public override void Train(List <T> records)
{
HashSet <string> class_labels = new HashSet <string>();
foreach (T rec in records)
{
class_labels.Add(rec.Label);
}
mClassFieldLabels = class_labels.ToList();
mTheta.Clear();
mKernelCentroids.Clear();
int m = records.Count;
if (m == 0)
{
throw new ArgumentException("sample count==0");
}
int dimension = records[0].Dimension;
int[] Y = new int[m];
for (int i = 0; i < m; ++i)
{
T rec = records[i];
mKernelCentroids.Add((double[])rec.Data.Clone());
}
double[] theta_0 = new double[m + 1];
foreach (string class_label in mClassFieldLabels)
{
for (int i = 0; i < m; ++i)
{
T rec = records[i];
Y[i] = rec.Label == class_label ? 1 : 0;
}
for (int d = 0; d < dimension; ++d)
{
theta_0[d] = 0;
}
KernelSVMCostFunction <T> f = new KernelSVMCostFunction <T>(records, Y, m, mKernel);
f.C = mC;
ContinuousSolution solution = mLocalSearcher.Minimize(theta_0, f, mMaxSolverIteration);
mTheta[class_label] = solution.Values;
}
}
/// <summary>
/// Method used to find the error in terms of SVM cost function
/// </summary>
/// <param name="data_set"></param>
/// <returns></returns>
public override double ComputeCost(List <T> data_set)
{
int sample_count = data_set.Count;
int[] Y = new int[sample_count];
double total_error = 0;
foreach (string class_label in mClassFieldLabels)
{
for (int i = 0; i < sample_count; ++i)
{
T rec = data_set[i];
Y[i] = rec.Label == class_label ? 1 : 0;
}
KernelSVMCostFunction <T> f = new KernelSVMCostFunction <T>(data_set, Y, sample_count, mKernel);
f.C = mC;
double error = f.Evaluate(mTheta[class_label]);
total_error += error;
}
return(total_error);
}
