org.nd4j.linalg.dataset.api.preprocessor.serializer.MinMaxSerializerStrategy Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
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* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.dataset.api.preprocessor.serializer;
import lombok.NonNull;
import org.nd4j.linalg.dataset.api.preprocessor.NormalizerMinMaxScaler;
import org.nd4j.linalg.factory.Nd4j;
import java.io.*;
public class MinMaxSerializerStrategy implements NormalizerSerializerStrategy {
@Override
public void write(@NonNull NormalizerMinMaxScaler normalizer, @NonNull OutputStream stream) throws IOException {
try (DataOutputStream dos = new DataOutputStream(stream)) {
dos.writeBoolean(normalizer.isFitLabel());
dos.writeDouble(normalizer.getTargetMin());
dos.writeDouble(normalizer.getTargetMax());
Nd4j.write(normalizer.getMin(), dos);
Nd4j.write(normalizer.getMax(), dos);
if (normalizer.isFitLabel()) {
Nd4j.write(normalizer.getLabelMin(), dos);
Nd4j.write(normalizer.getLabelMax(), dos);
}
dos.flush();
}
}
@Override
public NormalizerMinMaxScaler restore(@NonNull InputStream stream) throws IOException {
DataInputStream dis = new DataInputStream(stream);
boolean fitLabels = dis.readBoolean();
double targetMin = dis.readDouble();
double targetMax = dis.readDouble();
NormalizerMinMaxScaler result = new NormalizerMinMaxScaler(targetMin, targetMax);
result.fitLabel(fitLabels);
result.setFeatureStats(Nd4j.read(dis), Nd4j.read(dis));
if (fitLabels) {
result.setLabelStats(Nd4j.read(dis), Nd4j.read(dis));
}
return result;
}
@Override
public NormalizerType getSupportedType() {
return NormalizerType.MIN_MAX;
}
}