Package org.encog.persist

Source Code of org.encog.persist.TestPersistPopulation

/*
* 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.persist;

import java.io.File;
import java.io.IOException;

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

import org.encog.engine.network.activation.ActivationStep;
import org.encog.ml.CalculateScore;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.ea.population.Population;
import org.encog.ml.ea.train.EvolutionaryAlgorithm;
import org.encog.neural.neat.NEATPopulation;
import org.encog.neural.neat.NEATUtil;
import org.encog.neural.networks.XOR;
import org.encog.neural.networks.training.TrainingSetScore;
import org.encog.util.TempDir;
import org.encog.util.obj.SerializeObject;

public class TestPersistPopulation extends TestCase {
 
  public final TempDir TEMP_DIR = new TempDir();
  public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
 
  private NEATPopulation generate()
  {
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
   
    CalculateScore score = new TrainingSetScore(trainingSet);
    // train the neural network
    ActivationStep step = new ActivationStep();
    step.setCenter(0.5);

    EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(
        score, 2, 1, 10);
    //train.setOutputActivationFunction(step);
   
    return (NEATPopulation)train.getPopulation();
  }
 
  public void testPersistEG()
  {
    Population pop = generate();

    EncogDirectoryPersistence.saveObject((EG_FILENAME), pop);
    NEATPopulation pop2 = (NEATPopulation)EncogDirectoryPersistence.loadObject((EG_FILENAME));
   
    validate(pop2);
  }
 
  public void testPersistSerial() throws IOException, ClassNotFoundException
  {
    NEATPopulation pop = generate();
    validate(pop);
   
    SerializeObject.save(SERIAL_FILENAME, pop);
    NEATPopulation pop2 = (NEATPopulation)SerializeObject.load(SERIAL_FILENAME);
   
    validate(pop2);
  }
 
  private void validate(NEATPopulation pop)
  {
    Assert.assertEquals(10,pop.getPopulationSize());
    Assert.assertEquals(0.2,pop.getSurvivalRate());
   
    // see if the population can actually be used to train
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);   
    CalculateScore score = new TrainingSetScore(trainingSet);
    EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop, score);
    train.iteration();

  }
 
  @Override
  protected void tearDown() throws Exception {
    super.tearDown();
    TEMP_DIR.dispose();
  }
}
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