magpie.data.utilities.splitters

Class ClustererSplitter

java.lang.Object

magpie.data.utilities.splitters.BaseDatasetSplitter

magpie.data.utilities.splitters.ClustererSplitter

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Commandable, Options, Printable

public class ClustererSplitter
extends BaseDatasetSplitter

Split data based using unsupervised learning. Uses a BaseClusterer to label each entry's membership in a certain cluster. User can either provide an untrained clusterer and have the splitter train it, or provide a trained clusterer and use as-is.

Usage: $<clusterer>
clusterer: Data will be spit based on clusters produced by this BaseClusterer

See Also:

BaseClusterer, Serialized Form

Constructor Summary

Constructors

Constructor and Description
ClustererSplitter()

Method Summary

Modifier and Type	Method and Description
ClustererSplitter	clone()
java.util.List<java.lang.String>	getSplitNames() Get the names of the splits this splitter creates
protected java.util.List<java.lang.String>	getSplitterDetails(boolean htmlFormat) Get details of splitter.
int[]	label(Dataset D) Given a dataset, determine which subset each entry should belong to.
java.lang.String	printUsage() Print out required format for options.
void	setClusterer(BaseClusterer x) Set the clusterer used for splitting.
void	setOptions(java.util.List Options) Set any options for this object.
void	train(Dataset TrainingSet) Train a dataset splitter, if necessary

Methods inherited from class magpie.data.utilities.splitters.BaseDatasetSplitter

about, printCommand, printDescription, runCommand, split, split

Methods inherited from class java.lang.Object

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

Constructor Detail

ClustererSplitter

public ClustererSplitter()

Method Detail

clone

public ClustererSplitter clone()

Overrides:

clone in class BaseDatasetSplitter

setClusterer

public void setClusterer(BaseClusterer x)

Set the clusterer used for splitting. If this Clusterer is already trained, it will be used as provided.

Parameters:

x - Clusterer to be used for splitting

setOptions

public void setOptions(java.util.List Options)
                throws java.lang.Exception

Description copied from interface: Options

Set any options for this object. If parsing the options fails, return an Exception with the usage as its message.

Parameters:

Options - Array of options as Objects - can be null

Throws:

java.lang.Exception - if problem with inputs

printUsage

public java.lang.String printUsage()

Description copied from interface: Options

Print out required format for options. For consistency, should start with "Usage: ".

Returns:

Usage for this function

train

public void train(Dataset TrainingSet)

Description copied from class: BaseDatasetSplitter

Train a dataset splitter, if necessary

Specified by:

train in class BaseDatasetSplitter

Parameters:

TrainingSet - Dataset to use for training

label

public int[] label(Dataset D)

Description copied from class: BaseDatasetSplitter

Given a dataset, determine which subset each entry should belong to. Please ensure that D remains unaltered (clone if you must)

Specified by:

label in class BaseDatasetSplitter

Parameters:

D - Dataset to be labeled

Returns:

A list of nonnegative integers marking which subset to split each entry into

getSplitNames

public java.util.List<java.lang.String> getSplitNames()

Description copied from class: BaseDatasetSplitter

Get the names of the splits this splitter creates

Specified by:

getSplitNames in class BaseDatasetSplitter

Returns:

Names of each group

getSplitterDetails

protected java.util.List<java.lang.String> getSplitterDetails(boolean htmlFormat)

Description copied from class: BaseDatasetSplitter

Get details of splitter. Returns a list of options that can be set by the user. Used with BaseDatasetSplitter.printDescription(boolean).

Specified by:

getSplitterDetails in class BaseDatasetSplitter

Parameters:

htmlFormat - Whether to format results with HTML

Returns:

List of options describing the model