}
catch(Exception ex)
{
MessageBox.Show(ex + " Inner Exception: " + ex.InnerException);
worker.ReportProgress(1, ex + " Inner Exception: " + ex.InnerException);
}
}
public static void ProtoLoadFile(object sender, DoWorkEventArgs e)
{
worker = sender as BackgroundWorker;
string filename = e.Argument as string;
MyStream stream = new MyStream(filename, FileMode.Open, FileAccess.Read);
stream.ProgressChanged += new ProgressChangedEventHandler(Load_ProgressChanged);
SaveLoadData data = Serializer.Deserialize<SaveLoadData>(stream);
XElement elem = XElement.Parse(data.railml);
DataContainer.model = XML.FromXElement<railml>(elem);
DataContainer.NeuralNetwork = data.NN;
if (data.trainingset != null) { DataContainer.NeuralNetwork.Data = DeserializeDataSet(data.trainingset); }
if (data.network != null) { DataContainer.NeuralNetwork.Network = DeserializeNetwork(data.network); }
stream.Close();
}
public static void LoadFile(object sender, DoWorkEventArgs e)
{
worker = sender as BackgroundWorker;
string filename = e.Argument as string;
MyStream stream = new MyStream(filename, FileMode.Open, FileAccess.Read);
stream.ProgressChanged += new ProgressChangedEventHandler(Load_ProgressChanged);
IFormatter formatter = new BinaryFormatter();
SaveLoadData data = (SaveLoadData)formatter.Deserialize(stream);
XElement elem = XElement.Parse(data.railml);
DataContainer.model = XML.FromXElement<railml>(elem);
DataContainer.NeuralNetwork = data.NN;
stream.Close();
}
