Java InvalidConfigurationException.printStackTrace方法代码示例

import org.jgap.InvalidConfigurationException; //导入方法依赖的package包/类
@Override
  protected MLResults learn(AMapping trainingData) {

try {
	setUp(trainingData);
} catch (InvalidConfigurationException e) {
	e.printStackTrace();
	logger.error(e.getMessage());
	return null;
}
  	
  	turn++;
      fitness.addToReference(extractPositiveMatches(trainingData));
      fitness.fillCachesIncrementally(trainingData);

      Integer nGen = (Integer) getParameter(GENERATIONS);
      
      for (int gen = 1; gen <= nGen; gen++) {
          gp.evolve();
          bestSolutions.add(determineFittest(gp, gen));
      }

      MLResults result = createSupervisedResult();
      return result;
      
  }
 

import org.jgap.InvalidConfigurationException; //导入方法依赖的package包/类
@Override
public Object clone() {
    try {
        return new StringPreprocessingCommand(this.getGPConfiguration(), this.getReturnType(), function, getSubReturnType(), is_mutable);
    } catch (InvalidConfigurationException e) {
        e.printStackTrace();
        return this;
    }
}
 

import org.jgap.InvalidConfigurationException; //导入方法依赖的package包/类
public CommandGene clone() {
    try {
        return new NumberPropertyPair(config, getReturnType(), getSubReturnType(), mutateable, pairIndex);
    } catch (InvalidConfigurationException e) {
        e.printStackTrace();
        return this;
    }
}
 

import org.jgap.InvalidConfigurationException; //导入方法依赖的package包/类
public CommandGene clone() {
    try {
        return new DatePropertyPair(config, getReturnType(), getSubReturnType(), mutateable, pairIndex);
    } catch (InvalidConfigurationException e) {
        e.printStackTrace();
        return this;
    }
}
 

import org.jgap.InvalidConfigurationException; //导入方法依赖的package包/类
public CommandGene clone() {
    try {
        StringPropertyPair newPair = new StringPropertyPair(config, getReturnType(), getSubReturnType(), mutateable, pairIndex);
        return newPair;
    } catch (InvalidConfigurationException e) {
        e.printStackTrace();
        return this;
    }
}
 

import org.jgap.InvalidConfigurationException; //导入方法依赖的package包/类
public CommandGene clone() {
    try {
        PointSetPropertyPair newPair = new PointSetPropertyPair(config, getReturnType(), getSubReturnType(), mutateable, pairIndex);
        return newPair;
    } catch (InvalidConfigurationException e) {
        e.printStackTrace();
        return this;
    }
}
 

import org.jgap.InvalidConfigurationException; //导入方法依赖的package包/类
@Override
public void optimize (final IRunExecutable run)
{
	Configuration config = new DefaultConfiguration ();
	config.setPreservFittestIndividual (true);
	
	try
	{
		config.setFitnessFunction (new FitnessFunction ()
		{
			private static final long serialVersionUID = 1L;
			private Double m_fBias = null;

			@Override
			protected double evaluate (IChromosome chromosome)
			{
				StringBuilder sbResult = new StringBuilder ();					
				double fResult = run.execute (MetaHeuristicOptimizer.getParametersFromChromosome (chromosome), sbResult, checkBounds ());
				chromosome.setApplicationData (sbResult.toString ());

				// JGAP maximizes the fitness function, and only positive results are allowed
				
				//return Math.exp (-fResult / 1e8);
				
				//if (m_fBias == null)
				//	m_fBias = fResult * 10;
				//return m_fBias - fResult;
				
				if (m_fBias == null && fResult != Double.MAX_VALUE && fResult != 0)
					m_fBias = fResult;
				if (m_fBias == null)
					return Math.exp (-fResult);
				return Math.exp (-fResult / m_fBias);
			}
		});
		
		// create the chromosome
		Gene[] rgSampleGenes = new Gene[run.getParametersCount ()];
		for (int i = 0; i < run.getParametersCount (); i++)
			rgSampleGenes[i] = new IntegerGene (config, run.getParameterLowerBounds ()[i], run.getParameterUpperBounds ()[i]);
		config.setSampleChromosome (new Chromosome (config, rgSampleGenes));

		config.setPopulationSize (POPULATION_SIZE);
		
		// create population and evolve it
		Genotype population = Genotype.randomInitialGenotype (config);
		for (int i = 0; i < MAX_EVOLUTIONS; i++)
		{
			population.evolve ();
			LOGGER.info (population.getFittestChromosome ());
		}
		
		setResult (population.getFittestChromosome ());
	}
	catch (InvalidConfigurationException e)
	{
		e.printStackTrace ();
	}		
}
 

import org.jgap.InvalidConfigurationException; //导入方法依赖的package包/类
private Operand findOneSequence ()
{
	try
	{
		// create population and evolve it
		IChromosome[] rgChromosomes = new IChromosome[m_listInputOperands.size ()];
		int nIdx = 0;
		for (Operand op : m_listInputOperands)
		{
			Gene[] rgGenes = new Gene[m_nElementsCount];
			for (int i = 0; i < m_nElementsCount; i++)
			{
				rgGenes[i] = new IntegerGene (m_config, 0, 2 * m_nElementsCount - 1);
				rgGenes[i].setAllele (op.getElements ()[i]);
			}
			
			rgChromosomes[nIdx] = new Chromosome (m_config, rgGenes);
			rgChromosomes[nIdx].setApplicationData (op);
			nIdx++;
		}
		
		// evolve the population
		Population population = new Population (m_config, rgChromosomes);
		Genotype genotype = new Genotype (m_config, population);
		for (int i = 0; i < MAX_EVOLUTIONS; i++)
		{
			genotype.evolve ();
			//LOGGER.info (genotype.getFittestChromosome ());
		}
		
		IChromosome chromFittest = genotype.getFittestChromosome ();
		LOGGER.info (chromFittest);
		
		Operand opRes = (Operand) chromFittest.getApplicationData ();
		opRes.m_rgElements = new int[m_nElementsCount];
		for (int i = 0; i < m_nElementsCount; i++)
			opRes.m_rgElements[i] = (Integer) chromFittest.getGene (i).getAllele ();
		
		return opRes;
	}
	catch (InvalidConfigurationException e)
	{
		e.printStackTrace ();
	}

	return null;
}
 

import org.jgap.InvalidConfigurationException; //导入方法依赖的package包/类
public static void main( String[] args ) {

		// Configure genetic algorithm
		PropertiesStore conf = GeneticSynth.getDefaults();

		// Take arguments from commandline
		String targetWav = "";
		int midiNote = -1;
		String outputDirectory = "";
		for ( String arg : args ) {
			String[] split = arg.split( "=", 2 );

			// Two essential parameters
			if ( split[0].equals( "target" ) ) {
				targetWav = split[1];
			} else if ( split[0].equals( "midinote" ) ) {
				midiNote = Integer.parseInt( split[1] );
			} else if ( split[0].equals( "output" ) ) {
				outputDirectory = split[1];
			} else {
				// Store in conf!
				PropertiesStore.ConstraintType type = conf.getPropertyType( split[0] );
				if ( type != null ) {
					switch ( type ) {
						case INT:
							conf.setProperty( split[0], Integer.parseInt( split[1] ) );
							break;
						case FLOAT:
							conf.setProperty( split[0], Float.parseFloat( split[1] ) );
							break;
						default:
							break;
					}
				}
			}
		}

		// Error if necessary parameters not specified.
		if ( midiNote == -1 ) {
			System.err.println( "No midi note specified!" );
			System.exit( -1 );
		} else if ( targetWav.equals( "" ) ) {
			System.err.println( "No target wav file specified!" );
			System.exit( -1 );
		} else if ( outputDirectory.equals( "" ) ) {
			System.err.println( "No output directory specified - no output will be stored!" );
		}

		// Load wav file
		float[] wavBuffer = MonoWaveformManipulator.readWav( targetWav );
		if ( wavBuffer == null ) {
			System.err.println( "Cannot read wav file" );
			System.exit( -1 );
		}

		// Create genetic algorithm
		GeneticSynth gSynth = null;
		try {
			gSynth = new GeneticSynth( conf, wavBuffer, midiNote, outputDirectory );
			gSynth.init();
		} catch ( InvalidConfigurationException e1 ) {
			e1.printStackTrace();
			System.exit( -1 );
		}

		// Run run run!
		gSynth.run();

		// Save best individual for inspection
		gSynth.saveBestWavAndConf( "./" );
	}