org.nd4j.linalg.dataset.api.preprocessor.MinMaxStrategy Maven / Gradle / Ivy
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* 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 lombok.EqualsAndHashCode;
import lombok.Getter;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastAddOp;
import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastDivOp;
import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastMulOp;
import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastSubOp;
import org.nd4j.linalg.dataset.api.DataSetUtil;
import org.nd4j.linalg.dataset.api.preprocessor.stats.MinMaxStats;
import org.nd4j.linalg.dataset.api.preprocessor.stats.NormalizerStats;
import org.nd4j.linalg.factory.Nd4j;
import java.io.Serializable;
/**
* {@link NormalizerStrategy} implementation that will normalize and denormalize data arrays to a given range, based on
* statistics of the upper and lower bounds of the population
*
* @author Ede Meijer
*/
@Getter
@EqualsAndHashCode
public class MinMaxStrategy implements NormalizerStrategy, Serializable {
private double minRange;
private double maxRange;
public MinMaxStrategy() {
this(0, 1);
}
/**
* @param minRange the target range lower bound
* @param maxRange the target range upper bound
*/
public MinMaxStrategy(double minRange, double maxRange) {
this.minRange = minRange;
this.maxRange = Math.max(maxRange, minRange + Nd4j.EPS_THRESHOLD);
}
/**
* Normalize a data array
*
* @param array the data to normalize
* @param stats statistics of the data population
*/
@Override
public void preProcess(INDArray array, INDArray maskArray, MinMaxStats stats) {
if (array.rank() <= 2) {
array.subiRowVector(stats.getLower().castTo(array.dataType()));
array.diviRowVector(stats.getRange().castTo(array.dataType()));
}
// if feature Rank is 3 (time series) samplesxfeaturesxtimesteps
// if feature Rank is 4 (images) samplesxchannelsxrowsxcols
// both cases operations should be carried out in dimension 1
else {
Nd4j.getExecutioner().execAndReturn(new BroadcastSubOp(array, stats.getLower().castTo(array.dataType()), array, 1));
Nd4j.getExecutioner().execAndReturn(new BroadcastDivOp(array, stats.getRange().castTo(array.dataType()), array, 1));
}
// Scale by target range
array.muli(maxRange - minRange);
// Add target range minimum values
array.addi(minRange);
if (maskArray != null) {
DataSetUtil.setMaskedValuesToZero(array, maskArray);
}
}
/**
* Denormalize a data array
*
* @param array the data to denormalize
* @param stats statistics of the data population
*/
@Override
public void revert(INDArray array, INDArray maskArray, MinMaxStats stats) {
// Subtract target range minimum value
array.subi(minRange);
// Scale by target range
array.divi(maxRange - minRange);
if (array.rank() <= 2) {
array.muliRowVector(stats.getRange());
array.addiRowVector(stats.getLower());
} else {
Nd4j.getExecutioner().execAndReturn(new BroadcastMulOp(array, stats.getRange().castTo(array.dataType()), array, 1));
Nd4j.getExecutioner().execAndReturn(new BroadcastAddOp(array, stats.getLower().castTo(array.dataType()), array, 1));
}
if (maskArray != null) {
DataSetUtil.setMaskedValuesToZero(array, maskArray);
}
}
/**
* Create a new {@link NormalizerStats.Builder} instance that can be used to fit new data and of the opType that
* belongs to the current NormalizerStrategy implementation
*
* @return the new builder
*/
@Override
public NormalizerStats.Builder newStatsBuilder() {
return new MinMaxStats.Builder();
}
}