clustermembership.java

代码是一个分类器的实现,其中使用了部分weka的源代码。可以将项目导入eclipse运行

/*
 *    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.
 */


/*
 *    ClusterMembership.java
 *    Copyright (C) 2004 Mark Hall
 *
 */

package weka.filters.unsupervised.attribute;

import weka.clusterers.DensityBasedClusterer;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;

import java.util.Enumeration;
import java.util.Vector;

/** 
 <!-- globalinfo-start -->
 * A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data). If a (nominal) class attribute is set, the clusterer is run separately for each class. The class attribute (if set) and any user-specified attributes are ignored during the clustering operation
 * <p/>
 <!-- globalinfo-end -->
 * 
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -W &lt;clusterer name&gt;
 *  Full name of clusterer to use. eg:
 *   weka.clusterers.EM
 *  Additional options after the '--'.
 *  (default: weka.clusterers.EM)</pre>
 * 
 * <pre> -I &lt;att1,att2-att4,...&gt;
 *  The range of attributes the clusterer should ignore.
 *  (the class attribute is automatically ignored)</pre>
 * 
 <!-- options-end -->
 *
 * Options after the -- are passed on to the clusterer.
 *
 * @author Mark Hall ([e-mail])
 * @author Eibe Frank
 * @version $Revision: 1.11 $
 */
public class ClusterMembership 
  extends Filter 
  implements UnsupervisedFilter, OptionHandler {
  
  /** for serialization */
  static final long serialVersionUID = 6675702504667714026L;

  /** The clusterer */
  protected DensityBasedClusterer m_clusterer = new weka.clusterers.EM();

  /** Array for storing the clusterers */
  protected DensityBasedClusterer[] m_clusterers;

  /** Range of attributes to ignore */
  protected Range m_ignoreAttributesRange;

  /** Filter for removing attributes */
  protected Filter m_removeAttributes;

  /** The prior probability for each class */
  protected double[] m_priors;

  /** 
   * Returns the Capabilities of this filter.
   *
   * @return            the capabilities of this object
   * @see               Capabilities
   */
  public Capabilities getCapabilities() {
    Capabilities result = m_clusterer.getCapabilities();
    
    result.setMinimumNumberInstances(0);
    
    return result;
  }
  
  /**
   * tests the data whether the filter can actually handle it
   * 
   * @param instanceInfo	the data to test
   * @throws Exception		if the test fails
   */
  protected void testInputFormat(Instances instanceInfo) throws Exception {
    getCapabilities().testWithFail(removeIgnored(instanceInfo));
  }
  
  /**
   * Sets the format of the input instances.
   *
   * @param instanceInfo an Instances object containing the input instance
   * structure (any instances contained in the object are ignored - only the
   * structure is required).
   * @return true if the outputFormat may be collected immediately
   * @throws Exception if the inputFormat can't be set successfully 
   */ 
  public boolean setInputFormat(Instances instanceInfo) throws Exception {
    
    super.setInputFormat(instanceInfo);
    m_removeAttributes = null;
    m_priors = null;

    return false;
  }

  /**
   * filters all attributes that should be ignored
   * 
   * @param data	the data to filter
   * @return		the filtered data
   * @throws Exception	if filtering fails
   */
  protected Instances removeIgnored(Instances data) throws Exception {
    Instances result = data;
    
    if (m_ignoreAttributesRange != null || data.classIndex() >= 0) {
      result = new Instances(data);
      m_removeAttributes = new Remove();
      String rangeString = "";
      if (m_ignoreAttributesRange != null) {
	rangeString += m_ignoreAttributesRange.getRanges();
      }
      if (data.classIndex() >= 0) {
	if (rangeString.length() > 0) {
	  rangeString += "," + (data.classIndex() + 1);
	} else {
	  rangeString = "" + (data.classIndex() + 1);
	}
      }
      ((Remove) m_removeAttributes).setAttributeIndices(rangeString);
      ((Remove) m_removeAttributes).setInvertSelection(false);
      m_removeAttributes.setInputFormat(data);
      result = Filter.useFilter(data, m_removeAttributes);
    }
    
    return result;
  }

  /**
   * Signify that this batch of input to the filter is finished.
   *
   * @return true if there are instances pending output
   * @throws IllegalStateException if no input structure has been defined 
   */  
  public boolean batchFinished() throws Exception {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }

    if (outputFormatPeek() == null) {
      Instances toFilter = getInputFormat();
      Instances[] toFilterIgnoringAttributes;

      // Make subsets if class is nominal
      if ((toFilter.classIndex() >= 0) && toFilter.classAttribute().isNominal()) {
	toFilterIgnoringAttributes = new Instances[toFilter.numClasses()];
	for (int i = 0; i < toFilter.numClasses(); i++) {
	  toFilterIgnoringAttributes[i] = new Instances(toFilter, toFilter.numInstances());
	}
	for (int i = 0; i < toFilter.numInstances(); i++) {
	  toFilterIgnoringAttributes[(int)toFilter.instance(i).classValue()].add(toFilter.instance(i));
	}
	m_priors = new double[toFilter.numClasses()];
	for (int i = 0; i < toFilter.numClasses(); i++) {
	  toFilterIgnoringAttributes[i].compactify();
	  m_priors[i] = toFilterIgnoringAttributes[i].sumOfWeights();
	}
	Utils.normalize(m_priors);
      } else {
	toFilterIgnoringAttributes = new Instances[1];
	toFilterIgnoringAttributes[0] = toFilter;
	m_priors = new double[1];
	m_priors[0] = 1;
      }

      // filter out attributes if necessary
      for (int i = 0; i < toFilterIgnoringAttributes.length; i++)
	toFilterIgnoringAttributes[i] = removeIgnored(toFilterIgnoringAttributes[i]);

      // build the clusterers
      if ((toFilter.classIndex() <= 0) || !toFilter.classAttribute().isNominal()) {
	m_clusterers = DensityBasedClusterer.makeCopies(m_clusterer, 1);
	m_clusterers[0].buildClusterer(toFilterIgnoringAttributes[0]);
      } else {
	m_clusterers = DensityBasedClusterer.makeCopies(m_clusterer, toFilter.numClasses());
	for (int i = 0; i < m_clusterers.length; i++) {
	  if (toFilterIgnoringAttributes[i].numInstances() == 0) {
	    m_clusterers[i] = null;
	  } else {
	    m_clusterers[i].buildClusterer(toFilterIgnoringAttributes[i]);
	  }
	}
      }
      
      // create output dataset
      FastVector attInfo = new FastVector();
      for (int j = 0; j < m_clusterers.length; j++) {
	if (m_clusterers[j] != null) {
	  for (int i = 0; i < m_clusterers[j].numberOfClusters(); i++) {
	    attInfo.addElement(new Attribute("pCluster_" + j + "_" + i));
	  }
	}
      }
      if (toFilter.classIndex() >= 0) {
	attInfo.addElement(toFilter.classAttribute().copy());
      }
      attInfo.trimToSize();
      Instances filtered = new Instances(toFilter.relationName()+"_clusterMembership",
					 attInfo, 0);
      if (toFilter.classIndex() >= 0) {
	filtered.setClassIndex(filtered.numAttributes() - 1);
      }
      setOutputFormat(filtered);

      // build new dataset
      for (int i = 0; i < toFilter.numInstances(); i++) {
	convertInstance(toFilter.instance(i));
      }
    }
    flushInput();

    m_NewBatch = true;
    return (numPendingOutput() != 0);
  }

  /**
   * Input an instance for filtering. Ordinarily the instance is processed
   * and made available for output immediately. Some filters require all
   * instances be read before producing output.
   *
   * @param instance the input instance
   * @return true if the filtered instance may now be
   * collected with output().
   * @throws IllegalStateException if no input format has been defined.
   */
  public boolean input(Instance instance) throws Exception {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");