/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://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.
*
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package org.encog.neural.networks.training.cross;
import org.encog.ml.MLMethod;
import org.encog.ml.TrainingImplementationType;
import org.encog.ml.data.folded.FoldedDataSet;
import org.encog.ml.train.BasicTraining;
/**
* Base class for cross training trainers. Must use a folded dataset.
*/
public abstract class CrossTraining extends BasicTraining {
/**
* The network to train.
*/
private final MLMethod network;
/**
* The folded dataset.
*/
private final FoldedDataSet folded;
/**
* Construct a cross trainer.
* @param network The network.
* @param training The training data.
*/
public CrossTraining(final MLMethod network,
final FoldedDataSet training) {
super(TrainingImplementationType.Iterative);
this.network = network;
setTraining(training);
this.folded = training;
}
/**
* @return The folded training data.
*/
public FoldedDataSet getFolded() {
return this.folded;
}
/**
* {@inheritDoc}
*/
@Override
public MLMethod getMethod() {
return this.network;
}
}