Package de.bwaldvogel.liblinear

Class Linear

  • public class Linear
extends java.lang.Object

    Java port of liblinear

    The usage should be pretty similar to the C version of liblinear.

    Please consider reading the README file of liblinear.

    The port was done by Benedikt Waldvogel (mail at bwaldvogel.de)

    Version:
    2.44

    • Field Detail

      • FILE_CHARSET

        static final java.nio.charset.Charset FILE_CHARSET

      • DEFAULT_LOCALE

        private static final java.util.Locale DEFAULT_LOCALE

      • OUTPUT_MUTEX

        private static final java.lang.Object OUTPUT_MUTEX

      • DEBUG_OUTPUT

        private static java.io.PrintStream DEBUG_OUTPUT

    • Constructor Detail

      • Linear

        public Linear()

    • Method Detail

      • crossValidation

        public static void crossValidation​(Problem prob,
                                   Parameter param,
                                   int nr_fold,
                                   double[] target)
        Parameters:
        target - predicted classes

      • info

        static void info​(java.lang.String message)

      • info

        static void info​(java.lang.String format,
                 java.lang.Object... args)

      • atof

        static double atof​(java.lang.String s)
        Parameters:
        s - the string to parse for the double value
        Throws:
        java.lang.IllegalArgumentException - if s is empty or represents NaN or Infinity
        java.lang.NumberFormatException - see Double.parseDouble(String)

      • atoi

        static int atoi​(java.lang.String s)
         throws java.lang.NumberFormatException
        Parameters:
        s - the string to parse for the integer value
        Throws:
        java.lang.IllegalArgumentException - if s is empty
        java.lang.NumberFormatException - see Integer.parseInt(String)

      • loadModel

        public static Model loadModel​(java.io.Reader inputReader)
                       throws java.io.IOException
        Loads the model from inputReader. It uses Locale.ENGLISH for number formatting.

        Note: The inputReader is NOT closed after reading or in case of an exception.

        Throws:
        java.io.IOException

      • loadModel

        public static Model loadModel​(java.io.File modelFile)
                       throws java.io.IOException
        Deprecated.
        use loadModel(Path) instead
        Loads the model from the file with ISO-8859-1 charset. It uses Locale.ENGLISH for number formatting.
        Throws:
        java.io.IOException

      • loadModel

        public static Model loadModel​(java.nio.file.Path modelPath)
                       throws java.io.IOException
        Loads the model from the file with ISO-8859-1 charset. It uses Locale.ENGLISH for number formatting.
        Throws:
        java.io.IOException

      • predict

        public static double predict​(Model model,
                             Feature[] x)

      • predictProbability

        public static double predictProbability​(Model model,
                                        Feature[] x,
                                        double[] prob_estimates)
                                 throws java.lang.IllegalArgumentException
        Throws:
        java.lang.IllegalArgumentException - if model is not probabilistic (see Model.isProbabilityModel())

      • predictValues

        public static double predictValues​(Model model,
                                   Feature[] x,
                                   double[] dec_values)

      • printf

        static void printf​(java.util.Formatter formatter,
                   java.lang.String format,
                   java.lang.Object... args)
            throws java.io.IOException
        Throws:
        java.io.IOException

      • saveModel

        public static void saveModel​(java.io.Writer modelOutput,
                             Model model)
                      throws java.io.IOException
        Writes the model to the modelOutput. It uses Locale.ENGLISH for number formatting.

        Note: The modelOutput is closed after reading or in case of an exception.

        Throws:
        java.io.IOException

      • saveModel

        public static void saveModel​(java.io.File modelFile,
                             Model model)
                      throws java.io.IOException
        Deprecated.
        use saveModel(Path, Model) instead
        Writes the model to the file with ISO-8859-1 charset. It uses Locale.ENGLISH for number formatting.
        Throws:
        java.io.IOException

      • saveModel

        public static void saveModel​(java.nio.file.Path modelPath,
                             Model model)
                      throws java.io.IOException
        Writes the model to the file with ISO-8859-1 charset. It uses Locale.ENGLISH for number formatting.
        Throws:
        java.io.IOException

      • GETI

        private static int GETI​(byte[] y,
                        int i)

      • solve_l2r_l1l2_svc

        private static int solve_l2r_l1l2_svc​(Problem prob,
                                      Parameter param,
                                      double[] w,
                                      double Cp,
                                      double Cn,
                                      int max_iter)
        A coordinate descent algorithm for L1-loss and L2-loss SVM dual problems 
          min_\alpha  0.5(\alpha^T (Q + D)\alpha) - e^T \alpha,
    s.t.      0 <= \alpha_i <= upper_bound_i,

  where Qij = yi yj xi^T xj and
  D is a diagonal matrix

 In L1-SVM case:
              upper_bound_i = Cp if y_i = 1
              upper_bound_i = Cn if y_i = -1
              D_ii = 0
 In L2-SVM case:
              upper_bound_i = INF
              D_ii = 1/(2*Cp) if y_i = 1
              D_ii = 1/(2*Cn) if y_i = -1

 Given:
 x, y, Cp, Cn
 eps is the stopping tolerance

 solution will be put in w

 this function returns the number of iterations

 See Algorithm 3 of Hsieh et al., ICML 2008

      • GETI_SVR

        private static int GETI_SVR​(int i)

      • solve_l2r_l1l2_svr

        private static int solve_l2r_l1l2_svr​(Problem prob,
                                      Parameter param,
                                      double[] w,
                                      int max_iter)
        A coordinate descent algorithm for L1-loss and L2-loss epsilon-SVR dual problem min_\beta 0.5\beta^T (Q + diag(lambda)) \beta - p \sum_{i=1}^l|\beta_i| + \sum_{i=1}^l yi\beta_i, s.t. -upper_bound_i <= \beta_i <= upper_bound_i, where Qij = xi^T xj and D is a diagonal matrix In L1-SVM case: upper_bound_i = C lambda_i = 0 In L2-SVM case: upper_bound_i = INF lambda_i = 1/(2*C) Given: x, y, p, C eps is the stopping tolerance solution will be put in w this function returns the number of iterations See Algorithm 4 of Ho and Lin, 2012

      • solve_l2r_lr_dual

        private static int solve_l2r_lr_dual​(Problem prob,
                                     Parameter param,
                                     double[] w,
                                     double Cp,
                                     double Cn,
                                     int max_iter)
        A coordinate descent algorithm for the dual of L2-regularized logistic regression problems 
          min_\alpha  0.5(\alpha^T Q \alpha) + \sum \alpha_i log (\alpha_i) + (upper_bound_i - \alpha_i) log (upper_bound_i - \alpha_i) ,
     s.t.      0 <= \alpha_i <= upper_bound_i,

  where Qij = yi yj xi^T xj and
  upper_bound_i = Cp if y_i = 1
  upper_bound_i = Cn if y_i = -1

 Given:
 x, y, Cp, Cn
 eps is the stopping tolerance

 solution will be put in w

 this function returns the number of iterations

 See Algorithm 5 of Yu et al., MLJ 2010
        Since:
        1.7

      • solve_l1r_l2_svc

        private static int solve_l1r_l2_svc​(Problem prob_col,
                                    Parameter param,
                                    double[] w,
                                    double Cp,
                                    double Cn,
                                    double eps,
                                    int max_iter)
        A coordinate descent algorithm for L1-regularized L2-loss support vector classification 
          min_w \sum |wj| + C \sum max(0, 1-yi w^T xi)^2,

 Given:
 x, y, Cp, Cn
 eps is the stopping tolerance

 solution will be put in w

 this function returns the number of iterations

 See Yuan et al. (2010) and appendix of LIBLINEAR paper, Fan et al. (2008)

 To not regularize the bias (i.e., regularize_bias = 0), a constant feature = 1
 must have been added to the original data. (see -B and -R option)
        Since:
        1.5

      • solve_l1r_lr

        private static int solve_l1r_lr​(Problem prob_col,
                                Parameter param,
                                double[] w,
                                double Cp,
                                double Cn,
                                double eps,
                                int max_iter)
        A coordinate descent algorithm for L1-regularized logistic regression problems 
          min_w \sum |wj| + C \sum log(1+exp(-yi w^T xi)),

 Given:
 x, y, Cp, Cn
 eps is the stopping tolerance

 solution will be put in w

 this function returns the number of iterations

 See Yuan et al. (2011) and appendix of LIBLINEAR paper, Fan et al. (2008)

 To not regularize the bias (i.e., regularize_bias = 0), a constant feature = 1
 must have been added to the original data. (see -B and -R option)
        Since:
        1.5

      • swap

        static void swap​(double[] array,
                 int idxA,
                 int idxB)

      • swap

        static void swap​(int[] array,
                 int idxA,
                 int idxB)

      • swap

        static void swap​(Feature[] array,
                 int idxA,
                 int idxB)

      • train

        public static Model train​(Problem prob,
                          Parameter param)
        Throws:
        java.lang.IllegalArgumentException - if the feature nodes of prob are not sorted in ascending order

      • checkProblemSize

        private static void checkProblemSize​(int n,
                                     int nr_class)
        verify the size and throw an exception early if the problem is too large

      • train_one

        private static void train_one​(Problem prob,
                              Parameter param,
                              double[] w,
                              double Cp,
                              double Cn)

      • calc_start_C

        private static double calc_start_C​(Problem prob,
                                   Parameter param)

      • calc_max_p

        private static double calc_max_p​(Problem prob)

      • disableDebugOutput

        public static void disableDebugOutput()

      • enableDebugOutput

        public static void enableDebugOutput()

      • setDebugOutput

        public static void setDebugOutput​(java.io.PrintStream debugOutput)

      • getVersion

        public static int getVersion()