org.nd4j.linalg.dataset.api.preprocessor.DataNormalization Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.dataset.api.preprocessor;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.DataSetPreProcessor;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
/**
* An interface for data normalizers.
* Data normalizers compute some sort of statistics
* over a dataset and scale the data in some way.
*
* @author Adam Gibson
*/
public interface DataNormalization extends Normalizer, DataSetPreProcessor {
/**
* Iterates over a dataset
* accumulating statistics for normalization
* @param iterator the iterator to use for
* collecting statistics.
*/
void fit(DataSetIterator iterator);
@Override
void preProcess(DataSet toPreProcess);
/**
* Transform the dataset
* @param features the features to pre process
*/
void transform(INDArray features);
/**
* Transform the features, with an optional mask array
* @param features the features to pre process
* @param featuresMask the mask array to pre process
*/
void transform(INDArray features, INDArray featuresMask);
/**
* Transform the labels. If {@link #isFitLabel()} == false, this is a no-op
*/
void transformLabel(INDArray labels);
/**
* Transform the labels. If {@link #isFitLabel()} == false, this is a no-op
*/
void transformLabel(INDArray labels, INDArray labelsMask);
/**
* Undo (revert) the normalization applied by this DataNormalization instance to the specified features array
*
* @param features Features to revert the normalization on
*/
void revertFeatures(INDArray features);
/**
* Undo (revert) the normalization applied by this DataNormalization instance to the specified features array
*
* @param features Features to revert the normalization on
* @param featuresMask
*/
void revertFeatures(INDArray features, INDArray featuresMask);
/**
* Undo (revert) the normalization applied by this DataNormalization instance to the specified labels array.
* If labels normalization is disabled (i.e., {@link #isFitLabels()} == false) then this is a no-op.
* Can also be used to undo normalization for network output arrays, in the case of regression.
*
* @param labels Labels array to revert the normalization on
*/
void revertLabels(INDArray labels);
/**
* Undo (revert) the normalization applied by this DataNormalization instance to the specified labels array.
* If labels normalization is disabled (i.e., {@link #isFitLabels()} == false) then this is a no-op.
* Can also be used to undo normalization for network output arrays, in the case of regression.
*
* @param labels Labels array to revert the normalization on
* @param labelsMask Labels mask array (may be null)
*/
void revertLabels(INDArray labels, INDArray labelsMask);
/**
* Flag to specify if the labels/outputs in the dataset should be also normalized. Default value is usually false.
*/
void fitLabel(boolean fitLabels);
/**
* Whether normalization for the labels is also enabled. Most commonly used for regression, not classification.
*
* @return True if labels will be
*/
boolean isFitLabel();
}