sparsesvmalgorithm.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.SparseSVM;

import com.prudsys.pdm.Core.MiningException;
import com.prudsys.pdm.Models.Regression.SVM.SupportVectorAlgorithm;
import com.prudsys.pdm.Models.Supervised.Classifier;

/**
 * Implementation of Sparse Grid algorithms for sparse data.
 * SVMMethod is used as SupportVectorClassifier object.
 */
public class SparseSVMAlgorithm extends SupportVectorAlgorithm {

  private SVMMethod svmMethod;
  private double cacheSize;
  private double terminationEpsilon;
  private int shrinking;
  private double weight;

  /**
   * Empty constructor.
   */
  public SparseSVMAlgorithm() {
  }

  /**
   * Run algorithm. Result is written in variable svmMethod.
   */
  protected void runAlgorithm() throws MiningException {

    System.out.print("Building classifier...");
    svmMethod = new SVMMethod();
    svmMethod.setMetaData( metaData );

    // Types of SVM:
    svmMethod.m_param.svm_type = svmType;        // type of SVM
    svmMethod.m_param.kernel_type = kernelType;  // kernel type

    // Parameters of the approximation functions:
    svmMethod.m_param.degree = degree;	    // for poly
    svmMethod.m_param.gamma = gamma;	    // for poly/rbf/sigmoid
    svmMethod.m_param.coef0 = coef0;	    // for poly/sigmoid

    // These parameters are for training only:
    svmMethod.m_param.cache_size = cacheSize;   // in MB
    svmMethod.m_param.eps = terminationEpsilon;	// stopping criteria
    svmMethod.m_param.C = C;	    // for C_SVC, EPSILON_SVR, NU_SVR, SPARSE GRIDS
//	public int nr_weight;       // for C_SVC
//	public int[] weight_label;  // for C_SVC
    if (svmMethod.m_param.nr_weight!=0)
      svmMethod.m_param.weight[0] = weight;	    // for C_SVC
    svmMethod.m_param.nu = nu;	                    // for NU_SVC, ONE_CLASS, and NU_SVR
    svmMethod.m_param.p = lossEpsilon;	            // for EPSILON_SVR
    svmMethod.m_param.shrinking = shrinking;	    // use the shrinking heuristics

    // Run algorithm:
    svmMethod.buildClassifier(miningInputStream, target);
    System.out.println("\tok");
  }

  /**
   * Returns classifier.
   *
   * @return classifier
   */
  public Classifier getClassifier() {

    return svmMethod;
  }

  // Specific parameters:
  public void setCacheSize(double cacheSize) {
    this.cacheSize = cacheSize;
  }
  public double getCacheSize() {
    return cacheSize;
  }
  public void setTerminationEpsilon(double terminationEpsilon) {
    this.terminationEpsilon = terminationEpsilon;
  }
  public double getTerminationEpsilon() {
    return terminationEpsilon;
  }
  public void setShrinking(int shrinking) {
    this.shrinking = shrinking;
  }
  public int getShrinking() {
    return shrinking;
  }
  public void setWeight(double weight) {
    this.weight = weight;
  }
  public double getWeight() {
    return weight;
  }
}