/// <summary>
/// Starts optimization based on the array of items and simulation setting that were passed in during instantiation
/// </summary>
/// <returns>The final (predicted) output of the optimization</returns>
public PlanogramOptResults StartOptimization()
{
UpdateStatus?.Invoke("Training...");
var retVal = new PlanogramOptResults();
// checks what type of simulation we are running
int sessions = simSettings.SimType == SimulationType.Sessions ? simSettings.Sessions.Value : DEFAULT_SESSIONS_PER_BATCH;
double hours = simSettings.SimType == SimulationType.Time ? simSettings.Hours.Value : 0;
simSettings.StartedOn = DateTime.Now;
simSettings.EndsOn = DateTime.Now.AddHours(hours);
// simulation scenarios:
// if we are doing SimulationType.Sessions then the do-while loop will fail, just does the codeblock once, and trains the network with the specific number of sessions
// if we are doing SimulationType.Time then as default it will train for 100 sessions at a time (per loop) and continues to do so until time runs out
// if we are doing SimulationType.Score then it will execute just like the Time option except that it will continue to do so until the specified Score is met or surpassed
do
{
// settings
network.NumSessions = sessions;
// you can change this and start experimenting which range is best
// NOTE if you decide to have multiple facings (by changing the NumFacings output Max to greater than 1) and enforce item uniqueness then the EndRandomness must not be 0 (i suggest 5 as the minimum)
// as the planogram needs a bit of randomness towards the end since we have a condition where we enforce uniqueness of items. In the event that the RLM outputs an item that
// is already in the planogram then having enough randomness left will allow it to still have a chance to suggest a different item otherwise the RLM will suggest the same item over and over again and cause an infinite loop
network.StartRandomness = START_RANDOMNESS;
network.EndRandomness = END_RANDOMNESS; //(simSettings.SimType == SimulationType.Sessions) ? 0 : network.StartRandomness;
//network.MaxLinearBracket = MAX_LINEAR;
//network.MinLinearBracket = MIN_LINEAR;
// since we might be retraining the network (i.e, SimulationType.Time or Score), we need to call this method to reset the randomization counter of the RLM
network.ResetRandomizationCounter();
// training, train 90% per session batch if not Session Type
var trainingTimes = (simSettings.SimType == SimulationType.Sessions) ? sessions : sessions - PREDICT_SESSIONS;
retVal = Optimize(trainingTimes, true, simSettings.EnableSimDisplay);
// for non Sessions type, we predict {PREDICT_SESSIONS}-times per session batch
if (simSettings.SimType != SimulationType.Sessions)
{
int predictTimes = PREDICT_SESSIONS;
if (simSettings.SimType == SimulationType.Score && predictTimes < RPOCSimpleSimSettings.NUM_SCORE_HITS)
{
predictTimes = RPOCSimpleSimSettings.NUM_SCORE_HITS;
}
retVal = Optimize(predictTimes, false, simSettings.EnableSimDisplay);
}
} while ((simSettings.SimType == SimulationType.Time && simSettings.EndsOn > DateTime.Now) ||
(simSettings.SimType == SimulationType.Score && RPOCSimpleSimSettings.NUM_SCORE_HITS > numScoreHits));
// we do a final prediction and to ensure we update the plangoram display for the final output
retVal = Optimize(1, false, true);
network.TrainingDone();
IsTrainingDone = true;
UpdateStatus?.Invoke("Processing data...", true);
return(retVal);
}
public void UpdateTensorflowResults(PlanogramOptResults results, bool enableSimDisplay = false)
{
if (tokenSource.IsCancellationRequested)
{
return;
}
if (enableSimDisplay)
{
Task.Delay(1);
}
Dispatcher.Invoke(() =>
{
string scoreText = (simSettings.SimType == SimulationType.Score) ? $"{results.Score.ToString("#,##0.##")} ({results.NumScoreHits})" : results.Score.ToString("#,##0.##");
scoreTxtTensor.Text = scoreText;
sessionRunTxtTensor.Text = results.CurrentSession.ToString();
timElapseTxtTensor.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);
minScoreTxtTensor.Text = results.MinScore.ToString("#,##0.##");
maxScoreTxtTensor.Text = results.MaxScore.ToString("#,##0.##");
//engineTxtTensor.Text = "Tensorflow";
averageScoreTxtTensor.Text = results.AvgScore.ToString("#,##0.##");
averageScoreOf10TxtTensor.Text = results.AvgLastTen.ToString("#,##0.##");
if (enableSimDisplay)
{
currentResultsTensor = results;
FillGrid(planogramTensorflow, results.Shelves, false, ItemAttrbutes_MouseEnter_Tensorflow, ItemAttrbutes_MouseLeave_Tensorflow, isRLM: false);
}
});
}
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;
}
});
}
public double CalculateScore(IMLMethod network)
{
SessionNumber++;
DateTime startSession = DateTime.Now;
PlanogramSimulation pilot = new PlanogramSimulation((BasicNetwork)network, Items, SimSettings);
var result = LastResult = pilot.ScorePilot();
result.CurrentSession = SessionNumber;
metricScoreHistory.Add(result.Score);
metricAvgLastTen.Enqueue(result.Score);
if (metricAvgLastTen.Count > 10)
{
metricAvgLastTen.Dequeue();
}
result.MaxScore = metricScoreHistory.Max();
result.MinScore = metricScoreHistory.Min();
result.AvgScore = metricScoreHistory.Average();
result.AvgLastTen = metricAvgLastTen.Average();
if (SimSettings.Score.HasValue)
{
if (result.Score >= SimSettings.Score.Value)
{
NumScoreHits++;
}
else
{
NumScoreHits = 0;
}
}
result.NumScoreHits = NumScoreHits;
if (Logger != null)
{
SimulationData logdata = new SimulationData();
logdata.Session = result.CurrentSession;
logdata.Score = result.Score;
logdata.Elapse = DateTime.Now - startSession;
Logger.Add(logdata, false);
}
UpdateUI?.Invoke(result, SimSettings.EnableSimDisplay);
return(result.Score);
}
public PlanogramOptResults ScorePilot()
{
PlanogramOptResults retVal = new PlanogramOptResults();
var shelves = new List <Shelf>();
double totalMetricScore = 0;
int slotNumber = 0;
Dictionary <int, int> itemDict = new Dictionary <int, int>();
bool hasExceedMax = false;
for (int i = 0; i < simSettings.NumShelves; i++)
{
Shelf shelfInstance = new Shelf();
for (int p = 0; p < simSettings.NumSlots; p++)
{
var inputs = new BasicMLData(1);
inputs[0] = slotNormalizer.Normalize(slotNumber);
IMLData output = network.Compute(inputs);
int index = Convert.ToInt32(itemNormalizer.DeNormalize(output[0]));
Item itemReference = items[index];
if (!hasExceedMax)
{
hasExceedMax = !(CheckDuplicateItem(itemDict, index));
}
// 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;
shelfInstance.Add(itemReference, itemMetricScore);
slotNumber++;
}
shelves.Add(shelfInstance);
}
retVal.Shelves = shelves;
retVal.Score = (hasExceedMax) ? 0 : totalMetricScore;
retVal.TimeElapsed = DateTime.Now - simSettings.StartedOn;
return(retVal);
}
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;
}
});
}
