/// <summary>
/// Instantiates a new instance of the plangoram optimizer
/// </summary>
/// <param name="items">The dataset (items with their attributes and metrics) for the RLM to learn from</param>
/// <param name="simSettings">Holds data which dictates what type of simulation to run and for how long. Also holds the weights metrics and other general settings</param>
/// <param name="updateUI">Callback function for sending the results of the optimization for each session</param>
/// <param name="updateStatus">Callback function for sending the current status of the RLM</param>
/// <param name="logger">Logs the per session stats and allows users to download the CSV file after the training</param>
/// <remarks>Used a callback instead of an event because we worry that the display might not keep up with the optimization. You can disable the display by setting it in the Simulation panel</remarks>
public PlanogramOptimizer(Item[] items, RPOCSimulationSettings simSettings, UpdateUICallback updateUI = null, UpdateStatusCallback updateStatus = null, SimulationCsvLogger logger = null, string dbIdentifier = null)
{
this.logger = logger;
this.items = items.ToArray();
this.simSettings = simSettings;
UpdateUI = updateUI;
UpdateStatus = updateStatus;
if (ENABLE_RLM_OUTPUT_LIMITER)
{
currentItemIndexes = new List <int>();
}
UpdateStatus?.Invoke("Initializing...");
// creates the network (and the underlying DB) with a unique name to have a different network everytime you run a simulation
//IRlmDbData rlmDbData = new RlmDbDataPostgreSqlServer(dbIdentifier != null ? dbIdentifier : "RLM_planogram_" + Guid.NewGuid().ToString("N"));
IRlmDbData rlmDbData = new RlmDbDataSQLServer(dbIdentifier != null ? dbIdentifier : "RLM_planogram_" + Guid.NewGuid().ToString("N"));
network = new RlmNetwork(rlmDbData);
network.DataPersistenceComplete += Network_DataPersistenceComplete;
// checks if the network structure already exists
// if not then we proceed to define the inputs and outputs
inputType = RLM.Enums.RlmInputType.Distinct;
if (!network.LoadNetwork("planogram"))
{
string int32Type = typeof(Int32).ToString();
var inputs = new List <RlmIO>();
//inputs.Add(new RlmIO() { Name = "Shelf", DotNetType = int32Type, Min = 1, Max = simSettings.NumShelves, Type = RLM.Enums.RlmInputType.Linear });
inputs.Add(new RlmIO()
{
Name = "Slot", DotNetType = int32Type, Min = 1, Max = simSettings.NumSlots * simSettings.NumShelves, Type = inputType
});
var outputs = new List <RlmIO>();
outputs.Add(new RlmIO()
{
Name = "Item", DotNetType = int32Type, Min = 0, Max = this.items.Length - 1
});
// change Max to any number above 1 (and must not be go beyond the NumSlots value) to have multiple facings
//outputs.Add(new RlmIO() { Name = "NumFacings", DotNetType = int32Type, Min = 1, Max = 1 });
// creates the network
network.NewNetwork("planogram", inputs, outputs);
}
}
// 10-24 set anneal to default false
public PlanogramOptimizerEncog(Item[] items, RPOCSimulationSettings simSettings, UpdateUINonRLMCallback updateUI = null, UpdateStatusCallback updateStatus = null, SimulationCsvLogger logger = null, bool anneal = false)
{
updateStatus?.Invoke("Initializing...");
//this.items = items;
this.simSettings = simSettings;
this.logger = logger;
this.updateUI = updateUI;
this.updateStatus = updateStatus;
this.signal = new AutoResetEvent(false);
network = CreateNetwork();
planogramScore = new PlanogramScore()
{
SimSettings = simSettings,
Items = items,
UpdateUI = updateUI,
Logger = logger
};
if (anneal)
{
train = new NeuralSimulatedAnnealing(network, planogramScore, 18, 2, 50);
//train = new NeuralSimulatedAnnealing(network, planogramScore, 10, 2, (simSettings.SimType == SimulationType.Sessions) ? 1 : 10); // todo make the # of cycles an input for users?
}
else
{
train = new MLMethodGeneticAlgorithm(() => {
((IMLResettable)network).Reset();
return(network);
}, planogramScore, POPULATION_SIZE);
}
planogramScore.StopEncogTraningEvent += () =>
{
signal.Set();
};
}