resample.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.
 */

/*
 *    Resample.java
 *    Copyright (C) 2002 University of Waikato
 *
 */


package weka.filters.unsupervised.instance;

import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;

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

/** 
 <!-- globalinfo-start -->
 * Produces a random subsample of a dataset using sampling with replacement. The original dataset must fit entirely in memory. The number of instances in the generated dataset may be specified. When used in batch mode, subsequent batches are NOT resampled.
 * <p/>
 <!-- globalinfo-end -->
 * 
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -S &lt;num&gt;
 *  Specify the random number seed (default 1)</pre>
 * 
 * <pre> -Z &lt;num&gt;
 *  The size of the output dataset, as a percentage of
 *  the input dataset (default 100)</pre>
 * 
 <!-- options-end -->
 *
 * @author Len Trigg ([e-mail])
 * @version $Revision: 1.7 $ 
 */
public class Resample 
  extends Filter 
  implements UnsupervisedFilter, OptionHandler {
  
  /** for serialization */
  static final long serialVersionUID = 3119607037607101160L;

  /** The subsample size, percent of original set, default 100% */
  private double m_SampleSizePercent = 100;
  
  /** The random number generator seed */
  private int m_RandomSeed = 1;

  /**
   * Returns a string describing this classifier
   * @return a description of the classifier suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "Produces a random subsample of a dataset using sampling with "
      + "replacement. The original dataset must fit entirely in memory. The "
      + "number of instances in the generated dataset may be specified. When "
      + "used in batch mode, subsequent batches are NOT resampled.";
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(1);

    newVector.addElement(new Option(
              "\tSpecify the random number seed (default 1)",
              "S", 1, "-S <num>"));
    newVector.addElement(new Option(
              "\tThe size of the output dataset, as a percentage of\n"
              +"\tthe input dataset (default 100)",
              "Z", 1, "-Z <num>"));

    return newVector.elements();
  }


  /**
   * Parses a given list of options. <p/>
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -S &lt;num&gt;
   *  Specify the random number seed (default 1)</pre>
   * 
   * <pre> -Z &lt;num&gt;
   *  The size of the output dataset, as a percentage of
   *  the input dataset (default 100)</pre>
   * 
   <!-- options-end -->
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {
    
    String seedString = Utils.getOption('S', options);
    if (seedString.length() != 0) {
      setRandomSeed(Integer.parseInt(seedString));
    } else {
      setRandomSeed(1);
    }

    String sizeString = Utils.getOption('Z', options);
    if (sizeString.length() != 0) {
      setSampleSizePercent(Double.valueOf(sizeString).doubleValue());
    } else {
      setSampleSizePercent(100);
    }

    if (getInputFormat() != null) {
      setInputFormat(getInputFormat());
    }
  }

  /**
   * Gets the current settings of the filter.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {

    String [] options = new String [6];
    int current = 0;

    options[current++] = "-S"; options[current++] = "" + getRandomSeed();

    options[current++] = "-Z"; options[current++] = "" + getSampleSizePercent();

    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String randomSeedTipText() {
    return "The seed used for random sampling.";
  }

  /**
   * Gets the random number seed.
   *
   * @return the random number seed.
   */
  public int getRandomSeed() {

    return m_RandomSeed;
  }
  
  /**
   * Sets the random number seed.
   *
   * @param newSeed the new random number seed.
   */
  public void setRandomSeed(int newSeed) {

    m_RandomSeed = newSeed;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String sampleSizePercentTipText() {
    return "Size of the subsample as a percentage of the original dataset.";
  }

  /**
   * Gets the subsample size as a percentage of the original set.
   *
   * @return the subsample size
   */
  public double getSampleSizePercent() {

    return m_SampleSizePercent;
  }
  
  /**
   * Sets the size of the subsample, as a percentage of the original set.
   *
   * @param newSampleSizePercent the subsample set size, between 0 and 100.
   */
  public void setSampleSizePercent(double newSampleSizePercent) {

    m_SampleSizePercent = newSampleSizePercent;
  }

  /** 
   * 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);
    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 structure has been defined
   */
  public boolean input(Instance instance) {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }
    if (m_NewBatch) {
      resetQueue();
      m_NewBatch = false;
    }
    if (isFirstBatchDone()) {
      push(instance);
      return true;
    } else {
      bufferInput(instance);
      return false;
    }
  }

  /**
   * 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 (!isFirstBatchDone()) {
      // Do the subsample, and clear the input instances.
      createSubsample();
    }
    flushInput();

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

  /**
   * Creates a subsample of the current set of input instances. The output
   * instances are pushed onto the output queue for collection.
   */
  private void createSubsample() {

    int origSize = getInputFormat().numInstances();
    int sampleSize = (int) (origSize * m_SampleSizePercent / 100);
    
    // Simple subsample
    
    Random random = new Random(m_RandomSeed);
    // Convert pending input instances
    for(int i = 0; i < sampleSize; i++) {
      int index = random.nextInt(origSize);
      push((Instance)getInputFormat().instance(index).copy());
    }
  }
  
  /**
   * Main method for testing this class.
   *
   * @param argv should contain arguments to the filter: 
   * use -h for help
   */
  public static void main(String [] argv) {
    runFilter(new Resample(), argv);
  }
}