public RlmOptimizer(IRlmDbData rlmDbData, IRlmRneuronProcessor gpu = null)
{
Resources = new Dictionary <string, Resource>();
ResourceAttributes = new Dictionary <string, ResourceAttribute>();
Constraints = new Dictionary <string, Constraint>();
CycleOutputs = new Dictionary <string, object>();
SessionOutputs = new Dictionary <string, List <object> >();
CycleInputs = new Dictionary <string, object>();
TrainingVariables.Add("CycleScore", new TrainingVariable()
{
Name = "CycleScore"
});
TrainingVariables.Add("SessionScore", new TrainingVariable()
{
Name = "SessionScore"
});
//if (!string.IsNullOrEmpty(databaseName))
//{
// DatabaseName = databaseName;
//}
RlmDbData = rlmDbData;
DatabaseName = rlmDbData.DatabaseName;
if (gpu != null)
{
Gpu = gpu;
}
}
private void btnVisualizer_Click(object sender, RoutedEventArgs e)
{
//rlmDbData = new RlmDbDataPostgreSqlServer(dbIdentifier);
rlmDbData = new RlmDbDataSQLServer(dbIdentifier);
core = new RLVCore(rlmDbData);
rlvPanel = new VisualizerWindow(core, challenger.HighestMoveCount, challenger.RecentMoves);
rlvPanel.Show();
}
/// <summary>
/// sets your preferred database name
/// </summary>
/// <param name="databaseName">Uses a custom database name instead of the default generated name</param>
/// <param name="persistData">Allows you to turn on/off the data persistence feature of the RLM. Turned on by default.</param>
public RlmNetwork(IRlmDbData rlmDbData, bool persistData = true, IRlmRneuronProcessor gpu = null)
{
PersistData = persistData;
this.RlmDBData = rlmDbData;
DatabaseName = rlmDbData.DatabaseName;
rlmDbData.Initialize();
SessionCaseHistory = new RlmSessionCaseHistory(rlmDbData);
Initialize(gpu);
}
private void Window_Loaded(object sender, RoutedEventArgs e)
{
string dbIdentifier = "RLV_small";
// instantiate visualizer with this window as its parent reference
visualizer = new RLVOutputVisualizer(this);
rlmDbData = new RlmDbDataSQLServer(dbIdentifier);
//rlmDbData = new RlmDbDataPostgreSqlServer(dbIdentifier);
core = new RLVCore(rlmDbData);
this.Top = 20;
rlvPanel = new VisualizerWindow(core, visualizer);
}
private void startOptimizing()
{
trainingOverlay.Visibility = Visibility.Visible;
selectedSlotIndex = -1;
_row = -1;
_col = -1;
itemRow = -1;
itemCol = 1;
SimulationPanel simPanel = new SimulationPanel(mock);
simPanel.SetSimSettings(simSettings);
//bool? result = simPanel.ShowDialog();
simPanel.btnRun_Click(null, null);
//if (result.HasValue && result.Value == true)
{
// resets grid to default
FillGrid(gridIceCreamShelves, Colors.LightGray);
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;
txtTargetScore.Text = "";
if (simSettings.SimType == SimulationType.Score)
{
txtTargetScore.Text = simSettings.Score.Value.ToString("n");
}
else
{
txtTargetScore.Visibility = Visibility.Hidden;
}
//if (simSettings.SimType == SimulationType.Sessions)
//{
// lblSessionPerBatch.Visibility = Visibility.Hidden;
// txtSessionPerBatch.Visibility = Visibility.Hidden;
//}
//else
//{
// lblSessionPerBatch.Visibility = Visibility.Visible;
// txtSessionPerBatch.Visibility = Visibility.Visible;
//}
string dbIdentifier = "RLM_planogram_" + Guid.NewGuid().ToString("N");
// instantiate visualizer with this window as its parent reference
visualizer = new RLVOutputVisualizer(this);
rlmDbData = new RlmDbDataSQLServer(dbIdentifier);
//rlmDbData = new RlmDbDataPostgreSqlServer(dbIdentifier);
core = new RLVCore(rlmDbData);
// 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 VisualizerWindow(core, visualizer);
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);
}
// initialize and start RLM training
optimizer = new PlanogramOptimizer(items, simSettings, this.UpdateRLMResults, this.UpdateRLMStatus, Logger, dbIdentifier, OnRLMDataPersistProgress);
//optimizer.OnSessionDone += Optimizer_OnSessionDone;
optimizer.StartOptimization();
});
}
}
public RlmNetworkLegacy(IRlmDbData rlmDbData, bool persistData = true) : base(rlmDbData, persistData)
{
rlmDbDataLegacy = rlmDbData;
}
public RlmNetworkWebAPI(IRlmDbData rlmDbData) : base(rlmDbData)
{
}
public RlmSessionCaseHistory(IRlmDbData rlmDb)
{
this.rlmDb = rlmDb;
rlmDb.Initialize();
DatabaseName = rlmDb.DatabaseName;
}
public RLVCore(IRlmDbData rlmDb)
{
rlmDb.Initialize();
rlmHistory = new RlmSessionCaseHistory(rlmDb);
}
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);
rlmDbData = new RlmDbDataSQLServer(dbIdentifier);
//rlmDbData = new RlmDbDataPostgreSqlServer(dbIdentifier);
core = new RLVCore(rlmDbData);
// 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, tokenSource.Token); 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);
});
}
}
