libsvm.java

Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等

/*
 *    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.
 */

/*
 * LibSVM.java
 * Copyright (C) 2005 Yasser EL-Manzalawy (original code)
 * Copyright (C) 2005 University of Waikato, Hamilton, NZ (adapted code)
 * 
 */

package weka.classifiers.functions;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;

import java.lang.reflect.Array;
import java.lang.reflect.Field;
import java.lang.reflect.Method;
import java.util.Enumeration;
import java.util.StringTokenizer;
import java.util.Vector;

/*
 * Modifications by FracPete:
 * - complete overhaul to make it useable in Weka
 * - accesses libsvm classes only via Reflection to make Weka compile without
 *   the libsvm classes
 */

/** 
 <!-- globalinfo-start -->
 * A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier).<br/>
 * LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier.<br/>
 * LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. LibSVM reports many useful statistics about LibSVM classifier (e.g., confusion matrix,precision, recall, ROC score, etc.).<br/>
 * <br/>
 * Yasser EL-Manzalawy (2005). WLSVM. URL http://www.cs.iastate.edu/~yasser/wlsvm/.<br/>
 * <br/>
 * Chih-Chung Chang, Chih-Jen Lin (2001). LIBSVM - A Library for Support Vector Machines. URL http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;misc{EL-Manzalawy2005,
 *    author = {Yasser EL-Manzalawy},
 *    note = {You don't need to include the WLSVM package in the CLASSPATH},
 *    title = {WLSVM},
 *    year = {2005},
 *    URL = {http://www.cs.iastate.edu/~yasser/wlsvm/}
 * }
 * 
 * &#64;misc{Chang2001,
 *    author = {Chih-Chung Chang and Chih-Jen Lin},
 *    note = {The Weka classifier works with version 2.82 of LIBSVM},
 *    title = {LIBSVM - A Library for Support Vector Machines},
 *    year = {2001},
 *    URL = {http://www.csie.ntu.edu.tw/~cjlin/libsvm/}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -S &lt;int&gt;
 *   set type of SVM (default 0)
 *    0 = C-SVC
 *    1 = nu-SVC
 *    2 = one-class SVM
 *    3 = epsilon-SVR
 *    4 = nu-SVR</pre>
 * 
 * <pre> -K &lt;int&gt;
 *   set type of kernel function (default 2)
 *    0 = linear: u'*v
 *    1 = polynomial: (gamma*u'*v + coef0)^degree
 *    2 = radial basis function: exp(-gamma*|u-v|^2)
 *    3 = sigmoid: tanh(gamma*u'*v + coef0)</pre>
 * 
 * <pre> -D &lt;int&gt;
 *   set degree in kernel function (default 3)</pre>
 * 
 * <pre> -G &lt;double&gt;
 *   set gamma in kernel function (default 1/k)</pre>
 * 
 * <pre> -R &lt;double&gt;
 *   set coef0 in kernel function (default 0)</pre>
 * 
 * <pre> -C &lt;double&gt;
 *   set the parameter C of C-SVC, epsilon-SVR, and nu-SVR
 *   (default 1)</pre>
 * 
 * <pre> -N &lt;double&gt;
 *   set the parameter nu of nu-SVC, one-class SVM, and nu-SVR
 *   (default 0.5)</pre>
 * 
 * <pre> -Z
 *   turns on normalization of input data (default off)</pre>
 * 
 * <pre> -P &lt;double&gt;
 *   set the epsilon in loss function of epsilon-SVR (default 0.1)</pre>
 * 
 * <pre> -M &lt;double&gt;
 *   set cache memory size in MB (default 40)</pre>
 * 
 * <pre> -E &lt;double&gt;
 *   set tolerance of termination criterion (default 0.001)</pre>
 * 
 * <pre> -H
 *   turns the shrinking heuristics off (default on)</pre>
 * 
 * <pre> -W &lt;double&gt;
 *   set the parameters C of class i to weight[i]*C, for C-SVC
 *   (default 1)</pre>
 * 
 <!-- options-end -->
 *
 * @author  Yasser EL-Manzalawy
 * @author  FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 1.5 $
 */
public class LibSVM 
  extends Classifier
  implements TechnicalInformationHandler {
  
  /** the svm classname */
  protected final static String CLASS_SVM = "libsvm.svm";
  
  /** the svm_model classname */
  protected final static String CLASS_SVMMODEL = "libsvm.svm_model";
  
  /** the svm_problem classname */
  protected final static String CLASS_SVMPROBLEM = "libsvm.svm_problem";
  
  /** the svm_parameter classname */
  protected final static String CLASS_SVMPARAMETER = "libsvm.svm_parameter";
  
  /** the svm_node classname */
  protected final static String CLASS_SVMNODE = "libsvm.svm_node";
  
  /** serial UID */
  protected static final long serialVersionUID = 14172;
  
  /** LibSVM Model */
  protected Object m_Model;
  
  /** for normalizing the data */
  protected Filter m_Filter = null;
  
  /** normalize input data */
  protected boolean m_Normalize = false;
  
  /** SVM type C-SVC */
  public static final int SVMTYPE_C_SVC = 0;
  /** SVM type nu-SVC */
  public static final int SVMTYPE_NU_SVC = 1;
  /** SVM type one-class SVM */
  public static final int SVMTYPE_ONE_CLASS_SVM = 2;
  /** SVM type epsilon-SVR */
  public static final int SVMTYPE_EPSILON_SVR = 3;
  /** SVM type nu-SVR */
  public static final int SVMTYPE_NU_SVR = 4;
  /** SVM types */
  public static final Tag[] TAGS_SVMTYPE = {
    new Tag(SVMTYPE_C_SVC, "C-SVC"),
    new Tag(SVMTYPE_NU_SVC, "nu-SVC"),
    new Tag(SVMTYPE_ONE_CLASS_SVM, "one-class SVM"),
    new Tag(SVMTYPE_EPSILON_SVR, "epsilon-SVR"),
    new Tag(SVMTYPE_NU_SVR, "nu-SVR")
  };
  
  /** the SVM type */
  protected int m_SVMType = SVMTYPE_C_SVC;
  
  /** kernel type linear: u'*v */
  public static final int KERNELTYPE_LINEAR = 0;
  /** kernel type polynomial: (gamma*u'*v + coef0)^degree */
  public static final int KERNELTYPE_POLYNOMIAL = 1;
  /** kernel type radial basis function: exp(-gamma*|u-v|^2) */
  public static final int KERNELTYPE_RBF = 2;
  /** kernel type sigmoid: tanh(gamma*u'*v + coef0) */
  public static final int KERNELTYPE_SIGMOID = 3;
  /** the different kernel types */
  public static final Tag[] TAGS_KERNELTYPE = {
    new Tag(KERNELTYPE_LINEAR, "linear: u'*v"),
    new Tag(KERNELTYPE_POLYNOMIAL, "polynomial: (gamma*u'*v + coef0)^degree"),
    new Tag(KERNELTYPE_RBF, "radial basis function: exp(-gamma*|u-v|^2)"),
    new Tag(KERNELTYPE_SIGMOID, "sigmoid: tanh(gamma*u'*v + coef0)")
  };
  
  /** the kernel type */
  protected int m_KernelType = KERNELTYPE_RBF;
  
  /** for poly - in older versions of libsvm declared as a double.
   * At least since 2.82 it is an int. */
  protected int m_Degree = 3;
  
  /** for poly/rbf/sigmoid */
  protected double m_Gamma = 0;
  
  /** for poly/rbf/sigmoid (the actual gamma) */
  protected double m_GammaActual = 0;
  
  /** for poly/sigmoid */
  protected double m_Coef0 = 0;
  
  /** in MB */
  protected double m_CacheSize = 40;
  
  /** stopping criteria */
  protected double m_eps = 1e-3;
  
  /** cost, for C_SVC, EPSILON_SVR and NU_SVR */
  protected double m_Cost = 1;
  
  /** for C_SVC */
  protected int[] m_WeightLabel = new int[0];
  
  /** for C_SVC */
  protected double[] m_Weight = new double[0];
  
  /** for NU_SVC, ONE_CLASS, and NU_SVR */
  protected double m_nu = 0.5;
  
  /** loss, for EPSILON_SVR */
  protected double m_Loss = 0.1;
  
  /** use the shrinking heuristics */
  protected boolean m_Shrinking = true;	
  
  /** YASSER: don't predict probabilities */
  protected boolean m_predict_probability = false;
    
  /** whether the libsvm classes are in the Classpath */
  protected static boolean m_Present = false;
  static {
    try {
      Class.forName(CLASS_SVM);
      m_Present = true;
    }
    catch (Exception e) {
      m_Present = false;
    }
  }
  
  /**
   * Returns a string describing classifier
   * 
   * @return a description suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return 
      "A wrapper class for the libsvm tools (the libsvm classes, typically "
    + "the jar file, need to be in the classpath to use this classifier).\n"
    + "LibSVM runs faster than SMO since it uses LibSVM to build the SVM "
    + "classifier.\n"
    + "LibSVM allows users to experiment with One-class SVM, Regressing SVM, "
    + "and nu-SVM supported by LibSVM tool. LibSVM reports many useful "
    + "statistics about LibSVM classifier (e.g., confusion matrix,"
    + "precision, recall, ROC score, etc.).\n"
    + "\n"
    + getTechnicalInformation().toString();
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation 	result;
    TechnicalInformation 	additional;
    
    result = new TechnicalInformation(Type.MISC);
    result.setValue(TechnicalInformation.Field.AUTHOR, "Yasser EL-Manzalawy");
    result.setValue(TechnicalInformation.Field.YEAR, "2005");
    result.setValue(TechnicalInformation.Field.TITLE, "WLSVM");
    result.setValue(TechnicalInformation.Field.NOTE, "LibSVM was originally developed as 'WLSVM'");
    result.setValue(TechnicalInformation.Field.URL, "http://www.cs.iastate.edu/~yasser/wlsvm/");
    result.setValue(TechnicalInformation.Field.NOTE, "You don't need to include the WLSVM package in the CLASSPATH");
    
    additional = result.add(Type.MISC);
    additional.setValue(TechnicalInformation.Field.AUTHOR, "Chih-Chung Chang and Chih-Jen Lin");
    additional.setValue(TechnicalInformation.Field.TITLE, "LIBSVM - A Library for Support Vector Machines");
    additional.setValue(TechnicalInformation.Field.YEAR, "2001");
    additional.setValue(TechnicalInformation.Field.URL, "http://www.csie.ntu.edu.tw/~cjlin/libsvm/");
    additional.setValue(TechnicalInformation.Field.NOTE, "The Weka classifier works with version 2.82 of LIBSVM");
    
    return result;
  }
  
  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    Vector      result;
    
    result = new Vector();
    
    result.addElement(
        new Option(
            "\t set type of SVM (default 0)\n"
            + "\t\t 0 = C-SVC\n" 
            + "\t\t 1 = nu-SVC\n"
            + "\t\t 2 = one-class SVM\n" 
            + "\t\t 3 = epsilon-SVR\n"
            + "\t\t 4 = nu-SVR", 
            "S", 1, "-S <int>"));
    
    result.addElement(
        new Option(
            "\t set type of kernel function (default 2)\n"
            + "\t\t 0 = linear: u'*v\n"
            + "\t\t 1 = polynomial: (gamma*u'*v + coef0)^degree\n"
            + "\t\t 2 = radial basis function: exp(-gamma*|u-v|^2)\n"
            + "\t\t 3 = sigmoid: tanh(gamma*u'*v + coef0)",
            "K", 1, "-K <int>"));
    
    result.addElement(
        new Option(
            "\t set degree in kernel function (default 3)", 
            "D", 1, "-D <int>"));
    
    result.addElement(
        new Option(
            "\t set gamma in kernel function (default 1/k)", 
            "G", 1, "-G <double>"));
    
    result.addElement(
        new Option(
            "\t set coef0 in kernel function (default 0)", 
            "R", 1, "-R <double>"));
    
    result.addElement(
        new Option(
            "\t set the parameter C of C-SVC, epsilon-SVR, and nu-SVR\n"
            + "\t (default 1)",
            "C", 1, "-C <double>"));
    
    result.addElement(
        new Option(
            "\t set the parameter nu of nu-SVC, one-class SVM, and nu-SVR\n"
            + "\t (default 0.5)",
            "N", 1, "-N <double>"));
    
    result.addElement(
        new Option(
            "\t turns on normalization of input data (default off)", 
            "Z", 0, "-Z"));
    
    result.addElement(
        new Option(
            "\t set the epsilon in loss function of epsilon-SVR (default 0.1)",
            "P", 1, "-P <double>"));
    
    result.addElement(
        new Option(
            "\t set cache memory size in MB (default 40)", 
            "M", 1, "-M <double>"));
    
    result.addElement(
        new Option(
            "\t set tolerance of termination criterion (default 0.001)",
            "E", 1, "-E <double>"));
    
    result.addElement(
        new Option(
            "\t turns the shrinking heuristics off (default on)",
            "H", 0, "-H"));
    
    result.addElement(
        new Option(
            "\t set the parameters C of class i to weight[i]*C, for C-SVC\n" 
            + "\t (default 1)",
            "W", 1, "-W <double>"));
    
    return result.elements();
  }
  
  /**
   * Sets the classifier options <p/>
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -S &lt;int&gt;
   *   set type of SVM (default 0)
   *    0 = C-SVC
   *    1 = nu-SVC
   *    2 = one-class SVM
   *    3 = epsilon-SVR
   *    4 = nu-SVR</pre>
   * 
   * <pre> -K &lt;int&gt;
   *   set type of kernel function (default 2)
   *    0 = linear: u'*v
   *    1 = polynomial: (gamma*u'*v + coef0)^degree
   *    2 = radial basis function: exp(-gamma*|u-v|^2)
   *    3 = sigmoid: tanh(gamma*u'*v + coef0)</pre>
   * 
   * <pre> -D &lt;int&gt;
   *   set degree in kernel function (default 3)</pre>
   * 
   * <pre> -G &lt;double&gt;
   *   set gamma in kernel function (default 1/k)</pre>
   * 
   * <pre> -R &lt;double&gt;
   *   set coef0 in kernel function (default 0)</pre>
   * 
   * <pre> -C &lt;double&gt;
   *   set the parameter C of C-SVC, epsilon-SVR, and nu-SVR
   *   (default 1)</pre>
   * 
   * <pre> -N &lt;double&gt;
   *   set the parameter nu of nu-SVC, one-class SVM, and nu-SVR
   *   (default 0.5)</pre>
   * 
   * <pre> -Z
   *   turns on normalization of input data (default off)</pre>
   * 
   * <pre> -P &lt;double&gt;
   *   set the epsilon in loss function of epsilon-SVR (default 0.1)</pre>
   * 
   * <pre> -M &lt;double&gt;
   *   set cache memory size in MB (default 40)</pre>
   * 
   * <pre> -E &lt;double&gt;
   *   set tolerance of termination criterion (default 0.001)</pre>
   * 
   * <pre> -H
   *   turns the shrinking heuristics off (default on)</pre>
   * 
   * <pre> -W &lt;double&gt;
   *   set the parameters C of class i to weight[i]*C, for C-SVC
   *   (default 1)</pre>
   * 
   <!-- options-end -->
   *
   * @param options     the options to parse
   * @throws Exception  if parsing fails
   */
  public void setOptions(String[] options) throws Exception {
    String      tmpStr;
    
    tmpStr = Utils.getOption('S', options);
    if (tmpStr.length() != 0)
      setSVMType(
          new SelectedTag(Integer.parseInt(tmpStr), TAGS_SVMTYPE));
    else
      setSVMType(
          new SelectedTag(SVMTYPE_C_SVC, TAGS_SVMTYPE));
    
    tmpStr = Utils.getOption('K', options);
    if (tmpStr.length() != 0)