weka.filters
Class ResampleFilter

java.lang.Object
  |
  +--weka.filters.Filter
        |
        +--weka.filters.ResampleFilter

All Implemented Interfaces:

OptionHandler, java.io.Serializable

public class ResampleFilter

extends Filter

implements OptionHandler

Produces a random subsample of a dataset. The original dataset must fit entirely in memory. The number of instances in the generated dataset may be specified. If the dataset has a (nominal) class attribute, 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, subsequent batches are not resampled. Valid options are:

-S num
Specify the random number seed (default 1).

-B num
Specify a bias towards uniform class distribution. 0 = distribution in input data, 1 = uniform class distribution (default 0).

-Z percent
Specify the size of the output dataset, as a percentage of the input dataset (default 100).

Author:

Len Trigg (len@intelligenesis.net)

See Also:

Serialized Form

Fields inherited from class weka.filters.Filter

m_NewBatch

Constructor Summary

ResampleFilter()

Method Summary

boolean	batchFinished() Signify that this batch of input to the filter is finished.
double	getBiasToUniformClass() Gets the bias towards a uniform class.
java.lang.String[]	getOptions() Gets the current settings of the filter.
int	getRandomSeed() Gets the random number seed.
double	getSampleSizePercent() Gets the subsample size as a percentage of the original set.
boolean	input(Instance instance) Input an instance for filtering.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setBiasToUniformClass(double newBiasToUniformClass) Sets the bias towards a uniform class.
boolean	setInputFormat(Instances instanceInfo) Sets the format of the input instances.
void	setOptions(java.lang.String[] options) Parses a list of options for this object.
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, bufferInput, copyStringValues, copyStringValues, filterFile, flushInput, getInputFormat, getInputStringIndex, getOutputFormat, getOutputStringIndex, getStringIndices, inputFormat, isOutputFormatDefined, numPendingOutput, output, outputFormat, outputFormatPeek, outputPeek, push, resetQueue, setOutputFormat, useFilter

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ResampleFilter

public ResampleFilter()

Method Detail

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 list of options for this object. Valid options are:

-S num
Specify the random number seed (default 1).

-B num
Specify a bias towards uniform class distribution. 0 = distribution in input data, 1 = uniform class distribution (default 0).

-Z percent
Specify the size of the output dataset, as a percentage of the input dataset (default 100).

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

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.

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.

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.

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

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