Uses of Interface
jsat.datatransform.DataTransform

Packages that use DataTransform

Package	Description
jsat
jsat.classifiers.neuralnetwork
jsat.datatransform
jsat.datatransform.featureselection
jsat.datatransform.kernel

Uses of DataTransform in jsat

Methods in jsat with parameters of type DataTransform

Modifier and Type	Method and Description
void	DataSet.applyTransform(DataTransform dt) Applies the given transformation to all points in this data set, replacing each data point with the new value.
void	DataSet.applyTransform(DataTransform dt, boolean mutate) Applies the given transformation to all points in this data set.
void	DataSet.applyTransform(DataTransform dt, boolean mutate, ExecutorService ex) Applies the given transformation to all points in this data set in parallel.
void	DataSet.applyTransform(DataTransform dt, ExecutorService ex) Applies the given transformation to all points in this data set in parallel, replacing each data point with the new value.

Uses of DataTransform in jsat.classifiers.neuralnetwork

Classes in jsat.classifiers.neuralnetwork that implement DataTransform

Modifier and Type	Class and Description
class	RBFNet This provides a highly configurable implementation of a Radial Basis Function Neural Network.

Uses of DataTransform in jsat.datatransform

Subinterfaces of DataTransform in jsat.datatransform

Modifier and Type	Interface and Description
interface	InPlaceInvertibleTransform This interface behaves exactly as InPlaceTransform specifies, with the addition of an in-place "reverse" method that can be used to alter any given transformed data point back into an approximation of the original vector, without having to new vector object, but altering the one given.
interface	InPlaceTransform An In Place Transform is one that has the same number of categorical and numeric features as the input.
interface	InvertibleTransform A InvertibleTransform is one in which any given transformed vector can be inverse to recover an approximation of the original vector when using a transform that implements this interface.

Classes in jsat.datatransform that implement DataTransform

Modifier and Type	Class and Description
class	AutoDeskewTransform This transform applies a shifted Box-Cox transform for several fixed values of λ, and selects the one that provides the greatest reduction in the skewness of the distribution.
class	DataTransformBase This abstract class implements the Parameterized interface to ease the development of simple Data Transforms.
class	DataTransformProcess Performing a transform on the whole data set before training a classifier can add bias to the results.
class	DenseSparceTransform Dense sparce transform alters the vectors that store the numerical values.
class	FastICA Provides an implementation of the FastICA algorithm for Independent Component Analysis (ICA).
class	Imputer Imputes missing values in a dataset by finding reasonable default values.
class	InsertMissingValuesTransform This transform mostly exists for testing code.
class	InverseOfTransform Creates a new Transform object that simply uses the inverse of an InvertibleTransform as a regular transform.
class	JLTransform The Johnson-Lindenstrauss (JL) Transform is a type of random projection down to a lower dimensional space.
class	LinearTransform This class transforms all numerical values into a specified range by a linear scaling of all the data point values.
class	NominalToNumeric This transform converts nominal feature values to numeric ones be adding a new numeric feature for each possible categorical value for each nominal feature.
class	NumericalToHistogram This transform converts numerical features into categorical ones via a simple histogram.
class	PCA Principle Component Analysis is a method that attempts to create a basis of the given space that maintains the variance in the data set while eliminating correlation of the variables.
class	PNormNormalization PNormNormalization transformation performs normalizations of a vector x by one its p-norms where p is in (0, Infinity)
class	PolynomialTransform A transform for applying a polynomial transformation on the data set.
class	RemoveAttributeTransform This Data Transform allows the complete removal of specific features from the data set.
class	StandardizeTransform This transform performs standardization of the data, which makes each column have a mean of zero and a variance of one.
class	UnitVarianceTransform Creates a transform to alter data points so that each attribute has a standard deviation of 1, which means a variance of 1.
class	WhitenedPCA An extension of PCA that attempts to capture the variance, and make the variables in the output space independent from each-other.
class	WhitenedZCA An extension of WhitenedPCA, is the Whitened Zero Component Analysis.
class	ZeroMeanTransform A transformation to shift all numeric variables so that their mean is zero

Methods in jsat.datatransform that return DataTransform

Modifier and Type	Method and Description
DataTransform	WhitenedPCA.clone()
DataTransform	UnitVarianceTransform.clone()
DataTransform	PolynomialTransform.clone()
DataTransform	PCA.clone()
DataTransform	JLTransform.clone()
abstract DataTransform	DataTransformBase.clone()
DataTransform	DataTransform.clone()

Methods in jsat.datatransform with parameters of type DataTransform

Modifier and Type	Method and Description
void	DataTransformProcess.addTransform(DataTransform transform) Adds a transform to the list of transforms.

Constructors in jsat.datatransform with parameters of type DataTransform

Constructor and Description
DataModelPipeline(Classifier baseClassifier, DataTransform... transforms) Creates a new Data Model Pipeline from the given transform factories and base classifier
DataModelPipeline(Regressor baseRegressor, DataTransform... transforms) Creates a new Data Model Pipeline from the given transform factories and base classifier
DataTransformProcess(DataTransform... transforms) Creates a new transform process from the listed factories, which will be applied in order by index.

Uses of DataTransform in jsat.datatransform.featureselection

Classes in jsat.datatransform.featureselection that implement DataTransform

Modifier and Type	Class and Description
class	BDS Bidirectional Search (BDS) is a greedy method of selecting a subset of features to use for prediction.
class	LRS plus-L minus-R Selection (LRS) is a greedy method of selecting a subset of features to use for prediction.
class	MutualInfoFS Performs greedy feature selection based on Mutual Information of the features with respect to the class values.
class	ReliefF Provides an implementation of the ReliefF algorithm for feature importance computing.
class	SBS Sequential Backward Selection (SBS) is a greedy method of selecting a subset of features to use for prediction.
class	SFS Sequential Forward Selection (SFS) is a greedy method of selecting a subset of features to use for prediction.

Uses of DataTransform in jsat.datatransform.kernel

Classes in jsat.datatransform.kernel that implement DataTransform

Modifier and Type	Class and Description
class	KernelPCA A kernelized implementation of PCA.
class	Nystrom An implementation of the Nystrom approximation for any Kernel Trick.
class	RFF_RBF An Implementation of Random Fourier Features for the RBFKernel.