All Packages Class Hierarchy This Package Previous Next Index WEKA's home
Class weka.filters.SpreadSubsampleFilter
java.lang.Object
|
+----weka.filters.Filter
|
+----weka.filters.SpreadSubsampleFilter
- public class SpreadSubsampleFilter
- extends Filter
- implements OptionHandler
Produces a random subsample of a dataset. The original dataset must
fit entirely in memory. This filter allows you to specify the maximum
"spread" between the rarest and most common class. For example, you may
specify that there be at most a 2:1 difference in class frequencies.
When used in batch mode, subsequent batches are
not resampled.
Valid options are:
-S num
Specify the random number seed (default 1).
-M num
The maximum class distribution spread.
0 = no maximum spread, 1 = uniform distribution, 10 = allow at most a
10:1 ratio between the classes (default 0)
-X num
The maximum count for any class value.
(default 0 = unlimited)
-W
Adjust weights so that total weight per class is maintained. Individual
instance weighting is not preserved. (default no weights adjustment)
- Version:
- $Revision: 1.8 $
- Author:
- Stuart Inglis (stuart@intelligenesis.net)
SpreadSubsampleFilter()
-
batchFinished()
- Signify that this batch of input to the filter is finished.
getAdjustWeights()
- Returns true if instance weights will be adjusted to maintain
total weight per class.
getDistributionSpread()
- Gets the value for the distribution spread
getMaxCount()
- Gets the value for the max count
getOptions()
- Gets the current settings of the filter.
getRandomSeed()
- Gets the random number seed.
input(Instance)
- Input an instance for filtering.
listOptions()
- Returns an enumeration describing the available options
main(String[])
- Main method for testing this class.
setAdjustWeights(boolean)
- Sets whether the instance weights will be adjusted to maintain
total weight per class.
setDistributionSpread(double)
- Sets the value for the distribution spread
setInputFormat(Instances)
- Sets the format of the input instances.
setMaxCount(double)
- Sets the value for the max count
setOptions(String[])
- Parses a list of options for this object.
setRandomSeed(int)
- Sets the random number seed.
SpreadSubsampleFilter
public SpreadSubsampleFilter()
getAdjustWeights
public boolean getAdjustWeights()
Returns true if instance weights will be adjusted to maintain
total weight per class.
- Returns:
- true if instance weights will be adjusted to maintain
total weight per class.
setAdjustWeights
public void setAdjustWeights(boolean newAdjustWeights)
Sets whether the instance weights will be adjusted to maintain
total weight per class.
- Parameters:
newAdjustWeights
-
listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options
- 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).
-M num
The maximum class distribution spread.
0 = no maximum spread, 1 = uniform distribution, 10 = allow at most a
10:1 ratio between the classes (default 0)
-X num
The maximum count for any class value.
(default 0 = unlimited)
-W
Adjust weights so that total weight per class is maintained. Individual
instance weighting is not preserved. (default no weights adjustment)
- 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.
- Returns:
- an array of strings suitable for passing to setOptions
setDistributionSpread
public void setDistributionSpread(double spread)
Sets the value for the distribution spread
- Parameters:
spread
- the new distribution spread
getDistributionSpread
public double getDistributionSpread()
Gets the value for the distribution spread
- Returns:
- the distribution spread
setMaxCount
public void setMaxCount(double maxcount)
Sets the value for the max count
- Parameters:
spread
- the new max count
getMaxCount
public double getMaxCount()
Gets the value for the max count
- Returns:
- the max count
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.
setInputFormat
public boolean setInputFormat(Instances instanceInfo) throws java.lang.Exception
Sets the format of the input instances.
- 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:
- UnassignedClassException - if no class attribute has been set.
- UnsupportedClassTypeException - if the class attribute
is not nominal.
- Overrides:
- setInputFormat in class Filter
input
public boolean input(Instance instance)
Input an instance for filtering. Filter requires all
training instances be read before producing output.
- 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
- Overrides:
- input in class Filter
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.
- Returns:
- true if there are instances pending output
- Throws:
- java.lang.IllegalStateException - if no input structure has been defined
- Overrides:
- batchFinished in class Filter
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
All Packages Class Hierarchy This Package Previous Next Index WEKA's home