64 lines
2.6 KiB
Java
64 lines
2.6 KiB
Java
/*
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* ******************************************************************************
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* *
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* *
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* * This program and the accompanying materials are made available under the
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* * terms of the Apache License, Version 2.0 which is available at
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* * https://www.apache.org/licenses/LICENSE-2.0.
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* *
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* * See the NOTICE file distributed with this work for additional
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* * information regarding copyright ownership.
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* * Unless required by applicable law or agreed to in writing, software
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* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * License for the specific language governing permissions and limitations
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* * under the License.
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* *
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* * SPDX-License-Identifier: Apache-2.0
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* *****************************************************************************
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*/
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package org.deeplearning4j.datasets.iterator;
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import org.deeplearning4j.BaseDL4JTest;
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import org.junit.jupiter.api.Test;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.api.MultiDataSet;
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import org.nd4j.linalg.dataset.api.MultiDataSetPreProcessor;
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import org.nd4j.linalg.dataset.api.preprocessor.MultiNormalizerMinMaxScaler;
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import org.nd4j.linalg.factory.Nd4j;
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import static org.junit.jupiter.api.Assertions.assertEquals;
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public class CombinedPreProcessorTests extends BaseDL4JTest {
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@Test
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public void somePreProcessorsCombined() {
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INDArray[] featureArr = new INDArray[] {Nd4j.linspace(100, 200, 20).reshape(10, 2)};
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org.nd4j.linalg.dataset.MultiDataSet multiDataSet =
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new org.nd4j.linalg.dataset.MultiDataSet(featureArr, null, null, null);
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MultiNormalizerMinMaxScaler minMaxScaler = new MultiNormalizerMinMaxScaler();
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minMaxScaler.fit(multiDataSet);
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CombinedMultiDataSetPreProcessor multiDataSetPreProcessor = new CombinedMultiDataSetPreProcessor.Builder()
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.addPreProcessor(minMaxScaler).addPreProcessor(1, new addFivePreProcessor()).build();
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multiDataSetPreProcessor.preProcess(multiDataSet);
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assertEquals(Nd4j.zeros(10, 2).addColumnVector(Nd4j.linspace(0, 1, 10).reshape(10, 1)).addi(5),
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multiDataSet.getFeatures(0));
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}
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/*
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Adds five to the features - assumes multidataset here is one feature and one label
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*/
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public final class addFivePreProcessor implements MultiDataSetPreProcessor {
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@Override
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public void preProcess(MultiDataSet multiDataSet) {
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multiDataSet.getFeatures(0).addi(5);
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}
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}
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}
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