smile.feature

Class Normalizer

java.lang.Object

smile.feature.FeatureTransform

smile.feature.Normalizer

All Implemented Interfaces:

java.io.Serializable

public class Normalizer
extends FeatureTransform

Normalize samples individually to unit norm. Each sample (i.e. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (L1 or L2) equals one.

Scaling inputs to unit norms is a common operation for text classification or clustering for instance.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Normalizer.Norm The types of data scaling.

Field Summary

Fields inherited from class smile.feature.FeatureTransform

copy

Constructor Summary

Constructors

Constructor and Description
Normalizer() Default constructor with L2 norm.
Normalizer(boolean copy) Constructor with L2 norm.
Normalizer(Normalizer.Norm norm) Constructor.
Normalizer(Normalizer.Norm norm, boolean copy) Constructor.

Method Summary

Modifier and Type	Method and Description
void	learn(smile.data.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

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

Constructor Detail

Normalizer

public Normalizer()

Default constructor with L2 norm.

Normalizer

public Normalizer(boolean copy)

Constructor with L2 norm.

Parameters:

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

Normalizer

public Normalizer(Normalizer.Norm norm)

Constructor.

Parameters:

norm - The norm to use to normalize each non zero sample.

Normalizer

public Normalizer(Normalizer.Norm norm,
                  boolean copy)

Constructor.

Parameters:

norm - The norm to use to normalize each non zero sample.

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

Method Detail

learn

public void learn(smile.data.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