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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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 3 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, see .
*/
/*
* Resample.java
* Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.unsupervised.instance;
import java.util.Collections;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;
/**
* Produces a random subsample of a dataset using
* either sampling with replacement or without 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.
*
*
*
* Valid options are:
*
*
*
* -S <num>
* Specify the random number seed (default 1)
*
*
*
* -Z <num>
* The size of the output dataset, as a percentage of
* the input dataset (default 100)
*
*
*
* -no-replacement
* Disables replacement of instances
* (default: with replacement)
*
*
*
* -V
* Inverts the selection - only available with '-no-replacement'.
*
*
*
*
* @author Len Trigg ([email protected])
* @author FracPete (fracpete at waikato dot ac dot nz)
* @author Eibe Frank
* @version $Revision: 12037 $
*/
public class Resample extends Filter implements UnsupervisedFilter,
OptionHandler {
/** for serialization */
static final long serialVersionUID = 3119607037607101160L;
/** The subsample size, percent of original set, default 100% */
protected double m_SampleSizePercent = 100;
/** The random number generator seed */
protected int m_RandomSeed = 1;
/** Whether to perform sampling with replacement or without */
protected boolean m_NoReplacement = false;
/**
* Whether to invert the selection (only if instances are drawn WITHOUT
* replacement)
*
* @see #m_NoReplacement
*/
protected boolean m_InvertSelection = false;
/**
* 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 either sampling with "
+ "replacement or without 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.
*/
@Override
public Enumeration