/// <summary>
/// Performs a Grid parameter selection, trying all possible combinations of the two lists and returning the
/// combination which performed best. Use this method if validation data isn't available, as it will
/// divide the training data and train on a portion of it and test on the rest.
/// </summary>
/// <param name="problem">The training data</param>
/// <param name="parameters">The parameters to use when optimizing</param>
/// <param name="CValues">The set of C values to use</param>
/// <param name="GammaValues">The set of Gamma values to use</param>
/// <param name="outputFile">Output file for the parameter results.</param>
/// <param name="nrfold">The number of times the data should be divided for validation</param>
/// <param name="C">The optimal C value will be placed in this variable</param>
/// <param name="Gamma">The optimal Gamma value will be placed in this variable</param>
public static void Grid(
Problem problem,
Parameter parameters,
List <double> CValues,
List <double> GammaValues,
string outputFile,
int nrfold,
out double C,
out double Gamma)
{
C = 0;
Gamma = 0;
List <double> avgAcc = new List <double>(); //avgAcc->average accuracy; it stores the average accuracies for all the C and Gamma values
List <double> CVal = new List <double>(); //store the C values with high accuracies
List <double> GVal = new List <double>(); //store the C values with high accuracies
double crossValidation = double.MinValue;
StreamWriter output = null;
List <double> nCVal = new List <double>(); //nCVal -> randomly selected new C Values
List <double> nGVal = new List <double>(); //nGVal -> randomly selected new Gamma Values
List <double> nAvgAcc = new List <double>(); //nAvgAcc -> randomly selected new average accuracies
string outputValues = ""; // outputValues -> hold the C,Gamma and accuracy for each iteration. This is for to derive a pattern and formula
//List<string> outputValues = new List<string>(); // outputValues -> hold the C,Gamma and accuracy for each iteration. This is for to derive a pattern and formula
ParameterSelection ps = new ParameterSelection();
FireFly ff = new FireFly();
int nFF = CValues.Count * GammaValues.Count;//nFF -> number of fireflies
Random r = new Random();
int nValues = CValues.Count - 1;
//int nValues = CValues.Count;
if (outputFile != null)
{
output = new StreamWriter(outputFile);
}
//****Firefly Optimized SVM
//for (int i = 0; i < GammaValues.Count; i++)
//{
// parameters.C = CValues[nValues--];
// parameters.Gamma = GammaValues[i];
// double test = Training.PerformCrossValidation(problem, parameters, nrfold);
// //avgAcc.Add(test);
// //CVal.Add(parameters.C);
// //GVal.Add(parameters.Gamma);
// Console.Write("{0} {1} {2}", parameters.C, parameters.Gamma, test);
// outputValues = parameters.C.ToString() + " " + parameters.Gamma.ToString() + " " + test.ToString();
// File.AppendAllText(ps.filePath, outputValues);
// File.AppendAllText(ps.filePath, Environment.NewLine);
// if (output != null)
// output.WriteLine("{0} {1} {2}", parameters.C, parameters.Gamma, test);
// if (test > crossValidation)
// {
// C = parameters.C;
// Gamma = parameters.Gamma;
// crossValidation = test;
// Console.WriteLine(" New Maximum!");
// //break from loop if the cross validation rate is equal to 1 (i.e. 100%)
// /*if (crossValidation == 1.0)
// {
// proceed = true;
// break;
// }*/
// }
// else
// Console.WriteLine();
//}
//Object selectedFirefly = ff.firefly_simple(avgAcc, CVal, GVal, problem, parameters);
//C = (double)selectedFirefly.cValue;
//Gamma = (double)selectedFirefly.GValue;
//Standard SVM Optimization
//for (int i = 0; i < CValues.Count; i++)
// for (int j = 0; j < GammaValues.Count; j++)
// {
// parameters.C = CValues[i];
// parameters.Gamma = GammaValues[j];
// double test = Training.PerformCrossValidation(problem, parameters, nrfold);
// Console.Write("{0} {1} {2}", parameters.C, parameters.Gamma, test);
// if (output != null)
// output.WriteLine("{0} {1} {2}", parameters.C, parameters.Gamma, test);
// if (test > crossValidation)
// {
// C = parameters.C;
// Gamma = parameters.Gamma;
// crossValidation = test;
// Console.WriteLine(" New Maximum!");
// }
// else Console.WriteLine();
// }
for (int i = 0; i < CValues.Count; i++)
{
double test = new double();
for (int j = 0; j < GammaValues.Count; j++)
{
parameters.C = CValues[i];
parameters.Gamma = GammaValues[j];
test = Training.PerformCrossValidation(problem, parameters, nrfold);
File.AppendAllText(ps.filePath2, Environment.NewLine); //insert double line to file at the end of each parameter evaluation
File.AppendAllText(ps.filePath2, Environment.NewLine); //insert double line to file at the end of each parameter evaluation
Console.Write("{0} {1} {2}", parameters.C, parameters.Gamma, test);
outputValues = parameters.C.ToString() + " " + parameters.Gamma.ToString() + " " + test.ToString();
File.AppendAllText(ps.filePath, outputValues);
File.AppendAllText(ps.filePath, Environment.NewLine);
if (output != null)
{
output.WriteLine("{0} {1} {2}", parameters.C, parameters.Gamma, test);
}
if (test > crossValidation)
{
C = parameters.C;
Gamma = parameters.Gamma;
crossValidation = test;
Console.WriteLine(" New Maximum!");
if (test == 1)
{
CVAccuracy = test;
break;
}
else
{
CVAccuracy = test;
}
//outputValues.Add(C.ToString()); outputValues.Add(Gamma.ToString()); outputValues.Add(test.ToString());
//outputValues = C.ToString() + " " + Gamma.ToString() + " " + test.ToString();
//File.AppendAllText(ps.filePath, outputValues);
//File.AppendAllText(ps.filePath, Environment.NewLine);
}
else
{
Console.WriteLine();
}
}
if (test == 1)
{
CVAccuracy = test;
break;
}
else
{
CVAccuracy = test;
}
}
//File.AppendAllText(ps.filePath, Environment.NewLine);
if (output != null)
{
output.Close();
}
}
