smile.feature

Class Standardizer

java.lang.Object

smile.feature.FeatureTransform

smile.feature.Standardizer

Direct Known Subclasses:

RobustStandardizer

public class Standardizer
extends FeatureTransform

Standardizes numeric feature to 0 mean and unit variance. Standardization makes an assumption that the data follows a Gaussian distribution and are also not robust when outliers present. A robust alternative is to subtract the median and divide by the IQR by RobustStandardizer.

Constructor Summary

Constructors

Constructor and Description
Standardizer() Constructor.
Standardizer(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) Transform a feature vector.

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

Standardizer

public Standardizer()

Constructor.

Standardizer

public Standardizer(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)

Description copied from class: FeatureTransform

Transform a feature vector.

Specified by:

transform in class FeatureTransform

Parameters:

x - a feature vector.

Returns:

the transformed feature value.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object