jsat.datatransform

Class StandardizeTransform

java.lang.Object

jsat.datatransform.StandardizeTransform

All Implemented Interfaces:

Serializable, Cloneable, DataTransform, InPlaceTransform

public class StandardizeTransform
extends Object
implements InPlaceTransform

This transform performs standardization of the data, which makes each column have a mean of zero and a variance of one. This assume the data comes from a normal distribution and scales it to the unit normal distribution.

This transform is equivalent to applying ZeroMeanTransform followed by UnitVarianceTransform.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
StandardizeTransform() Creates a new object for Standardizing datasets
StandardizeTransform(DataSet dataset) Creates a new object for standaidizing datasets fit to the given dataset
StandardizeTransform(StandardizeTransform toCopy) Copy constructor

Method Summary

Modifier and Type	Method and Description
StandardizeTransform	clone()
void	fit(DataSet dataset) Fits this transform to the given dataset.
void	mutableTransform(DataPoint dp) Mutates the given data point.
boolean	mutatesNominal() By default returns false.
DataPoint	transform(DataPoint dp) Returns a new data point that is a transformation of the original data point.

Methods inherited from class java.lang.Object

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

Constructor Detail

StandardizeTransform

public StandardizeTransform()

Creates a new object for Standardizing datasets

StandardizeTransform

public StandardizeTransform(DataSet dataset)

Creates a new object for standaidizing datasets fit to the given dataset

Parameters:

dataset - the dataset to learn how to standardize from

StandardizeTransform

public StandardizeTransform(StandardizeTransform toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

fit

public void fit(DataSet dataset)

Description copied from interface: DataTransform

Fits this transform to the given dataset. Some transforms can only be learned from classification or regression datasets. If an incompatible dataset type is given, a FailedToFitException exception may be thrown.

Specified by:

fit in interface DataTransform

Parameters:

dataset - the dataset to fir this transform to

transform

public DataPoint transform(DataPoint dp)

Description copied from interface: DataTransform

Returns a new data point that is a transformation of the original data point. This new data point is a different object, but may contain the same references as the original data point. It is not guaranteed that you can mutate the transformed point without having a side effect on the original point.

Specified by:

transform in interface DataTransform

Parameters:

dp - the data point to apply a transformation to

Returns:

a transformed data point

mutableTransform

public void mutableTransform(DataPoint dp)

Description copied from interface: InPlaceTransform

Mutates the given data point. This causes side effects, altering the data point to have the same value as the output of DataTransform.transform(jsat.classifiers.DataPoint).

Specified by:

mutableTransform in interface InPlaceTransform

Parameters:

dp - the data point to alter

mutatesNominal

public boolean mutatesNominal()

Description copied from interface: InPlaceTransform

By default returns false. Only returns true if this transform will mutableTransform the nominal feature values of a data point.

Specified by:

mutatesNominal in interface InPlaceTransform

Returns:

true if nominal feature values are mutated, false otherwise.

clone

public StandardizeTransform clone()

Specified by:

clone in interface DataTransform

Overrides:

clone in class Object