regparameters.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/**
 * Title: XELOPES Data Mining Library
 * Description: The XELOPES library is an open platform-independent and data-source-independent library for Embedded Data Mining.
 * Copyright: Copyright (c) 2002 Prudential Systems Software GmbH
 * Company: ZSoft (www.zsoft.ru), Prudsys (www.prudsys.com)
 * @author Michael Thess
 * @author Victor Borichev
 * @author Valentine Stepanenko ([e-mail])
 * @version 1.0
 */

package com.prudsys.pdm.Models.Regression.SVM.Algorithms.RegularizationNetworks;

/**
 * Parameters of regularization network.
 *
 */
class RegParameters {

        // Regularization network type:
        public static final int C_SVC          = 0;
        public static final int NU_SVC         = 1;
        public static final int ONE_CLASS      = 2;
        public static final int EPSILON_SVR    = 3;
        public static final int NU_SVR         = 4;
        public static final int SPARSE_GRIDS   = 5;

        // Kernel type:
        public static final int LINEAR         = 0;
        public static final int POLY           = 1;
        public static final int RBF            = 2;
        public static final int SIGMOID        = 3;

        // Grid type (only for sparse grids):
        public static final int TENSOR_PRODUCT = 0;
        public static final int SIMPLICIAL     = 1;

        // Types:
        public int reg_type;        // type of regularization network
        public int kernel_type;     // kernel type
        public int grid_type;       // grid type of sparse grids

        // Parameters of the approximation functions:
        public double degree;	    // for poly
        public double gamma;	    // for poly/rbf/sigmoid
        public double coef0;	    // for poly/sigmoid
        public int    level;        // level of sparse grids

        // These parameters are for training only:
        public double cache_size;   // in MB
        public double eps;	    // stopping criteria
        public double C;	    // for C_SVC, EPSILON_SVR, NU_SVR, SPARSE GRIDS
        public int nr_weight;       // for C_SVC
        public int[] weight_label;  // for C_SVC
        public double[] weight;	    // for C_SVC
        public double nu;	    // for NU_SVC, ONE_CLASS, and NU_SVR
        public double p;	    // for EPSILON_SVR
        public int shrinking;	    // use the shrinking heuristics

  /**
   * Constructor sets parameters to default values
   * using the operation resetParameters().
   */
  public RegParameters() {

    resetParameters();
  }

  /**
   * Resets the parameters to the default values.
   */
  public void resetParameters() {

    // Default values:
    reg_type     = C_SVC;
    kernel_type  = RBF;
    grid_type    = SIMPLICIAL;
    degree       = 3;
    gamma        = 0;	// 1/k
    coef0        = 0;
    level        = 0;
    nu           = 0.5;
    cache_size   = 40;
    C            = 1;
    eps          = 1e-3;
    p            = 0.1;
    shrinking    = 1;
    nr_weight    = 0;
    weight_label = new int[0];  //0
    weight       = new double[0]; //0
  }
}