center.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.
 */

/*
 * Center.java
 * Copyright (C) 2006 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.unsupervised.attribute;

import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SparseInstance;
import weka.core.Capabilities.Capability;
import weka.filters.UnsupervisedFilter;

/** 
 <!-- globalinfo-start -->
 * Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -unset-class-temporarily
 *  Unsets the class index temporarily before the filter is
 *  applied to the data.
 *  (default: no)</pre>
 * 
 <!-- options-end -->
 * 
 * @author Eibe Frank ([e-mail]) 
 * @author FracPete (fracpete at waikato dot ac dot nz) 
 * @version $Revision: 1.2 $
 */
public class Center 
  extends PotentialClassIgnorer 
  implements UnsupervisedFilter {

  /** for serialization */
  private static final long serialVersionUID = -9101338448900581023L;
  
  /** The means */
  private double[] m_Means;

  /**
   * Returns a string describing this filter
   *
   * @return 		a description of the filter suitable for
   * 			displaying in the explorer/experimenter gui
   */
  public String globalInfo() {

    return "Centers all numeric attributes in the given dataset "
      + "to have zero mean (apart from the class attribute, if set).";
  }

  /** 
   * Returns the Capabilities of this filter.
   *
   * @return            the capabilities of this object
   * @see               Capabilities
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enableAllAttributes();
    result.enable(Capability.MISSING_VALUES);
    
    // class
    result.enableAllClasses();
    result.enable(Capability.MISSING_CLASS_VALUES);
    result.enable(Capability.NO_CLASS);
    
    return result;
  }

  /**
   * 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 input format can't be set successfully
   */
  public boolean setInputFormat(Instances instanceInfo) throws Exception {
    super.setInputFormat(instanceInfo);
    setOutputFormat(instanceInfo);
    m_Means = null;
    return true;
  }

  /**
   * Input an instance for filtering. Filter requires all
   * training 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 set.
   */
  public boolean input(Instance instance) {

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

    if (m_NewBatch) {
      resetQueue();
      m_NewBatch = false;
    }
    
    if (m_Means == null) {
      bufferInput(instance);
      return false;
    } 
    else {
      convertInstance(instance);
      return true;
    }
  }

  /**
   * Signify that this batch of input to the filter is finished. 
   * If the filter requires all instances prior to filtering,
   * output() may now be called to retrieve the filtered instances.
   *
   * @return true 			if there are instances pending output
   * @throws IllegalStateException 	if no input structure has been defined
   */
  public boolean batchFinished() {
    if (getInputFormat() == null)
      throw new IllegalStateException("No input instance format defined");
    
    if (m_Means == null) {
      Instances input = getInputFormat();
      m_Means = new double[input.numAttributes()];
      for (int i = 0; i < input.numAttributes(); i++) {
	if (input.attribute(i).isNumeric() &&
	    (input.classIndex() != i)) {
	  m_Means[i] = input.meanOrMode(i);
	}
      }

      // Convert pending input instances
      for (int i = 0; i < input.numInstances(); i++)
	convertInstance(input.instance(i));
    }
    
    // Free memory
    flushInput();

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

  /**
   * Convert a single instance over. The converted instance is 
   * added to the end of the output queue.
   *
   * @param instance 	the instance to convert
   */
  private void convertInstance(Instance instance) {
    Instance inst = null;
    
    if (instance instanceof SparseInstance) {
      double[] newVals = new double[instance.numAttributes()];
      int[] newIndices = new int[instance.numAttributes()];
      double[] vals = instance.toDoubleArray();
      int ind = 0;
      for (int j = 0; j < instance.numAttributes(); j++) {
	double value;
	if (instance.attribute(j).isNumeric() &&
	    (!Instance.isMissingValue(vals[j])) &&
	    (getInputFormat().classIndex() != j)) {
	  
	  value = vals[j] - m_Means[j];
	  if (value != 0.0) {
	    newVals[ind] = value;
	    newIndices[ind] = j;
	    ind++;
	  }
	} else {
	  value = vals[j];
	  if (value != 0.0) {
	    newVals[ind] = value;
	    newIndices[ind] = j;
	    ind++;
	  }
	}
      }	
      double[] tempVals = new double[ind];
      int[] tempInd = new int[ind];
      System.arraycopy(newVals, 0, tempVals, 0, ind);
      System.arraycopy(newIndices, 0, tempInd, 0, ind);
      inst = new SparseInstance(instance.weight(), tempVals, tempInd,
                                instance.numAttributes());
    } 
    else {
      double[] vals = instance.toDoubleArray();
      for (int j = 0; j < getInputFormat().numAttributes(); j++) {
	if (instance.attribute(j).isNumeric() &&
	    (!Instance.isMissingValue(vals[j])) &&
	    (getInputFormat().classIndex() != j)) {
	  vals[j] = (vals[j] - m_Means[j]);
	}
      }	
      inst = new Instance(instance.weight(), vals);
    }
    
    inst.setDataset(instance.dataset());
    
    push(inst);
  }

  /**
   * Main method for running this filter.
   *
   * @param args 	should contain arguments to the filter: use -h for help
   */
  public static void main(String [] args) {
    runFilter(new Center(), args);
  }
}