org.nd4j.linalg.dataset.api.iterator.SamplingDataSetIterator Maven / Gradle / Ivy
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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
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*/
package org.nd4j.linalg.dataset.api.iterator;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.DataSetPreProcessor;
import java.util.List;
/**
* A wrapper for a dataset to sample from.
* This will randomly sample from the given dataset.
*
* @author Adam Gibson
*/
public class SamplingDataSetIterator implements DataSetIterator {
private DataSet sampleFrom;
private int batchSize;
private int totalNumberSamples;
private int numTimesSampled;
private boolean replace = false;
private DataSetPreProcessor preProcessor;
/**
* @param sampleFrom the dataset to sample from
* @param batchSize the batch size to sample
* @param totalNumberSamples the sample size
*/
public SamplingDataSetIterator(DataSet sampleFrom, int batchSize, int totalNumberSamples, boolean replace) {
super();
this.sampleFrom = sampleFrom;
this.batchSize = batchSize;
this.totalNumberSamples = totalNumberSamples;
this.replace = replace;
}
/**
* @param sampleFrom the dataset to sample from
* @param batchSize the batch size to sample
* @param totalNumberSamples the sample size
*/
public SamplingDataSetIterator(DataSet sampleFrom, int batchSize, int totalNumberSamples) {
super();
this.sampleFrom = sampleFrom;
this.batchSize = batchSize;
this.totalNumberSamples = totalNumberSamples;
}
@Override
public boolean hasNext() {
return numTimesSampled < totalNumberSamples;
}
@Override
public DataSet next() {
DataSet ret = sampleFrom.sample(batchSize, replace);
numTimesSampled += batchSize;
if (preProcessor != null) {
preProcessor.preProcess(ret);
}
return ret;
}
@Override
public void remove() {
throw new UnsupportedOperationException();
}
@Override
public int inputColumns() {
return sampleFrom.numInputs();
}
@Override
public int totalOutcomes() {
return sampleFrom.numOutcomes();
}
@Override
public boolean resetSupported() {
return true;
}
@Override
public boolean asyncSupported() {
//Aleady in memory -> async prefetching doesn't make sense here
return false;
}
@Override
public void reset() {
numTimesSampled = 0;
}
@Override
public int batch() {
return batchSize;
}
/**
* Set a pre processor
*
* @param preProcessor a pre processor to set
*/
@Override
public void setPreProcessor(DataSetPreProcessor preProcessor) {
this.preProcessor = preProcessor;
}
@Override
public DataSetPreProcessor getPreProcessor() {
return preProcessor;
}
@Override
public List getLabels() {
return null;
}
@Override
public DataSet next(int num) {
DataSet ret = sampleFrom.sample(num);
numTimesSampled++;
return ret;
}
}