private void LoadFromXMLButton_Click(object sender, RoutedEventArgs e)
{
if (mainWindow.neuralNetwork != null)
{
SaveWarning warning = new SaveWarning();
warning.ShowDialog();
if (warning.OverrideNetwork)
{
OpenFileDialog file = new OpenFileDialog();
file.CheckFileExists = true;
file.Multiselect = false;
file.Filter = "XML (.xml)|*.xml";
file.ShowDialog();
mainWindow.neuralNetwork = NeuralNetwork.LoadNetworkFromXML(file.FileName);
}
}
else
{
OpenFileDialog file = new OpenFileDialog();
file.CheckFileExists = true;
file.Multiselect = false;
file.Filter = "XML (.xml)|*.xml";
file.ShowDialog();
mainWindow.neuralNetwork = NeuralNetwork.LoadNetworkFromXML(file.FileName);
}
}
} // constructor
public InferenceSaver Execute()
{
if (this.requestID <= 0)
{
throw new InferenceSaverWrongRequestIDAlert(this.requestID, Log);
}
if (this.response == null)
{
throw new InferenceSaverNoDataAlert(this.requestID, Log);
}
if (Executed)
{
Log.Alert(
"Inference saver({0}, '{1}') has already been executed.",
this.requestID,
this.response.ToShortString()
);
return(this);
} // if
Executed = true;
Log.Debug(
"Executing inference saver({0}, '{1}')...",
this.requestID,
this.response.ToShortString()
);
ConnectionWrapper con = DB.GetPersistent();
con.BeginTransaction();
try {
new SaveRawResponse(this.requestID, this.response, DB, Log).ExecuteNonQuery(con);
ResponseID = new SaveResponse(
this.requestID,
this.response,
this.bucketRepo,
this.timeoutSourceRepo,
DB,
Log
).Execute(con);
if (this.response.Parsed.HasInference())
{
var map = new SortedDictionary <ModelNames, long>();
var saveMo = new SaveModelOutput(ResponseID, this.response, DB, Log);
if (saveMo.HasValidParameters())
{
saveMo.ForEachRowSafe(con, sr => {
long id = sr["ModelOutputID"];
ModelNames name = (ModelNames)(int)(long)sr["ModelID"];
map[name] = id;
});
var saveEf = new SaveEncodingFailure(map, this.response, DB, Log);
if (saveEf.HasValidParameters()) // invalid if e.g. no failures
{
saveEf.ExecuteNonQuery(con);
}
var saveMi = new SaveMissingColumn(map, this.response, DB, Log);
if (saveMi.HasValidParameters()) // invalid if e.g. no missing columns
{
saveMi.ExecuteNonQuery(con);
}
var saveOr = new SaveOutputRatio(map, this.response, DB, Log);
if (saveOr.HasValidParameters()) // invalid if e.g. no output ratio
{
saveOr.ExecuteNonQuery(con);
}
var saveW = new SaveWarning(map, this.response, DB, Log);
if (saveW.HasValidParameters()) // invalid if e.g. no output ratio
{
saveW.ExecuteNonQuery(con);
}
} // if
} // if
var saveEtl = new SaveEtlData(ResponseID, this.response, this.etlCodeRepo, DB, Log);
if (saveEtl.HasValidParameters()) // invalid if e.g. no ETL data
{
saveEtl.Execute(con);
}
new SaveCustomerHistory(ResponseID, DB, Log).ExecuteNonQuery(con);
con.Commit();
} catch (Exception e) {
con.Rollback();
Log.Warn(
"Executing inference saver({0}, '{1}') failed because of exception: '{2}'.",
this.requestID,
this.response.ToShortString(),
e.Message
);
throw;
} // try
Log.Debug(
"Executing inference saver({0}, '{1}') complete, response ID is {2}.",
this.requestID,
this.response.ToShortString(),
ResponseID
);
return(this);
} // Execute
private void CreateNetworkButton_Click(object sender, RoutedEventArgs e)
{
//check if learning rate is set
double learningRate;
if (!Double.TryParse(LearningRateTextBox.Text, out learningRate))
{
consoleTextBox.AppendText("Learning rate must be set and must be in a correct format (x,xxx) ! \n");
return;
}
for (int lay = 1; lay < layerDisplay.Items.Count; lay++)
{
if ((layerDisplay.Items[lay] as NetworkLayerDescriptor).neurons == 0)
{
consoleTextBox.AppendText("Network´s layer cant have 0 neurons ! \n");
return;
}
}
neuralNetwork = window.neuralNetwork;
SaveWarning saveWarning;
//if there allready is a network, ask user to save it
if (neuralNetwork != null)
{
saveWarning = new SaveWarning();
saveWarning.ShowDialog();
if (saveWarning.OverrideNetwork)
{
List <int> layers = new List <int>();
List <ActivationFunctions> activationFunctions = new List <ActivationFunctions>();
for (int lay = 1; lay < layerDisplay.Items.Count; lay++)
{
layers.Add((layerDisplay.Items[lay] as NetworkLayerDescriptor).neurons);
activationFunctions.Add((ActivationFunctions)Enum.Parse(typeof(ActivationFunctions), (layerDisplay.Items[lay] as NetworkLayerDescriptor).selectedIndex.ToString()));
}
window.neuralNetwork = new NeuralNetwork(layers.ToArray(), learningRate, activationFunctions);
this.neuralNetwork = window.neuralNetwork;
consoleTextBox.AppendText("Network succesfully created ! \n");
}
else
{
consoleTextBox.AppendText("Creation canceled \n");
}
}
//if there is no network, create a new one
else
{
List <int> layers = new List <int>();
List <ActivationFunctions> activationFunctions = new List <ActivationFunctions>();
for (int lay = 1; lay < layerDisplay.Items.Count; lay++)
{
layers.Add((layerDisplay.Items[lay] as NetworkLayerDescriptor).neurons);
activationFunctions.Add((ActivationFunctions)Enum.Parse(typeof(ActivationFunctions), (layerDisplay.Items[lay] as NetworkLayerDescriptor).selectedIndex.ToString()));
}
window.neuralNetwork = new NeuralNetwork(layers.ToArray(), learningRate, activationFunctions);
this.neuralNetwork = window.neuralNetwork;
consoleTextBox.AppendText("Network succesfully created ! \n");
}
}
