示例#1
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 /// <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)
 {
 }
示例#2
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 /// <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)
 {
 }
示例#3
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 /// <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)
 {
 }