wekaclusteringminingmodel.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 1.0
  */

package com.prudsys.pdm.Adapters.Weka;

import java.lang.reflect.Method;

import com.prudsys.pdm.Core.MiningException;
import com.prudsys.pdm.Core.MiningMatrixElement;
import com.prudsys.pdm.Input.MiningVector;
import com.prudsys.pdm.Models.Clustering.ClusteringMiningModel;

/**
  * Representation of a Weka clustering model.
  */
public class WekaClusteringMiningModel extends ClusteringMiningModel
{

  private Object wekaClusterer = null;
  private Object wekaInstances = null;

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

  /**
   * Constructor with weka clusterer object.
   *
   * @param wekaClusterer clusterer obtained from weka
   * @exception MiningException wekaClusterer is not assignable to weka.clusterers.Clusterer
   */
  public WekaClusteringMiningModel(Object wekaClusterer) throws MiningException
  {
    try {
      // Check for assinable class:
      Class argClassType       = wekaClusterer.getClass();
      Class<?> wekaClustererClass = Class.forName("weka.clusterers.Clusterer");
      if (! wekaClustererClass.isAssignableFrom(argClassType))
        throw new Exception(wekaClustererClass.getName() + " is not assignable from "
	          + argClassType);

      // Assign:
      this.wekaClusterer = wekaClusterer;
    }
    catch (Exception ex) {
      throw new MiningException( "Weka class exception." );
    };
  }

  /**
   * Set Weka instances for applying vectors.
   *
   * @param wekaInstances weka instances to apply
   */
  public void setWekaInstances(Object wekaInstances) {

    this.wekaInstances = wekaInstances;
  }

  /**
   * Applies Weka clustering model to mining vector returning the number
   * of the cluster it belongs to.
   *
   * @param miningVector mining vector to be assigned to a cluster
   * @return number of the cluster the vector belongs to
   * @throws MiningException if there are some errors when model is applied
   * @deprecated since version 1.1, use applyModel instead
   */
  public int apply(MiningVector miningVector) throws MiningException
  {
    return (int) applyModelFunction(miningVector);
  }

  /**
   * Applies function of Weka clustering model to a mining vector.
   * The mining vector is converted to a Weka instance and then its
   * clusterInstance method is applied. The meta data of the mining vector
   * should be similar to the metaData of this class. This ensures
   * compatibility of training and application data.
   *
   * @param miningVector mining vector where the model should be applied
   * @return function value of the mining vector
   * @throws MiningException if there are some errors when model is applied
   */
  public double applyModelFunction(MiningVector miningVector)
      throws MiningException
    {

    try {
      Object wekaInstance = WekaCoreAdapter.PDMMiningVector2WekaInstance( miningVector,
                            wekaInstances );

      Class wekaClustererClass = Class.forName("weka.clusterers.Clusterer");
      Class wekaInstanceClass  = Class.forName("weka.core.Instance");

      Class[] methodArgumentTypes = { wekaInstanceClass };
      Method classMethod = wekaClustererClass.getMethod("clusterInstance", methodArgumentTypes);

      Object[] instance = { wekaInstance };
      Integer res = (Integer) classMethod.invoke(wekaClusterer, instance);

      return res.intValue();

    }
    catch (Exception ex) {
      ex.printStackTrace();
      throw new MiningException("Mining vector could not be clustered accurately.");
    }
  }

  /**
   * Returns the cluster object of the mining vector given as argument.
   * Not implemented since in Weka there is no general Cluster class.
   *
   * @param miningData mining data where the model should be applied
   * @return data resulting from the model application
   * @throws MiningException always thrown
   */
  public MiningMatrixElement applyModel(MiningMatrixElement miningData)
      throws MiningException {
    throw new MiningException("Not implemented.");
  }

  /**
   * Returns the number of clusters calling Weka's numberOftClusters
   * method.
   *
   * @return number of clusters
   * @exception MiningException could not get number of clusters
   */
  public int getNumberOfClusters() throws MiningException {

    try {
      Class wekaClustererClass = Class.forName("weka.clusterers.Clusterer");
      
      Class parameterTypes = null;
      
      Method classMethod = wekaClustererClass.getMethod("numberOfClusters", parameterTypes);
      Integer res = (Integer) classMethod.invoke(wekaClusterer, parameterTypes);

      return res.intValue();

    }
    catch (Exception ex) {
      ex.printStackTrace();
      throw new MiningException("Could not get the number of clusters.");
    }
  }



}