public static void Main(string[] args)
{
Problem train = Problem.Read("a1a.train.txt");
Problem test = Problem.Read("a1a.test.txt");
//For this example (and indeed, many scenarios), the default
//parameters will suffice.
Parameter parameters = new Parameter();
double C;
double Gamma;
//This will do a grid optimization to find the best parameters
//and store them in C and Gamma, outputting the entire
//search to params.txt.
ParameterSelection.Grid(train, parameters, "params.txt", out C, out Gamma);
parameters.C = C;
parameters.Gamma = Gamma;
//Train the model using the optimal parameters.
Model model = Training.Train(train, parameters);
//Perform classification on the test data, putting the
//results in results.txt.
Prediction.Predict(test, "results.txt", model, false);
}
/// <summary>
/// Move all fireflies toward brighter ones
/// </summary>
public void ffa_move(double[] Lightn, Object[] fireflies0, double[] Lighto, double alpha, double gamma, List <Object> fireflies,
Problem prob, Parameter param, List <double> avgAcc, List <int> changedIndex)
{
int nj = fireflies0.Length;
double rC, rG, rF; //rC -> distance for C value, rG-> distance for Gamma value, rF - distance for the feature mask
double beta0;
double beta; // beta -> attrativeness value for C and G, betaF -> attrativeness for the feature mask
//specifying the ranges for C and Gamma
double minC = Math.Pow(2, MIN_C); // minimum value for C
double maxC = Math.Pow(2, MAX_C); // maximum value for C
double minG = Math.Pow(2, MIN_G); // minimum value for G
double maxG = Math.Pow(2, MAX_G); // maximum value for G
int nF = fireflies[0].Attribute_Values.Count(); //nF -> number of features
double[] CBackup = new double[fireflies.Count]; //back up array for C value
double[] GammaBackup = new double[fireflies.Count]; ////back up array for Gamma value
Random rnd = new Random(); Random rx = new Random(); Random ry = new Random();
duplicateValue(fireflies, CBackup, GammaBackup);
for (int i = 0; i < nj; i++)
{
for (int j = 0; j < nj; j++)
{
if (j == i) //avoid comparism with the same element
{
continue;
}
rF = 0.0;
rC = Math.Pow(((double)fireflies[i].cValue - (double)fireflies0[j].cValue), 2);
rG = Math.Pow(((double)fireflies[i].GValue - (double)fireflies0[j].GValue), 2);
double r = Math.Sqrt(rC + rG); //r -> total distance for both C and Gamma
if (Lightn[i] < Lighto[j])
{
beta0 = 1; //setting beta to 1
beta = beta0 * Math.Exp(-gamma * Math.Pow(r, 2)); //The attractiveness parameter for C and Gamma -> beta=exp(-gamma*r)
//changing firefly i position for the continuous values - i.e C and Gamma value respectively
fireflies[i].cValue = ((double)fireflies[i].cValue * (1 - beta)) + (CBackup[j] * beta) + (alpha * (rnd.NextDouble() - 0.5));
fireflies[i].GValue = ((double)fireflies[i].GValue * (1 - beta)) + (GammaBackup[j] * beta) + (alpha * (rnd.NextDouble() - 0.5));
findrange(fireflies[i], minC, maxC, minG, maxG); //restrict the values of C and Gamma to the specified range
}
}
if ((double)fireflies[i].cValue != CBackup[i] || (double)fireflies[i].GValue != GammaBackup[i])
{
changedIndex.Add(i); //saving the index of the firefly that has been moved for the purpose of accuracy calculation
}
}
//calculate the new accuracy for the newly updated C and Gamma value
ParameterSelection.Grid(prob, param, fireflies, changedIndex, avgAcc, CBackup, GammaBackup, NFOLD);
}
