private double CalculateFitnessOf(Individual_GA individual, double[][] inputData)
{
// Chỗ này chính là chỗ cần dùng với RBF đây
RadialNetwork rn = new RadialNetwork(m_RadialNetwork);
double fitness = 0.0;
rn.SetWeights(individual.values);
fitness = rn.Accuracy(inputData);
individual.Fitness = fitness;
return(fitness);
}
private void btnStart_Click(object sender, EventArgs e)
{
//Get input from Form
//Get testing rate
if (!StringIsNull(txtTestingRate.Text))
{
testingRate = double.Parse(txtTestingRate.Text);
}
//Get window size
if (!StringIsNull(txtWindowSize.Text))
{
windowSize = int.Parse(txtWindowSize.Text);
}
//Step1: Get inputTrain, idealTrain, inputTest, idealTest
//GetTrainTest(data, out train, out test, testingRate);
//GetInputIdeal(train, out inputTrain, out idealTrain, windowSize);
//GetInputIdeal(train, out inputTest, out idealTest, windowSize);
GetAllData(data, out allData, windowSize);
double[][] trainData = null;
double[][] testData = null;
int seed = 8; // gives a good demo
GetTrainTest(allData, seed, out trainData, out testData, testingRate);
Console.WriteLine("\nCreating a 4-5-3 radial basis function network");
int numInput = windowSize;
int numHidden = 2 * numInput;
int numOutput = 1;
RadialNetwork rn = new RadialNetwork(numInput, numHidden, numOutput);
Console.WriteLine("\nBeginning RBF training\n");
int maxIterations = 50; // max for GA
int maxEvaluations = 1000; // max for NSGAII
//double[] bestWeights = rn.TrainWithPSO(trainData, maxIterations);
//double[] bestWeights = rn.TrainWithGA(trainData, maxIterations);
Population_NSGA bestPopulation = rn.TrainWithNSGAII(trainData, maxEvaluations);
Console.WriteLine("\nEvaluating result RBF classification accuracy on the test data");
//rn.SetWeights(bestWeights);
double acc = rn.Accuracy(testData);
//Set Textbox
SetText(txtTestErr, acc.ToString("F5"));
Console.WriteLine("Complete Program!");
}
