/*
* 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.strategy;
import org.encog.EncogError;
import org.encog.ml.MLEncodable;
import org.encog.ml.train.MLTrain;
import org.encog.ml.train.strategy.Strategy;
import org.encog.util.EngineArray;
public class RegularizationStrategy implements Strategy {
private double lambda; // Weight decay
private MLTrain train;
private double[] weights;
private double[] newWeights;
private MLEncodable encodable;
public RegularizationStrategy(double lambda) {
this.lambda = lambda;
}
@Override
public void init(MLTrain train) {
this.train = train;
if( !(train.getMethod() instanceof MLEncodable) ) {
throw new EncogError("Method must implement MLEncodable to be used with regularization.");
}
this.encodable = ((MLEncodable)train.getMethod());
this.weights = new double[this.encodable.encodedArrayLength()];
this.newWeights = new double[this.encodable.encodedArrayLength()];
}
@Override
public void preIteration() {
((MLEncodable)train.getMethod()).encodeToArray(weights);
}
@Override
public void postIteration() {
this.encodable.encodeToArray(newWeights);
for (int i = 0; i < newWeights.length; i++) {
newWeights[i] -= lambda * weights[i];
}
this.encodable.decodeFromArray(newWeights);
EngineArray.arrayCopy(newWeights, weights);
}
}