private void UpdateRLMResults(PlanogramOptResultsSettings results, bool enableSimDisplay)
{
enableSimDisplay = enableSimulation;
Dispatcher.Invoke(() =>
{
if (enableSimDisplay)
{
currentResults = results;
FillGrid(planogram, results.Shelves, false, ItemAttributes_MouseEnter, ItemAttributes_MouseLeave);
}
if (core.IsComparisonModeOn)
{
DisplayLearningComparisonResults(tmpSelectedData, tmpComparisonData);
}
else
{
string scoreText = (simSettings.SimType == SimulationType.Score) ? $"{results.Score.ToString("#,###.##")} ({results.NumScoreHits})" : results.Score.ToString("#,###.##");
scoreTxt.Text = scoreText;
sessionRunTxt.Text = results.CurrentSession.ToString();
timElapseTxt.Text = results.TimeElapsed.ToString();//string.Format("{0:D2}:{1:D2}:{2:D2}.{3:D3}", results.TimeElapsed.Hours, results.TimeElapsed.Minutes, results.TimeElapsed.Seconds, results.TimeElapsed.Milliseconds);
minScoretxt.Text = results.MinScore.ToString("#,###.##");
maxScoreTxt.Text = results.MaxScore.ToString("#,###.##");
//engineTxt.Text = "RLM";
averageScoreTxt.Text = results.AvgScore.ToString("#,###.##");
averageScoreOf10Txt.Text = results.AvgLastTen.ToString("#,###.##");
currentRandomnessTxt.Text = results.CurrentRandomnessValue.ToString();
startRandomnessTxt.Text = results.StartRandomness.ToString();
endRandomnessTxt.Text = results.EndRandomness.ToString();
sessionPerBatchTxt.Text = results.SessionsPerBatch.ToString();
inputTypeTxt.Text = results.InputType;
}
});
}
private void UpdateRLMResults(PlanogramOptResultsSettings results, bool enableSimDisplay)
{
if (enableSimDisplay)
{
Task.Delay(1).Wait();
}
Dispatcher.Invoke(() =>
{
currentResults = results;
if (enableSimDisplay && !core.IsComparisonModeOn)
{
FillGrid(gridIceCreamShelves, results.Shelves, false, ItemAttributes_MouseEnter, ItemAttributes_MouseLeave);
}
if (core.IsComparisonModeOn)
{
DisplayLearningComparisonResults(tmpSelectedData, tmpComparisonData);
}
else
{
string scoreText = (simSettings.SimType == SimulationType.Score) ? $"{results.Score.ToString("c")} ({results.NumScoreHits})" : results.Score.ToString("c");
txtCurrentScore.Text = scoreText;
//sessionRunTxt.Text = results.CurrentSession.ToString();
txtTimeElapsed.Text = results.TimeElapsed.ToString();
//txtMinScore.Text = results.MinScore.ToString("#,###.##");
txtMaxScore.Text = results.MaxScore.ToString("c");
//txtAverageScore.Text = results.AvgScore.ToString("#,###.##");
//txtAverageOfTenScores.Text = results.AvgLastTen.ToString("#,###.##");
//txtCurrentRandomnes.Text = results.CurrentRandomnessValue.ToString();
//txtStartRandomnes.Text = results.StartRandomness.ToString();
//txtEndRandomnes.Text = results.EndRandomness.ToString();
//txtSessionPerBatch.Text = results.SessionsPerBatch.ToString();
//txtInputType.Text = results.InputType;
}
});
}
/// <summary>
/// Does training or prediction for a set number of sessions
/// </summary>
/// <param name="sessions">Number of session to train/predict for</param>
/// <param name="learn">Lets the RLM know if we are training or predicting</param>
/// <returns>The final output for the training or prediction</returns>
private PlanogramOptResults Optimize(int sessions, bool learn = true, bool enablePlanogramDisplay = false, CancellationToken?cancelToken = null)
{
var output = new PlanogramOptResultsSettings();
// holds the unique SKUs that are already in the planogram. Ensures there are no duplicate SKUs
var hashedSku = new HashSet <int>();
var itemDict = new Dictionary <int, int>();
var shelves = new List <Shelf>();
double totalMetricScore = 0;
for (int i = 0; i < sessions; i++)
{
// reset for next session
shelves.Clear();
hashedSku.Clear();
itemDict.Clear();
ResetCurrentItemIndexes();
totalMetricScore = 0;
totalSessions++;
DateTime startSession = DateTime.Now;
// starts the session. we need to save the session ID as we will pass it to the cycle.Run later on
long sessionId = network.SessionStart();
int numSlotFlattened = 0;
// iterates for how many number of shelves our planogram has
for (int shelf = 1; shelf <= simSettings.NumShelves; shelf++)
{
// this shelf instance will hold the items the RLM will output
var shelfInstance = new Shelf()
{
Number = shelf
};
int itemIndex;
int numFacings;
// iterates for how many number of slots on each shelf
// notice that the slot is incremented depending on how many number of facings was outputed by the RLM
for (int slot = 1; slot <= simSettings.NumSlots; slot += numFacings)
{
itemIndex = -1;
numFacings = -1;
bool isValid = false;
numSlotFlattened++;
do
{
// create the inputs with their corresponding values
var inputs = new List <RlmIOWithValue>();
//inputs.Add(new RlmIOWithValue(network.Inputs.First(a => a.Name == "Shelf"), shelf.ToString()));
//inputs.Add(new RlmIOWithValue(network.Inputs.First(a => a.Name == "Slot"), slot.ToString()));
inputs.Add(new RlmIOWithValue(network.Inputs.First(a => a.Name == "Slot"), numSlotFlattened.ToString()));
var rlmItemOutput = network.Outputs.FirstOrDefault();
var rlmIdeas = new List <RlmIdea>()
{
new RlmOutputLimiter(rlmItemOutput.ID, currentItemIndexes.Count - 1, GetEquivalentIndex)
};
// runs a cycle with the sessionId passed in
var cycle = new RlmCycle();
var rlmOutput = cycle.RunCycle(network, sessionId, inputs, learn, ideas: rlmIdeas);
// get the outputs
// the RLM outputs the item index so we will get the actual reference to the Item later on and
// the second output is the number of facings that will show up on the plangram
itemIndex = Convert.ToInt32(rlmOutput.CycleOutput.Outputs.First(a => a.Name == "Item").Value);
numFacings = 1;//Convert.ToInt32(rlmOutput.CycleOutput.Outputs.First(a => a.Name == "NumFacings").Value);
// we calculate how many remaining slots are there left to check for validity
// because there might not be enough slots for what the number of facings was outputed by the RLM
int remainingSlots = simSettings.NumSlots - shelfInstance.Items.Count();
bool isWithinLimit = CheckDuplicateItem(itemDict, itemIndex, numFacings);
// here we check if the item is not a duplicate and that we have enough slots to fit the number of facings
//if (hashedSku.Add(itemIndex) && remainingSlots >= numFacings)
if (isWithinLimit && remainingSlots >= numFacings)
{
isValid = true;
Item itemReference = items[itemIndex]; // we get the item reference using the index outputed by the RLM
// with the item reference we will also have the Attributes which we need to calculate for the metrics
double itemMetricScore = PlanogramOptimizer.GetCalculatedWeightedMetrics(itemReference, simSettings);
// we add the item's metric score to totalMetricScore(which is our Session score in this case)
// notice that we multplied it with the numFacings since that is how many will also show up on the planogram
totalMetricScore += (itemMetricScore * numFacings);
// add the items to the shelf container depending on how many facings
for (int n = 0; n < numFacings; n++)
{
shelfInstance.Add(itemReference, itemMetricScore);
}
// A non-duplicate is good.
network.ScoreCycle(rlmOutput.CycleOutput.CycleID, 1);
}
else
{
// we give the cycle a zero (0) score as it was not able to satisfy our conditions (punish it)
isValid = false;
network.ScoreCycle(rlmOutput.CycleOutput.CycleID, -1);
//System.Diagnostics.Debug.WriteLine("try again");
}
} while (!isValid); // if invalid, we redo the whole thing until the RLM is able to output an item that is unique and fits the remaining slot in the planogram
}
shelves.Add(shelfInstance);
}
// ends the session with the summed metric score for all items in the planogram
network.SessionEnd(totalMetricScore);
System.Diagnostics.Debug.WriteLine($"Session #{i}, Score: {totalMetricScore}");
// set statistics and the optimized planogram shelves (and items inside them)
output.Shelves = shelves;
metricScoreHistory.Add(totalMetricScore);
metricAvgLastTen.Enqueue(totalMetricScore);
if (metricAvgLastTen.Count > 10)
{
metricAvgLastTen.Dequeue();
}
output.Score = totalMetricScore;
output.AvgScore = metricScoreHistory.Average();
output.AvgLastTen = metricAvgLastTen.Average();
output.MinScore = metricScoreHistory.Min();
output.MaxScore = metricScoreHistory.Max();
output.TimeElapsed = DateTime.Now - simSettings.StartedOn;
output.CurrentSession = totalSessions;
output.MaxItems = MAX_ITEMS;
output.StartRandomness = network.StartRandomness;
output.EndRandomness = network.EndRandomness;
output.SessionsPerBatch = DEFAULT_SESSIONS_PER_BATCH;
output.InputType = inputType.ToString();
output.CurrentRandomnessValue = network.RandomnessCurrentValue;
if (logger != null)
{
SimulationData logdata = new SimulationData();
logdata.Session = output.CurrentSession;
logdata.Score = output.Score;
logdata.Elapse = DateTime.Now - startSession;
logger.Add(logdata);
}
// update the numScoreHits if the sim type is Score
if (simSettings.SimType == SimulationType.Score)
{
if (totalMetricScore >= simSettings.Score.Value)
{
numScoreHits++;
output.NumScoreHits = numScoreHits;
}
else
{
numScoreHits = 0;
}
}
//OnSessionDone?.Invoke(output);
// updates the results to the UI
//bool enableSimDisplay = (!learn) ? true : (learn && simSettings.EnableSimDisplay) ? true : false;
if (enablePlanogramDisplay)
{
output.MetricMin = simSettings.ItemMetricMin;
output.MetricMax = simSettings.ItemMetricMax;
output.CalculateItemColorIntensity();
}
UpdateUI?.Invoke(output, enablePlanogramDisplay);
// checks if we have already by passed the time or score that was set
// if we did, then we stop the training and end it abruptly
if ((simSettings.SimType == SimulationType.Time && simSettings.EndsOn.Value <= DateTime.Now) ||
(simSettings.SimType == SimulationType.Score && numScoreHits >= SimulationSettings.NUM_SCORE_HITS))
{
break;
}
if (cancelToken.HasValue && cancelToken.Value.IsCancellationRequested)
{
return(output);
}
}
return(output);
}
