supportvectorsettings.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
 * @version 1.1
 */
package com.prudsys.pdm.Models.Regression.SVM;

import com.prudsys.pdm.Core.MiningModel;
import com.prudsys.pdm.Core.MiningSettings;
import com.prudsys.pdm.Models.Regression.RegressionSettings;

/**
  * Parameters for computing support vector machines. <p>
  *
  * From PDM CWM extension. <p>
  *
  * Superclasses:
  * <ul>
  *   <li> RegressionSettings
  * </ul>
  * Attributes:
  * <ul>
  *   <li> <i>svmType</i>: Defines the type of SVM. The following types
  *   are predefined: SvmCSvc, SvmNuSvc, SvmOneClass, SvmEpsilonSvr, SvmNuSvr. <br>
  *     - type: Integer <br>
  *     - multiplicity: exactly one
  *   <li> <i>kernelType</i>: Defines the kernel type of the SVM. The following types
  *   are predefined: KernelLinear, KernelPoly, KernelRbf, KernelSigmoid. <br>
  *     - type: Integer <br>
  *     - multiplicity: exactly one
  *   <li> <i>degree</i>: Degree in kernel function. <br>
  *     - type: Float <br>
  *     - multiplicity: exactly one
  *   <li> <i>gamma</i>: Gamma in kernel function. <br>
  *     - type: Float <br>
  *     - multiplicity: exactly one
  *   <li> <i>coef0</i>: Coefficient coef0 in kernel function. <br>
  *     - type: float <br>
  *     - multiplicity: exactly one
  *   <li> <i>C</i>: Regularization parameter C. <br>
  *     - type: float <br>
  *     - multilplicity: exactly one
  *   <li> <i>nu</i>: Nu in NU-SVM. <br>
  *     - type: Float <br>
  *     - multiplicity: exactly one
  *   <li> <i>lossEpsilon</i>: Epsilon in EPSILON-SVR. <br>
  *     - type: Float <br>
  *     - multiplicity: exactly one
  * </ul>
  * Constraints:
  * <ul>
  *   <li> number of supportVectors must be equal to number of coefficients.
  * </ul>
  *
  * @see MiningSettings
  * @see RegressionSettings
  */
public class SupportVectorSettings extends RegressionSettings
{
    // -----------------------------------------------------------------------
    //  Constants defining SVM and kernel types
    // -----------------------------------------------------------------------
    public static final int SVM_C_SVC = 0;
    public static final int SVM_NU_SVC = 1;
    public static final int SVM_ONE_CLASS = 2;
    public static final int SVM_EPSILON_SVR = 3;
    public static final int SVM_NU_SVR = 4;

    public static final int KERNEL_LINEAR = 0;
    public static final int KERNEL_POLY = 1;
    public static final int KERNEL_RBF = 2;
    public static final int KERNEL_SIGMOID = 3;

    // -----------------------------------------------------------------------
    //  Variables declarations
    // -----------------------------------------------------------------------
    // SVM types:
    private int svmType    = SVM_C_SVC;
    private int kernelType = KERNEL_RBF;

    // Kernel parameters:
    private double degree = 3.0;
    private double gamma  = 1.0;
    private double coef0  = 0.0;

    // Algorithm parameters:
    private double C           = 1.0;
    private double nu          = 0.5;
    private double lossEpsilon = 0.1;

    // -----------------------------------------------------------------------
    //  Constructor
    // -----------------------------------------------------------------------
    /**
     * Empty constructor.
     */
    public SupportVectorSettings()
    {
        setFunction( MiningModel.REGRESSION_FUNCTION );
        setAlgorithm( MiningModel.SUPPORT_VECTOR_MACHINE_ALGORITHM);
    }

    // -----------------------------------------------------------------------
    //  Getter and setter methods
    // -----------------------------------------------------------------------
    // SVM type:
    public int getSvmType()
    {
      return svmType;
    }

    public void setSvmType(int svmType)
    {
      this.svmType = svmType;
    }

    // Kernel type:
    public int getKernelType()
    {
      return kernelType;
    }

    public void setKernelType(int kernelType)
    {
      this.kernelType = kernelType;
    }

    // Kernel parameters:
    public double getDegree()
    {
      return degree;
    }

    public void setDegree(double degree)
    {
      this.degree = degree;
    }

    public double getGamma()
    {
      return gamma;
    }

    public void setGamma(double gamma)
    {
      this.gamma = gamma;
    }

    public double getCoef0()
    {
      return coef0;
    }

    public void setCoef0(double coef0)
    {
      this.coef0 = coef0;
    }

    // algorithm parameters:
    public void setC(double C) {
      this.C = C;
    }

    public double getC() {
      return C;
    }

    public void setNu(double nu) {
      this.nu = nu;
    }

    public double getNu() {
      return nu;
    }

    public void setLossEpsilon(double lossEpsilon) {
      this.lossEpsilon = lossEpsilon;
    }

    public double getLossEpsilon() {
      return lossEpsilon;
    }

    // -----------------------------------------------------------------------
    //  Export methods
    // -----------------------------------------------------------------------
    /**
     * Returns settings as string.
     *
     * @return settings as string
     */
    public String toString()
    {
        return "Support vector machine\n" +
        "Target attribute=\"" + target + "\"";
    }

    /**
     * Returns settings as HTML string.
     *
     * @return settings as HTML string
     */
    public String toHtmlString()
    {
        String description = "Model:&nbsp;Support Vector Machine<br>" +
        "<a href=http://this?Target>Target attribute&nbsp;=&nbsp;<font color=red><b>" + target + "</b></color></a>";
        return description;
    }

}