jsat.datatransform

Class LinearTransform

java.lang.Object

jsat.datatransform.LinearTransform

All Implemented Interfaces:

Serializable, Cloneable, DataTransform, InPlaceInvertibleTransform, InPlaceTransform, InvertibleTransform

public class LinearTransform
extends Object
implements InPlaceInvertibleTransform

This class transforms all numerical values into a specified range by a linear scaling of all the data point values.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LinearTransform() Creates a new Linear Transformation that will scale values to the [0, 1] range.
LinearTransform(DataSet dataSet) Creates a new Linear Transformation for the input data set so that all values are in the [0, 1] range.
LinearTransform(DataSet dataSet, double A, double B) Creates a new Linear Transformation for the input data set.
LinearTransform(double A, double B) Creates a new Linear Transformation.

Method Summary

Modifier and Type	Method and Description
LinearTransform	clone()
void	fit(DataSet dataSet) Fits this transform to the given dataset.
DataPoint	inverse(DataPoint dp) Applies the inverse or "reverse" transform to approximately undo the effect of DataTransform.transform(jsat.classifiers.DataPoint) to recover an approximation of the original data point.
void	mutableInverse(DataPoint dp) Mutates the given data point.
void	mutableTransform(DataPoint dp) Mutates the given data point.
boolean	mutatesNominal() By default returns false.
void	setRange(double A, double B) Sets the min and max value to scale the data to.
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

LinearTransform

public LinearTransform()

Creates a new Linear Transformation that will scale values to the [0, 1] range.

LinearTransform

public LinearTransform(DataSet dataSet)

Creates a new Linear Transformation for the input data set so that all values are in the [0, 1] range.

Parameters:

dataSet - the data set to learn the transform from

LinearTransform

public LinearTransform(double A,
                       double B)

Creates a new Linear Transformation.

Parameters:

dataSet - the data set to learn the transform from

A - the maximum value for the transformed data set

B - the minimum value for the transformed data set

LinearTransform

public LinearTransform(DataSet dataSet,
                       double A,
                       double B)

Creates a new Linear Transformation for the input data set.

Parameters:

dataSet - the data set to learn the transform from

A - the maximum value for the transformed data set

B - the minimum value for the transformed data set

Method Detail

setRange

public void setRange(double A,
                     double B)

Sets the min and max value to scale the data to. If given in the wrong order, this method will swap them

Parameters:

A - the maximum value for the transformed data set

B - the minimum value for the transformed data set

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

clone

public LinearTransform clone()

Specified by:

clone in interface DataTransform

Specified by:

clone in interface InPlaceInvertibleTransform

Specified by:

clone in interface InvertibleTransform

Overrides:

clone in class Object

mutableInverse

public void mutableInverse(DataPoint dp)

Description copied from interface: InPlaceInvertibleTransform

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

Specified by:

mutableInverse in interface InPlaceInvertibleTransform

Parameters:

dp - the data point to alter with an inverse transformation

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.

inverse

public DataPoint inverse(DataPoint dp)

Description copied from interface: InvertibleTransform

Applies the inverse or "reverse" transform to approximately undo the effect of DataTransform.transform(jsat.classifiers.DataPoint) to recover an approximation of the original data point.

Specified by:

inverse in interface InvertibleTransform

Parameters:

dp - the transformed data point

Returns:

the original data point, or a reasonable approximation