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## weka.filters.supervised.instance
Class Resample

    java.lang.Object
      weka.filters.Filter
          weka.filters.supervised.instance.Resample

All Implemented Interfaces:
  java.io.Serializable, CapabilitiesHandler, OptionHandler, RevisionHandler, SupervisedFilter

    public class Resample

extends Filter
implements SupervisedFilter, OptionHandler

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. The dataset must have a nominal class attribute. If not, use the unsupervised version. The filter can be made to maintain the class distribution in the subsample, or to bias the class distribution toward a uniform distribution. When used in batch mode (i.e. in the FilteredClassifier), 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)

     -B <num>
      Bias factor towards uniform class distribution.
      0 = distribution in input data -- 1 = uniform distribution.
      (default 0)

     -no-replacement
      Disables replacement of instances
      (default: with replacement)

     -V
      Inverts the selection - only available with '-no-replacement'.

Version:
  $Revision: 5542 $
Author:
  Len Trigg (len@reeltwo.com), FracPete (fracpete at waikato dot ac dot nz)
See Also:
  Serialized Form

Constructor Summary
`Resample()`

Method Summary
`boolean` | `batchFinished()` Signify that this batch of input to the filter is finished.
`java.lang.String` | `biasToUniformClassTipText()` Returns the tip text for this property.
`void` | `createSubsampleWithoutReplacement(java.util.Random random, int origSize, int sampleSize, int actualClasses, int[] classIndices)` creates the subsample without replacement.
`void` | `createSubsampleWithReplacement(java.util.Random random, int origSize, int sampleSize, int actualClasses, int[] classIndices)` creates the subsample with replacement.
`double` | `getBiasToUniformClass()` Gets the bias towards a uniform class.
`Capabilities` | `getCapabilities()` Returns the Capabilities of this filter.
`boolean` | `getInvertSelection()` Gets whether selection is inverted (only if instances are drawn WIHTOUT replacement).
`boolean` | `getNoReplacement()` Gets whether instances are drawn with or without replacement.
`java.lang.String[]` | `getOptions()` Gets the current settings of the filter.
`int` | `getRandomSeed()` Gets the random number seed.
`java.lang.String` | `getRevision()` Returns the revision string.
`double` | `getSampleSizePercent()` Gets the subsample size as a percentage of the original set.
`java.lang.String` | `globalInfo()` Returns a string describing this filter.
`boolean` | `input(Instance instance)` Input an instance for filtering.
`java.lang.String` | `invertSelectionTipText()` Returns the tip text for this property.
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String` | `noReplacementTipText()` Returns the tip text for this property.
`java.lang.String` | `randomSeedTipText()` Returns the tip text for this property.
`java.lang.String` | `sampleSizePercentTipText()` Returns the tip text for this property.
`void` | `setBiasToUniformClass(double newBiasToUniformClass)` Sets the bias towards a uniform class.
`boolean` | `setInputFormat(Instances instanceInfo)` Sets the format of the input instances.
`void` | `setInvertSelection(boolean value)` Sets whether the selection is inverted (only if instances are drawn WIHTOUT replacement).
`void` | `setNoReplacement(boolean value)` Sets whether instances are drawn with or with out replacement.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setRandomSeed(int newSeed)` Sets the random number seed.
`void` | `setSampleSizePercent(double newSampleSizePercent)` Sets the size of the subsample, as a percentage of the original set.

Methods inherited from class weka.filters.Filter
`batchFilterFile, filterFile, getCapabilities, getOutputFormat, isFirstBatchDone, isNewBatch, isOutputFormatDefined, makeCopies, makeCopy, numPendingOutput, output, outputPeek, toString, useFilter, wekaStaticWrapper`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

Constructor Detail

### Resample

    public Resample()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this filter.

  Returns:
    a description of the filter suitable for displaying in the explorer/experimenter gui

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Returns:
    an enumeration of all the available options.

### setOptions

    public void setOptions(java.lang.String[] options)
                    throws java.lang.Exception

  Parses a given list of options.

  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)

       -B <num>
        Bias factor towards uniform class distribution.
        0 = distribution in input data -- 1 = uniform distribution.
        (default 0)

       -no-replacement
        Disables replacement of instances
        (default: with replacement)

       -V
        Inverts the selection - only available with '-no-replacement'.

  Specified by:
    `setOptions` in interface `OptionHandler`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an option is not supported

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the filter.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Returns:
    an array of strings suitable for passing to setOptions

### biasToUniformClassTipText

    public java.lang.String biasToUniformClassTipText()

  Returns the tip text for this property.

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getBiasToUniformClass

    public double getBiasToUniformClass()

  Gets the bias towards a uniform class. A value of 0 leaves the class distribution as-is, a value of 1 ensures the class distributions are uniform in the output data.

  Returns:
    the current bias

### setBiasToUniformClass

    public void setBiasToUniformClass(double newBiasToUniformClass)

  Sets the bias towards a uniform class. A value of 0 leaves the class distribution as-is, a value of 1 ensures the class distributions are uniform in the output data.

  Parameters:
    `newBiasToUniformClass` - the new bias value, between 0 and 1.

### randomSeedTipText

    public java.lang.String randomSeedTipText()

  Returns the tip text for this property.

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getRandomSeed

    public int getRandomSeed()

  Gets the random number seed.

  Returns:
    the random number seed.

### setRandomSeed

    public void setRandomSeed(int newSeed)

  Sets the random number seed.

  Parameters:
    `newSeed` - the new random number seed.

### sampleSizePercentTipText

    public java.lang.String sampleSizePercentTipText()

  Returns the tip text for this property.

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getSampleSizePercent

    public double getSampleSizePercent()

  Gets the subsample size as a percentage of the original set.

  Returns:
    the subsample size

### setSampleSizePercent

    public void setSampleSizePercent(double newSampleSizePercent)

  Sets the size of the subsample, as a percentage of the original set.

  Parameters:
    `newSampleSizePercent` - the subsample set size, between 0 and 100.

### noReplacementTipText

    public java.lang.String noReplacementTipText()

  Returns the tip text for this property.

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getNoReplacement

    public boolean getNoReplacement()

  Gets whether instances are drawn with or without replacement.

  Returns:
    true if the replacement is disabled

### setNoReplacement

    public void setNoReplacement(boolean value)

  Sets whether instances are drawn with or with out replacement.

  Parameters:
    `value` - if true then the replacement of instances is disabled

### invertSelectionTipText

    public java.lang.String invertSelectionTipText()

  Returns the tip text for this property.

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getInvertSelection

    public boolean getInvertSelection()

  Gets whether selection is inverted (only if instances are drawn WIHTOUT replacement).

  Returns:
    true if the replacement is disabled
  See Also:
    `m_NoReplacement`

### setInvertSelection

    public void setInvertSelection(boolean value)

  Sets whether the selection is inverted (only if instances are drawn WIHTOUT replacement).

  Parameters:
    `value` - if true then selection is inverted

### getCapabilities

    public Capabilities getCapabilities()

  Returns the Capabilities of this filter.

  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `Filter`
  Returns:
    the capabilities of this object
  See Also:
    `Capabilities`

### setInputFormat

    public boolean setInputFormat(Instances instanceInfo)
                           throws java.lang.Exception

  Sets the format of the input instances.

  Overrides:
    `setInputFormat` in class `Filter`
  Parameters:
    `instanceInfo` - an Instances object containing the input instance structure (any instances contained in the object are ignored - only the structure is required).
  Returns:
    true if the outputFormat may be collected immediately
  Throws:
    `java.lang.Exception` - if the input format can't be set successfully

### input

    public boolean input(Instance instance)

  Input an instance for filtering. Filter requires all training instances be read before producing output.

  Overrides:
    `input` in class `Filter`
  Parameters:
    `instance` - the input instance
  Returns:
    true if the filtered instance may now be collected with output().
  Throws:
    `java.lang.IllegalStateException` - if no input structure has been defined

### batchFinished

    public boolean batchFinished()

  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.

  Overrides:
    `batchFinished` in class `Filter`
  Returns:
    true if there are instances pending output
  Throws:
    `java.lang.IllegalStateException` - if no input structure has been defined

### createSubsampleWithReplacement

    public void createSubsampleWithReplacement(java.util.Random random,
                                               int origSize,
                                               int sampleSize,
                                               int actualClasses,
                                               int[] classIndices)

  creates the subsample with replacement.

  Parameters:
    `random` - the random number generator to use
    `origSize` - the original size of the dataset
    `sampleSize` - the size to generate
    `actualClasses` - the number of classes found in the data
    `classIndices` - the indices where classes start

### createSubsampleWithoutReplacement

    public void createSubsampleWithoutReplacement(java.util.Random random,
                                                  int origSize,
                                                  int sampleSize,
                                                  int actualClasses,
                                                  int[] classIndices)

  creates the subsample without replacement.

  Parameters:
    `random` - the random number generator to use
    `origSize` - the original size of the dataset
    `sampleSize` - the size to generate
    `actualClasses` - the number of classes found in the data
    `classIndices` - the indices where classes start

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `Filter`
  Returns:
    the revision

### main

    public static void main(java.lang.String[] argv)

  Main method for testing this class.

  Parameters:
    `argv` - should contain arguments to the filter: use -h for help

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