public MainWindow()
{
InitializeComponent();
headToHead = false;
Width = 580;
//toggleColorBtn.Margin = new Thickness(422, 14, 0, 0); //new Thickness(62, 10, 0, 0);
//dataGenerationBtn.Margin = new Thickness(24, 14, 0, 0); //new Thickness(182, 10, 0, 0);
//metricsBtn.Margin = new Thickness(146, 14, 0, 0); //new Thickness(302, 10, 0, 0);
//runSlmBtn.Margin = new Thickness(266, 14, 0, 0); //new Thickness(422, 10, 0, 0);
rectHeadToHeadDivider.Visibility = Visibility.Hidden;
planogramTensorflow.Visibility = Visibility.Hidden;
tensorFlowPlanogramScore.Visibility = Visibility.Hidden;
GrdTensorFlowsetting.Visibility = Visibility.Hidden;
grpBox_Tensorflow.Visibility = Visibility.Hidden;
FillGrid(planogram, Colors.LightGray);
//For testing only
string dbIdentifier = "RLV_small";
// instantiate visualizer with this window as its parent reference
visualizer = new RLVOutputVisualizer(this);
core = new RLVCore(dbIdentifier);
//core.SetupVisualizer(new List<IRLVPanel>
//{
// detailsPanel,
// chartPanel
//}, visualizer);
//this.Resources.MergedDictionaries.Add(new ResourceDictionary() { Source = new Uri("pack://application:,,,/MahApps.Metro;component/Styles/Controls.xaml") });
//this.Resources.MergedDictionaries.Add(new ResourceDictionary() { Source = new Uri("pack://application:,,,/MahApps.Metro;component/Styles/Fonts.xaml") });
//this.Resources.MergedDictionaries.Add(new ResourceDictionary() { Source = new Uri("pack://application:,,,/MahApps.Metro;component/Styles/Colors.xaml") });
//this.Resources.MergedDictionaries.Add(new ResourceDictionary() { Source = new Uri("pack://application:,,,/MahApps.Metro;component/Styles/Accents/Blue.xaml") });
//this.Resources.MergedDictionaries.Add(new ResourceDictionary() { Source = new Uri("pack://application:,,,/MahApps.Metro;component/Styles/Accents/BaseLight.xaml") });
}
private void runSlmBtn_Click(object sender, RoutedEventArgs e)
{
selectedSlotIndex = -1;
_row = -1;
_col = -1;
itemRow = -1;
itemCol = -1;
SimulationPanel simPanel = new SimulationPanel();
simPanel.SetSimSettings(simSettings);
bool?result = simPanel.ShowDialog();
if (result.HasValue && result.Value == true)
{
// resets grid to default
usePerfColor = false;
FillGrid(planogram, Colors.LightGray);
if (headToHead)
{
FillGrid(planogramTensorflow, Colors.LightGray);
}
// disable control buttons
//statusTxt.Text = statusTxtTensor.Text = "";
statusTxtTensor.Text = "Waiting for RLM to finish running...";
EnableControlButtons(false);
// set simulation settings
simSettings.SimType = simPanel.SimType;
simSettings.Sessions = simPanel.Sessions;
simSettings.Hours = simPanel.Hours;
simSettings.Score = simPanel.Score;
simSettings.EnableSimDisplay = simPanel.EnableSimDisplay;
simSettings.DefaultScorePercentage = simPanel.simScoreSlider.Value;
simSettings.HiddenLayers = simPanel.HiddenLayers;
simSettings.HiddenLayerNeurons = simPanel.HiddenLayerNeurons;
targetScoreTxt.Text = "";
if (simSettings.SimType == SimulationType.Score)
{
targetScoreLbl.Visibility = Visibility.Visible;
targetScoreTxt.Visibility = Visibility.Visible;
targetScoreTxt.Text = simSettings.Score.Value.ToString("n");
targetScoreTxt2.Visibility = Visibility.Visible;
targetScoreTxt2.Text = simSettings.Score.Value.ToString("n");
}
else
{
targetScoreLbl.Visibility = Visibility.Hidden;
targetScoreTxt.Visibility = Visibility.Hidden;
targetScoreTxt2.Visibility = Visibility.Hidden;
}
if (simSettings.SimType == SimulationType.Sessions)
{
sessionPerBatchLbl.Visibility = Visibility.Hidden;
sessionPerBatchTxt.Visibility = Visibility.Hidden;
}
else
{
sessionPerBatchLbl.Visibility = Visibility.Visible;
sessionPerBatchTxt.Visibility = Visibility.Visible;
}
Logger.Clear();
string dbIdentifier = "RLM_planogram_" + Guid.NewGuid().ToString("N");
// instantiate visualizer with this window as its parent reference
visualizer = new RLVOutputVisualizer(this);
core = new RLVCore(dbIdentifier);
// subscribe mainwindow to the comparison event
//visualizer.LearningComparisonDisplayResultsEvent += DisplayLearningComparisonResults;
// open temporary RLV container panel
// todo this must be embeded in this Window instead of the temporary container
if (rlvPanel != null)
{
rlvPanel.Close();
}
rlvPanel = new TempRLVContainerPanel(core, visualizer);
//this.Top = 20;
//tmpPanel.Top = this.Top;
//this.Height = tmpPanel.Height;
//tmpPanel.Left = 10;
//this.Left = tmpPanel.Width + tmpPanel.Left;
//tmpPanel.Visibility = Visibility.Hidden;
Task.Run(() =>
{
// get items from db as well as the min and max metric scores as we need that for the calculation later on
Item[] items;
using (PlanogramContext ctx = new PlanogramContext())
{
MockData mock = new MockData(ctx);
items = itemsCache = mock.GetItemsWithAttr();
simSettings.ItemMetricMin = mock.GetItemMinimumScore(simSettings);
simSettings.ItemMetricMax = mock.GetItemMaximumScore(simSettings);
}
// let's tensorflow (or other listeners) know that it should start training
//OnSimulationStart?.Invoke(items, simSettings); //return;
// initialize and start RLM training
optimizer = new PlanogramOptimizer(items, simSettings, this.UpdateRLMResults, this.UpdateRLMStatus, Logger, dbIdentifier);
//optimizer.OnSessionDone += Optimizer_OnSessionDone;
optimizer.StartOptimization(tokenSource.Token);
});
}
}