private void SetPlottingMethod(PartIIProblemType problemType)
{
if (problemType == PartIIProblemType.CTS)
{
plotAgainstInput = true;
}
else
{
plotAgainstInput = false;
}
}
private void StartButtonClick(object sender, RoutedEventArgs e)
{
ToggleSensitiveGUIParts(false);
SetResultsLink(false, null);
ToggleAutomationRelatedSettings(true); // user can prepare params for the next run
runsPerSettings = int.Parse(RunsTextBox.Text);
bool bias = (YesNo)BiasCombobox.SelectedItem == YesNo.Yes;
int pcaDimensions = 0;
int.TryParse(PCA.Text, out pcaDimensions); // TODO: test
float trainSetPercentage = float.Parse(TrainSetPercentage.Text, CultureInfo.InvariantCulture);
int outputCount = int.Parse(OutputCount.Text);
NetworkType networkType = (NetworkType)NetworkTypeCombobox.SelectedItem;
PartIIProblemType problemType = csvLines[0].Count == 1 ? PartIIProblemType.CTS : PartIIProblemType.Stock;
ReportingOptions reportingOptions = GetReportingOptions();
if (settingsToRun.Count == 0)
{
settingsToRun = GetLearningSettingsFromUI();
}
SetPlottingMethod(problemType);
eid = new EngineInitData();
eid.ErrorScale = ERROR_SCALE;
eid.DiscardWorstFactor = DISCARD_FACTOR;
eid.TrainSetPercentage = trainSetPercentage;
eid.CtsPrevValuesCount = 1; // always 1
eid.OutputCount = outputCount;
eid.InputCount = problemType == PartIIProblemType.CTS ? 1 : csvLines[0].Count - outputCount;
if (pcaDimensions > 0)
{
eid.InputCount = Math.Min(pcaDimensions, eid.InputCount);
}
eid.PcaDimensions = pcaDimensions;
eid.RunsPerSettings = runsPerSettings;
eid.UseBiases = bias;
eid.PlotAgainstInput = plotAgainstInput;
eid.NetworkType = networkType;
eid.ProblemType = problemType;
eid.ReportingOptions = reportingOptions;
eid.CsvLines = csvLines;
eid.SettingsToRun = settingsToRun;
eid.DataSetName = dataSetPath;
eid.ParametersFileName = paramsFileName;
ConfirmReportingSettings();
CreateResultDirectories(DateTime.Now);
worker = new BackgroundWorker();
worker.DoWork += worker_DoWork;
worker.RunWorkerCompleted += worker_RunWorkerCompleted;
worker.RunWorkerAsync();
}
