smile.feature

Class MaxAbsScaler

java.lang.Object

smile.feature.FeatureTransform

smile.feature.MaxAbsScaler

public class MaxAbsScaler
extends FeatureTransform

Scales each feature by its maximum absolute value. This class scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.

Constructor Summary

Constructors

Constructor and Description
MaxAbsScaler() Constructor.
MaxAbsScaler(boolean copy) Constructor.

Method Summary

Modifier and Type	Method and Description
void	learn(Attribute[] attributes, double[][] data) Learns transformation parameters from a dataset.
java.lang.String	toString()
double[]	transform(double[] x) Scales each feature by its maximum absolute value.

Methods inherited from class smile.feature.FeatureTransform

learn, transform

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

MaxAbsScaler

public MaxAbsScaler()

Constructor.

MaxAbsScaler

public MaxAbsScaler(boolean copy)

Constructor.

Parameters:

copy - If false, try to avoid a copy and do inplace scaling instead.

Method Detail

learn

public void learn(Attribute[] attributes,
                  double[][] data)

Description copied from class: FeatureTransform

Learns transformation parameters from a dataset.

Specified by:

learn in class FeatureTransform

Parameters:

attributes - The variable attributes. Of which, numeric variables will be standardized.

data - The training data to learn scaling parameters. The data will not be modified.

transform

public double[] transform(double[] x)

Scales each feature by its maximum absolute value.

Specified by:

transform in class FeatureTransform

Parameters:

x - a vector to be scaled. The vector will be modified on output.

Returns:

the input vector.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object