Uses of Package
jsat.datatransform

Packages that use jsat.datatransform

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

Classes in jsat.datatransform used by jsat

Class and Description
DataTransform A pre-processing step may be desirable before training.

Classes in jsat.datatransform used by jsat.classifiers

Class and Description
DataTransformProcess Performing a transform on the whole data set before training a classifier can add bias to the results.

Classes in jsat.datatransform used by jsat.classifiers.neuralnetwork

Class and Description
DataTransform A pre-processing step may be desirable before training.

Classes in jsat.datatransform used by jsat.datatransform

Class and Description
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.
DataModelPipeline A Data Model Pipeline combines several data transforms and a base Classifier or Regressor into a unified object for performing classification and Regression with.
DataTransform A pre-processing step may be desirable before training.
DataTransformBase This abstract class implements the Parameterized interface to ease the development of simple Data Transforms.
DataTransformProcess Performing a transform on the whole data set before training a classifier can add bias to the results.
DenseSparceTransform Dense sparce transform alters the vectors that store the numerical values.
FastICA Provides an implementation of the FastICA algorithm for Independent Component Analysis (ICA).
FastICA.DefaultNegEntropyFunc A set of default negative entropy functions as specified in the original FastICA paper
FastICA.NegEntropyFunc The FastICA algorithm requires a function f(x) to be used iteratively in the algorithm, but only makes use of the first and second derivatives of the algorithm.
Imputer Imputes missing values in a dataset by finding reasonable default values.
Imputer.NumericImputionMode
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.
InPlaceTransform An In Place Transform is one that has the same number of categorical and numeric features as the input.
InsertMissingValuesTransform This transform mostly exists for testing code.
InverseOfTransform Creates a new Transform object that simply uses the inverse of an InvertibleTransform as a regular transform.
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.
JLTransform The Johnson-Lindenstrauss (JL) Transform is a type of random projection down to a lower dimensional space.
JLTransform.TransformMode Determines which distribution to construct the transform matrix from
LinearTransform This class transforms all numerical values into a specified range by a linear scaling of all the data point values.
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.
NumericalToHistogram This transform converts numerical features into categorical ones via a simple histogram.
PNormNormalization PNormNormalization transformation performs normalizations of a vector x by one its p-norms where p is in (0, Infinity)
RemoveAttributeTransform This Data Transform allows the complete removal of specific features from the data set.
StandardizeTransform This transform performs standardization of the data, which makes each column have a mean of zero and a variance of one.
WhitenedPCA An extension of PCA that attempts to capture the variance, and make the variables in the output space independent from each-other.
ZeroMeanTransform A transformation to shift all numeric variables so that their mean is zero

Classes in jsat.datatransform used by jsat.datatransform.featureselection

Class and Description
DataTransform A pre-processing step may be desirable before training.
RemoveAttributeTransform This Data Transform allows the complete removal of specific features from the data set.

Classes in jsat.datatransform used by jsat.datatransform.kernel

Class and Description
DataTransform A pre-processing step may be desirable before training.
DataTransformBase This abstract class implements the Parameterized interface to ease the development of simple Data Transforms.

Classes in jsat.datatransform used by jsat.regression

Class and Description
DataTransformProcess Performing a transform on the whole data set before training a classifier can add bias to the results.

Classes in jsat.datatransform used by jsat.text

Class and Description
RemoveAttributeTransform This Data Transform allows the complete removal of specific features from the data set.