/// <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($"\nElapsed: {watch.Elapsed}");
Console.WriteLine("\nThe total number of times it tried the Logistic Simulation for training: " + pilotScore.SessionCnt);
Console.ReadLine();
}
public double CalculateScore(IMLMethod network)
{
int cnt = Interlocked.Increment(ref sessionCnt);
EncogLogisticSimulator sim = new EncogLogisticSimulator((BasicNetwork)network, false);
var score = sim.CalculateScore(CustomerOrders, false);
Console.WriteLine($"Session #{cnt} \t Score: {Math.Abs(score).ToString("$#,##0")}");
return(score);
}