/*
 * Encog(tm) Unit Tests v2.5 - Java Version
 * http://www.heatonresearch.com/encog/
 * http://code.google.com/p/encog-java/
 
 * Copyright 2008-2010 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.neural.networks.training;

import java.util.Iterator;

import junit.framework.Assert;
import junit.framework.TestCase;

import org.encog.mathutil.randomize.RangeRandomizer;
import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.NetworkUtil;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.layers.Layer;
import org.encog.neural.networks.training.anneal.NeuralSimulatedAnnealing;
import org.encog.neural.networks.training.genetic.NeuralGeneticAlgorithm;
import org.encog.neural.networks.training.propagation.Propagation;
import org.encog.neural.networks.training.propagation.back.Backpropagation;
import org.encog.neural.networks.training.propagation.manhattan.ManhattanPropagation;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.neural.networks.training.propagation.scg.ScaledConjugateGradient;
import org.encog.neural.prune.PruneSelective;
import org.encog.util.logging.Logging;
import org.junit.Test;

public class TestTraining extends TestCase   {

	
	@Test
	public void testRPROP() throws Throwable
	{
		Logging.stopConsoleLogging();
		NeuralDataSet trainingData = new BasicNeuralDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
		
		BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
		Train rprop = new ResilientPropagation(network, trainingData);
		NetworkUtil.testTraining(rprop,0.03);
	}
	
	@Test
	public void testBPROP() throws Throwable
	{
		Logging.stopConsoleLogging();
		NeuralDataSet trainingData = new BasicNeuralDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
		
		BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
		Train bprop = new Backpropagation(network, trainingData, 0.7, 0.9);
		NetworkUtil.testTraining(bprop,0.01);
	}
	
	@Test
	public void testManhattan() throws Throwable
	{
		Logging.stopConsoleLogging();
		NeuralDataSet trainingData = new BasicNeuralDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
		
		BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
		Train bprop = new ManhattanPropagation(network, trainingData, 0.01);
		NetworkUtil.testTraining(bprop,0.01);
	}
	
	@Test
	public void testSCG() throws Throwable
	{
		Logging.stopConsoleLogging();
		NeuralDataSet trainingData = new BasicNeuralDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
		
		BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
		Train bprop = new ScaledConjugateGradient(network, trainingData);
		NetworkUtil.testTraining(bprop,0.04);
	}
	
	@Test
	public void testAnneal() throws Throwable
	{
		Logging.stopConsoleLogging();
		NeuralDataSet trainingData = new BasicNeuralDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);		
		BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
		CalculateScore score = new TrainingSetScore(trainingData);
		NeuralSimulatedAnnealing anneal = new NeuralSimulatedAnnealing(network,score,10,2,100);
		NetworkUtil.testTraining(anneal,0.01);
	}
	
	@Test
	public void testGenetic() throws Throwable
	{
		Logging.stopConsoleLogging();
		NeuralDataSet trainingData = new BasicNeuralDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);		
		BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
		CalculateScore score = new TrainingSetScore(trainingData);
		NeuralGeneticAlgorithm genetic = new NeuralGeneticAlgorithm(network, new RangeRandomizer(-1,1), score, 500,0.1,0.25);
		NetworkUtil.testTraining(genetic,0.00001);
	}
}