magpie.data.utilities.normalizers

Class MultiNormalizer

java.lang.Object

magpie.data.utilities.normalizers.BaseDatasetNormalizer

magpie.data.utilities.normalizers.MultiNormalizer

All Implemented Interfaces:

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

public class MultiNormalizer
extends BaseDatasetNormalizer

Use multiple strategies to normalize dataset.

Usage: -norm <method 1> [<options 1...>] [-norm <method 2> [<options 2...>] ] <...>
method: Name of another normalizer method
options: Any options for that method

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
MultiNormalizer()

Method Summary

Modifier and Type	Method and Description
void	addNormalizer(BaseDatasetNormalizer norm) Add a new normalizer to this multi normalizer.
protected void	normalizeAttributes(Dataset Data) Perform the actual normalization on the attributes
protected void	normalizeClassVariable(Dataset Data) Perform the actual normalization on the class variable.
java.lang.String	printUsage() Print out required format for options.
protected void	restoreAttributes(Dataset Data) Perform the actual restoration on the attributes
protected void	restoreClassVariable(Dataset Data) Perform the actual restoration on the class variables (measured and predicted!)
void	setOptions(java.util.List<java.lang.Object> Options) Set any options for this object.
protected void	trainOnAttributes(Dataset Data) Perform the actual training work for attributes.
protected void	trainOnMeasuredClass(Dataset Data) Perform the actual training work for the class variable.

Methods inherited from class magpie.data.utilities.normalizers.BaseDatasetNormalizer

about, clone, isTrained, normalize, printCommand, printDescription, restore, runCommand, setToNormalizeAttributes, setToNormalizeClass, test, train, willNormalizeAttributes, willNormalizeClass

Methods inherited from class java.lang.Object

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

Constructor Detail

MultiNormalizer

public MultiNormalizer()

Method Detail

setOptions

public void setOptions(java.util.List<java.lang.Object> 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

trainOnAttributes

protected void trainOnAttributes(Dataset Data)

Description copied from class: BaseDatasetNormalizer

Perform the actual training work for attributes. Must establish the parameters necessary to normalize attributes and return them to their original range.

Specified by:

trainOnAttributes in class BaseDatasetNormalizer

Parameters:

Data - Training set

trainOnMeasuredClass

protected void trainOnMeasuredClass(Dataset Data)

Description copied from class: BaseDatasetNormalizer

Perform the actual training work for the class variable. Must establish the parameters necessary to normalize attributes and return them to their original range.

NOTE: Use the measured class variable to train normalizer.

Specified by:

trainOnMeasuredClass in class BaseDatasetNormalizer

Parameters:

Data - Training set

addNormalizer

public void addNormalizer(BaseDatasetNormalizer norm)

Add a new normalizer to this multi normalizer.

Parameters:

norm - New normalizer

normalizeAttributes

protected void normalizeAttributes(Dataset Data)

Description copied from class: BaseDatasetNormalizer

Perform the actual normalization on the attributes

Specified by:

normalizeAttributes in class BaseDatasetNormalizer

Parameters:

Data - Dataset to be transformed

normalizeClassVariable

protected void normalizeClassVariable(Dataset Data)

Description copied from class: BaseDatasetNormalizer

Perform the actual normalization on the class variable. Predicted and measured class variables must be transformed the same way

Specified by:

normalizeClassVariable in class BaseDatasetNormalizer

Parameters:

Data - Dataset to be transformed

restoreAttributes

protected void restoreAttributes(Dataset Data)

Description copied from class: BaseDatasetNormalizer

Perform the actual restoration on the attributes

Specified by:

restoreAttributes in class BaseDatasetNormalizer

Parameters:

Data - Dataset to be transformed

restoreClassVariable

protected void restoreClassVariable(Dataset Data)

Description copied from class: BaseDatasetNormalizer

Perform the actual restoration on the class variables (measured and predicted!)

Specified by:

restoreClassVariable in class BaseDatasetNormalizer

Parameters:

Data - Dataset to be transformed