/// <summary>
/// This is where encog network settings are configured and training is being processed.
/// </summary>
public static void LogisticTrain()
{
Console.WriteLine("\n\nEncog network structure: \nNumber of Input neurons 1 \nNumber of output neurons 9 ");
int hiddenLayers = Util.GetInput("Number of hidden layers [default 1]: ", 1);
int hiddenLayerNeurons = Util.GetInput("Hidden layer neurons [default 100]: ", 100);
int type = Util.GetInput("\nSelect a training method [Annealing - 0][Genetic - 1 default]:", 1);
int numOfEpoch = Util.GetInput("Number of Epochs [default 10]:", 10);
BasicNetwork network = CreateNetwork(hiddenLayers, hiddenLayerNeurons);
var pilotScore = new EncogLogisticScore();
IMLTrain train;
if (type == 0)
{
int startTemp = Util.GetInput("Start Temperature [default 10]:", 10);
int endTemp = Util.GetInput("End Temperature [default 2]:", 2);
int cycles = Util.GetInput("Cycles [default 10]:", 10);
train = new NeuralSimulatedAnnealing(network, pilotScore, endTemp, startTemp, cycles);
}
else
{
int populationSize = Util.GetInput("Population Size [default 10]:", 10);
train = new MLMethodGeneticAlgorithm(() => {
BasicNetwork result = CreateNetwork(hiddenLayers, hiddenLayerNeurons);
((IMLResettable)result).Reset();
return(result);
}, pilotScore, populationSize); // population size
}
Stopwatch watch = new Stopwatch();
watch.Start();
Console.WriteLine("\n\nTraining: \n");
for (int i = 0; i < numOfEpoch; i++) // num of epochs
{
train.Iteration();
double totalCosts = train.Error;
string currencyScore = totalCosts.ToString("$#,##0");
Console.WriteLine($"Epoch # {i} \t Score: {currencyScore,10}");
}
watch.Stop();
Console.WriteLine("\nPredicted outputs:");
network = (BasicNetwork)train.Method;
var pilot = new EncogLogisticSimulator(network, true);
pilot.CalculateScore(LogisticSimulator.GenerateCustomerOrders(), true);
Console.WriteLine($"\nElapsed: {watch.Elapsed}");
Console.WriteLine("\nThe total number of times it tried the Logistic Simulation for training: " + pilotScore.SessionCnt);
Console.ReadLine();
}
public EncogLogisticsPilot(BasicNetwork network, LogisticSimulationMetadata_Encog metadata, bool turnOffMqtt = true)
{
_network = network;
sim = new LogisticSimulator(
metadata.StorageCostPerDay,
metadata.BacklogCostPerDay,
metadata.RetailerInitialInventory,
metadata.DistributorInitialInventory,
metadata.WholesalerInitialInventory,
metadata.FactoryInitialInventory);
}
public void LogisticTrain()
{
Console.WriteLine("\nRLM network settings:");
int sessions = Util.GetInput("\nEnter Number of Session [default 100]: ", 100); //Gets user input for the number of tries the game will play
int startRand = Util.GetInput("Enter Start Randomness [default 100]: ", 100); //Gets user input for start randomness
int endRand = Util.GetInput("Enter End Randomness [default 0]: ", 0); //Gets user input for end randomness
var dbName = $"RLM_logistic_" + Guid.NewGuid().ToString("N");
var networkName = "Logicstics Network";
LogisticSimulator simulator = null;
IEnumerable <int> customerOrders = LogisticInitialValues.CustomerOrders;
try
{
//IRlmDbData rlmDbData = new RlmDbDataPostgreSqlServer(dbName);
IRlmDbData rlmDbData = new RlmDbDataSQLServer(dbName);
RlmNetwork network = new RlmNetwork(rlmDbData); //Make an instance of rlm_network passing the database name as parameter
network.DataPersistenceComplete += Network_DataPersistenceComplete;
network.DataPersistenceProgress += Network_DataPersistenceProgress;
if (!network.LoadNetwork(networkName))
{
var inputs = new List <RlmIO>()
{
new RlmIO("X", typeof(Int32).ToString(), 1, 1, RlmInputType.Distinct),
};
double minFrom = LogisticInitialValues.PlayerMinRange[0];
double minTo = LogisticInitialValues.PlayerMinRange[1];
double maxFrom = LogisticInitialValues.PlayerMaxRange[0];
double maxTo = LogisticInitialValues.PlayerMaxRange[1];
var outputs = new List <RlmIO>()
{
new RlmIO("Retailer_Min", typeof(Int16).ToString(), minFrom, minTo),
new RlmIO("Retailer_Max", typeof(Int16).ToString(), maxFrom, maxTo),
new RlmIO("WholeSaler_Min", typeof(Int16).ToString(), minFrom, minTo),
new RlmIO("WholeSaler_Max", typeof(Int16).ToString(), maxFrom, maxTo),
new RlmIO("Distributor_Min", typeof(Int16).ToString(), minFrom, minTo),
new RlmIO("Distributor_Max", typeof(Int16).ToString(), maxFrom, maxTo),
new RlmIO("Factory_Min", typeof(Int16).ToString(), minFrom, minTo),
new RlmIO("Factory_Max", typeof(Int16).ToString(), maxFrom, maxTo),
new RlmIO("Factory_Units_Per_Day", typeof(Int16).ToString(), LogisticInitialValues.FactoryRange[0], LogisticInitialValues.FactoryRange[1]),
};
network.NewNetwork(networkName, inputs, outputs);
}
// execute it on another thread as not to block the RLM training
Console.WriteLine("\nPress 'd' to show Data persistence progress\n");
Task.Run(() =>
{
while (!Console.KeyAvailable && Console.ReadKey(true).Key == ConsoleKey.D)
{
showDataPersistProgress = true;
}
});
network.NumSessions = sessions; // num of sessioins default 100
network.StartRandomness = startRand;
network.EndRandomness = endRand;
simulator = new LogisticSimulator(LogisticInitialValues.StorageCost, LogisticInitialValues.BacklogCost, LogisticInitialValues.InitialInventory, LogisticInitialValues.InitialInventory, LogisticInitialValues.InitialInventory, LogisticInitialValues.InitialInventory);
Stopwatch watch = new Stopwatch();
watch.Start();
Console.WriteLine("\n\nTraining:\n");
IEnumerable <LogisticSimulatorOutput> predictedLogisticOutputs = null;
network.ResetRandomizationCounter();
for (int i = 0; i < sessions; i++)
{
var sessId = network.SessionStart();
var inputs = new List <RlmIOWithValue>();
inputs.Add(new RlmIOWithValue(network.Inputs.First(), "1"));
var cycle = new RlmCycle();
var outputs = cycle.RunCycle(network, sessId, inputs, true);
var simOutputs = outputs.CycleOutput.Outputs
.Select(a => new LogisticSimulatorOutput()
{
Name = a.Name, Value = Convert.ToInt32(a.Value)
})
.ToList();
simulator.ResetSimulationOutput();
simulator.Start(simOutputs, 50, customerOrders);
network.ScoreCycle(outputs.CycleOutput.CycleID, 0);
var totalCosts = simulator.SumAllCosts();
network.SessionEnd(totalCosts);
Console.WriteLine($"Session #{i + 1} \t Score: {Math.Abs(totalCosts).ToString("$#,##0"),10}");
if (i == sessions - 1)
{
predictedLogisticOutputs = simOutputs;
}
}
watch.Stop();
Console.WriteLine("\nPredicted outputs:");
string resultText = "";
foreach (var item in predictedLogisticOutputs)
{
resultText += "\n" + item.Name + ": " + item.Value;
}
Console.WriteLine(resultText);
Console.WriteLine($"\nElapsed: {watch.Elapsed}");
network.TrainingDone();
}
catch (Exception e)
{
if (e.InnerException != null && e.InnerException is RlmDefaultConnectionStringException)
{
Console.WriteLine($"Error: {e.InnerException.Message}");
}
else
{
Console.WriteLine($"ERROR: {e.Message}");
}
}
Console.ReadLine();
}
public int GetScore(LogisticSimulator sim)
{
var score = sim.SumAllCosts() * -1;
return(Convert.ToInt32(20000 - score));
}
