/*
 * 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.
 *   
 * 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.ml.data.versatile.VersatileMLDataSet;
import org.encog.ml.data.versatile.columns.ColumnType;
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;

/**
 * Config class for EncogModel to use a RBF neural network.
 */
public class RBFNetworkConfig implements MethodConfig {
	
	/**
	 * {@inheritDoc}
	 */
	@Override
	public String getMethodName() {
		return MLMethodFactory.TYPE_RBFNETWORK;
	}
	
	/**
	 * {@inheritDoc}
	 */
	@Override
	public String suggestModelArchitecture(VersatileMLDataSet dataset) {
		int inputColumns = dataset.getNormHelper().getInputColumns().size();
		int outputColumns = dataset.getNormHelper().getOutputColumns().size();
		int hiddenCount = (int) ((double)(inputColumns+outputColumns) * 1.5);
		StringBuilder result = new StringBuilder();
		
		result.append("?->gaussian(c=");
		result.append(hiddenCount);
		result.append(")->?");
		return result.toString();
	}
	
	/**
	 * {@inheritDoc}
	 */
	@Override
	public NormalizationStrategy suggestNormalizationStrategy(VersatileMLDataSet dataset, String architecture) {
		int outputColumns = dataset.getNormHelper().getOutputColumns().size();

		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 OneOfNNormalizer(0,1));
		result.assignOutputNormalizer(ColumnType.ordinal,new OneOfNNormalizer(0,1));
		return result;
	}


	/**
	 * {@inheritDoc}
	 */
	@Override
	public String suggestTrainingType() {
		return "rprop";
	}


	/**
	 * {@inheritDoc}
	 */
	@Override
	public String suggestTrainingArgs(String trainingType) {
		return "";
	}
	
	/**
	 * {@inheritDoc}
	 */
	@Override
	public int determineOutputCount(VersatileMLDataSet dataset) {
		return dataset.getNormHelper().calculateNormalizedOutputCount();
	}
}