/// <summary>
/// Read and scale inputs, create and train the Epsilon_SVR
/// </summary>
/// <param name="input_file_name">Training file path</param>
/// <param name="kernel">Selected Kernel</param>
/// <param name="probability">Specify if probability are needed</param>
/// <param name="cache_size">Indicates the maximum memory that can use the program</param>
public Epsilon_SVR(string input_file_name, Kernel kernel, double C, double epsilon, bool probability = true, double cache_size = 100)
: this(ProblemHelper.ReadAndScaleProblem(input_file_name), kernel, C, epsilon, probability, cache_size)
{
}
/// <summary>
/// Classification SVM
/// Supports multi-class classification
/// </summary>
/// <param name="input_file_name">Path to the training data set file. Has respect the libsvm format</param>
/// <param name="kernel">Selected Kernel</param>
/// <param name="C">Cost parameter </param>
/// <param name="cache_size">Indicates the maximum memory that can use the program</param>
public C_SVC(string input_file_name, Kernel kernel, double C, double cache_size = 100)
: this(ProblemHelper.ReadProblem(input_file_name), kernel, C, cache_size)
{
}
/// <summary>
/// Default SVM
/// </summary>
/// <remarks>The class store svm parameters and create the model.
/// This way, you can use it to predict</remarks>
public SVM(string input_file_name, svm_parameter param)
: this(ProblemHelper.ReadProblem(input_file_name), param)
{
}