| Package | Description |
|---|---|
| org.nd4j.linalg.dataset.api.preprocessor | |
| org.nd4j.linalg.dataset.api.preprocessor.serializer |
| Class and Description |
|---|
| AbstractDataSetNormalizer
Abstract base class for normalizers
that act upon
DataSet instances
or iterators |
| AbstractMultiDataSetNormalizer
Abstract base class for normalizers that act upon
MultiDataSet instances or iterators |
| AbstractNormalizer
Abstract base class for normalizers for both DataSet and MultiDataSet processing
|
| DataNormalization
An interface for data normalizers.
|
| MultiDataNormalization
An interface for multi dataset normalizers.
|
| MultiNormalizerHybrid
Pre processor for MultiDataSet that can be configured to use different normalization strategies for different inputs
and outputs, or none at all.
|
| Normalizer
Base interface for all normalizers
|
| NormalizerStrategy
Interface for strategies that can normalize and denormalize data arrays based on statistics of the population
|
| Class and Description |
|---|
| MultiNormalizerHybrid
Pre processor for MultiDataSet that can be configured to use different normalization strategies for different inputs
and outputs, or none at all.
|
| MultiNormalizerMinMaxScaler
Pre processor for MultiDataSet that normalizes feature values (and optionally label values) to lie between a minimum
and maximum value (by default between 0 and 1)
|
| MultiNormalizerStandardize
Pre processor for MultiDataSet that normalizes feature values (and optionally label values) to have 0 mean and
a standard deviation of 1
|
| Normalizer
Base interface for all normalizers
|
| NormalizerMinMaxScaler
Pre processor for DataSets that normalizes feature values (and optionally label values) to lie between a minimum
and maximum value (by default between 0 and 1)
|
| NormalizerStandardize
Created by susaneraly, Ede Meijer
variance and mean
Pre processor for DataSet that normalizes feature values (and optionally label values) to have 0 mean and a standard
deviation of 1
|
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