/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.model.config;
import org.encog.EncogError;
import org.encog.ml.data.versatile.VersatileMLDataSet;
import org.encog.ml.data.versatile.columns.ColumnType;
import org.encog.ml.data.versatile.normalizers.IndexedNormalizer;
import org.encog.ml.data.versatile.normalizers.OneOfNNormalizer;
import org.encog.ml.data.versatile.normalizers.RangeNormalizer;
import org.encog.ml.data.versatile.normalizers.strategies.BasicNormalizationStrategy;
import org.encog.ml.data.versatile.normalizers.strategies.NormalizationStrategy;
import org.encog.ml.factory.MLMethodFactory;
import org.encog.ml.factory.MLTrainFactory;
/**
* Config class for EncogModel to use a PNN neural network.
*/
public class PNNConfig implements MethodConfig {
/**
* {@inheritDoc}
*/
@Override
public String getMethodName() {
return MLMethodFactory.TYPE_PNN;
}
/**
* {@inheritDoc}
*/
@Override
public String suggestModelArchitecture(VersatileMLDataSet dataset) {
return ("?->C(kernel=gaussian)->?");
}
/**
* {@inheritDoc}
*/
@Override
public NormalizationStrategy suggestNormalizationStrategy(VersatileMLDataSet dataset, String architecture) {
int outputColumns = dataset.getNormHelper().getOutputColumns().size();
if( outputColumns>1 ) {
throw new EncogError("PNN does not support multiple output columns.");
}
ColumnType ct = dataset.getNormHelper().getOutputColumns().get(0).getDataType();
BasicNormalizationStrategy result = new BasicNormalizationStrategy();
result.assignInputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
result.assignInputNormalizer(ColumnType.nominal,new OneOfNNormalizer(0,1));
result.assignInputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
result.assignOutputNormalizer(ColumnType.continuous,new RangeNormalizer(0,1));
result.assignOutputNormalizer(ColumnType.nominal,new IndexedNormalizer());
result.assignOutputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
return result;
}
/**
* {@inheritDoc}
*/
@Override
public String suggestTrainingType() {
return MLTrainFactory.TYPE_PNN;
}
/**
* {@inheritDoc}
*/
@Override
public String suggestTrainingArgs(String trainingType) {
return "";
}
/**
* {@inheritDoc}
*/
@Override
public int determineOutputCount(VersatileMLDataSet dataset) {
return dataset.getNormHelper().getOutputColumns().get(0).getClasses().size();
}
}