List of usage examples for org.apache.commons.configuration XMLConfiguration XMLConfiguration
public XMLConfiguration()
XMLConfiguration. From source file:it.grid.storm.namespace.config.xml.XMLNamespaceLoader.java
private void init(String namespaceFileName, int refresh) { log.info("Reading Namespace configuration file " + namespaceFileName + " and setting refresh rate to " + refresh + " seconds."); // create reloading strategy for refresh xmlStrategy = new XMLReloadingStrategy(); period = 3000; // Conversion in millisec. log.debug(" Refresh time is " + period + " millisec"); xmlStrategy.setRefreshDelay(period); // Set to refresh sec the refreshing // delay. namespaceFN = namespaceFileName;/* w ww. ja v a 2s .c o m*/ // specify the properties file and set the reloading strategy for that file try { config = new XMLConfiguration(); config.setFileName(namespaceFileName); // Validation of Namespace.xml log.debug(" ... CHECK of VALIDITY of NAMESPACE Configuration ..."); schemaValidity = XMLNamespaceLoader.checkValidity(namespaceSchemaURL, namespaceFileName); if (!(schemaValidity)) { log.error("NAMESPACE IS NOT VALID IN RESPECT OF NAMESPACE SCHEMA! "); throw new ConfigurationException("XML is not valid!"); } else { log.debug("Namespace is valid in respect of NAMESPACE SCHEMA."); } // This will throw a ConfigurationException if the XML document does not // conform to its DTD. config.setReloadingStrategy(xmlStrategy); Peeper peeper = new Peeper(this); timer.schedule(peeper, delay, period); log.debug("Timer initialized"); config.load(); log.debug("Namespace Configuration read!"); } catch (ConfigurationException cex) { log.error("ATTENTION! Unable to load Namespace Configuration!"); log.error(toString()); } }
From source file:cn.quickj.Setting.java
private static void loadApplicationConfig() { XMLConfiguration config;/*from w w w . j a va2s . c o m*/ try { config = new XMLConfiguration(); // ?????? config.setDelimiterParsingDisabled(true); String appconfigPath = webRoot + "WEB-INF/appconfig.xml"; config.load(appconfigPath); } catch (Exception e) { config = null; } String className = null; if (config != null) className = config.getString("class", null); if (className != null) { try { Class<?> clazz = Class.forName(className); appconfig = (ApplicationConfig) clazz.newInstance(); appconfig.init(config); } catch (Exception e) { e.printStackTrace(); log.error("?appconfig.xml?!" + e.getCause()); appconfig = null; } } }
From source file:com.egreen.tesla.server.api.component.Component.java
/** * * Init configurations from Component/*from w ww . j a v a 2 s . co m*/ * * @param jf * @throws IOException * @throws ConfigurationException */ private void setConfiguraton(JarFile jf) throws IOException, ConfigurationException { JarEntry entry = jf.getJarEntry(TESLAR_WIDGET_MAINIFIESTXML); InputStream inputStream = jf.getInputStream(entry); configuration = new XMLConfiguration(); configuration.load(inputStream); componentID = configuration.getString(Settings.COMPONENT_ID.getType()); LOGGER.info("Loadding : " + componentID); component_base = context.getRealPath("/") + "/components/" + componentID; }
From source file:com.graphhopper.jsprit.core.problem.io.VrpXMLReader.java
private XMLConfiguration createXMLConfiguration() { XMLConfiguration xmlConfig = new XMLConfiguration(); xmlConfig.setAttributeSplittingDisabled(true); xmlConfig.setDelimiterParsingDisabled(true); if (schemaValidation) { final InputStream resource = Resource.getAsInputStream("vrp_xml_schema.xsd"); if (resource != null) { EntityResolver resolver = new EntityResolver() { @Override//www . j a v a 2s . c om public InputSource resolveEntity(String publicId, String systemId) throws SAXException, IOException { { InputSource is = new InputSource(resource); return is; } } }; xmlConfig.setEntityResolver(resolver); xmlConfig.setSchemaValidation(true); } else { logger.debug( "cannot find schema-xsd file (vrp_xml_schema.xsd). try to read xml without xml-file-validation."); } } return xmlConfig; }
From source file:edu.cornell.med.icb.R.TestRConnectionPool.java
/** * Validates that the connection pool will not allow connections to be handed out * when no valid servers were set./* w w w . j a v a 2s .c o m*/ * @throws RserveException if there is an issue connecting with an Rserver */ @Test(expected = IllegalStateException.class) public void noValidServers() throws RserveException { // set up a pool with an empty configuration pool = new RConnectionPool(new XMLConfiguration()); assertNotNull("Connection pool should never be null", pool); assertTrue("The pool should not be open", pool.isClosed()); assertEquals("There should be no connections", 0, pool.getNumberOfConnections()); assertEquals("There should be no connections", 0, pool.getNumberOfActiveConnections()); assertEquals("There should be no connections", 0, pool.getNumberOfIdleConnections()); // if we try to get a connection, we shouldn't be able to - there are none configured pool.borrowConnection(); }
From source file:keel.Algorithms.Neural_Networks.NNEP_Regr.KEELWrapperRegr.java
/** * <p>//from ww w .j a v a 2 s . c o m * Configure the execution of the algorithm. * </p> * @param jobFilename Name of the KEEL file with properties of the * execution */ @SuppressWarnings("unchecked") private static void configureJob(String jobFilename) { Properties props = new Properties(); try { InputStream paramsFile = new FileInputStream(jobFilename); props.load(paramsFile); paramsFile.close(); } catch (IOException ioe) { ioe.printStackTrace(); System.exit(0); } // Files training and test String trainFile; String testFile; StringTokenizer tokenizer = new StringTokenizer(props.getProperty("inputData")); tokenizer.nextToken(); trainFile = tokenizer.nextToken(); trainFile = trainFile.substring(1, trainFile.length() - 1); testFile = tokenizer.nextToken(); testFile = testFile.substring(1, testFile.length() - 1); // Classification or Regression ?? byte[] schema = null; try { schema = readSchema(trainFile); } catch (IOException e) { e.printStackTrace(); } catch (DatasetException e) { e.printStackTrace(); } // Algorithm auxiliar configuration XMLConfiguration algConf = new XMLConfiguration(); algConf.setRootElementName("algorithm"); algConf.addProperty("population-size", 1000); algConf.addProperty("max-of-generations", Integer.parseInt(props.getProperty("Generations"))); algConf.addProperty("creation-ratio", 10.0); algConf.addProperty("percentage-second-mutator", 10); algConf.addProperty("max-generations-without-improving-mean", 20); algConf.addProperty("max-generations-without-improving-best", 20); algConf.addProperty("fitness-difference", 0.0000001); algConf.addProperty("species[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies"); algConf.addProperty("species.neural-net-type", "keel.Algorithms.Neural_Networks.NNEP_Regr.neuralnet.NeuralNetRegressor"); if (props.getProperty("Transfer").equals("Product_Unit")) { algConf.addProperty("species.hidden-layer[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpLayer"); algConf.addProperty("species.hidden-layer[@biased]", false); algConf.addProperty("evaluator[@log-input-data]", true); } else { algConf.addProperty("species.hidden-layer[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmLayer"); algConf.addProperty("species.hidden-layer[@biased]", true); } int neurons = Integer.parseInt(props.getProperty("Hidden_nodes")); algConf.addProperty("species.hidden-layer.minimum-number-of-neurons", (neurons / 3) != 0 ? (neurons / 3) : 1); algConf.addProperty("species.hidden-layer.initial-maximum-number-of-neurons", (neurons / 2) != 0 ? (neurons / 2) : 1); algConf.addProperty("species.hidden-layer.maximum-number-of-neurons", neurons); algConf.addProperty("species.hidden-layer.initiator-of-links", "keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator"); algConf.addProperty("species.hidden-layer.weight-range[@type]", "net.sf.jclec.util.range.Interval"); algConf.addProperty("species.hidden-layer.weight-range[@closure]", "closed-closed"); algConf.addProperty("species.hidden-layer.weight-range[@left]", -5.0); algConf.addProperty("species.hidden-layer.weight-range[@right]", 5.0); algConf.addProperty("species.output-layer[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearLayer"); algConf.addProperty("species.output-layer[@biased]", true); algConf.addProperty("species.output-layer.initiator-of-links", "keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator"); algConf.addProperty("species.output-layer.weight-range[@type]", "net.sf.jclec.util.range.Interval"); algConf.addProperty("species.output-layer.weight-range[@closure]", "closed-closed"); algConf.addProperty("species.output-layer.weight-range[@left]", -5.0); algConf.addProperty("species.output-layer.weight-range[@right]", 5.0); algConf.addProperty("evaluator[@type]", "keel.Algorithms.Neural_Networks.NNEP_Regr.problem.regression.RegressionProblemEvaluator"); algConf.addProperty("evaluator[@normalize-data]", true); algConf.addProperty("evaluator.error-function", "keel.Algorithms.Neural_Networks.NNEP_Regr.problem.errorfunctions.MSEErrorFunction"); algConf.addProperty("evaluator.input-interval[@closure]", "closed-closed"); if (props.getProperty("Transfer").equals("Product_Unit")) { algConf.addProperty("evaluator.input-interval[@left]", 1.0); algConf.addProperty("evaluator.input-interval[@right]", 2.0); } else { algConf.addProperty("evaluator.input-interval[@left]", 0.1); algConf.addProperty("evaluator.input-interval[@right]", 0.9); } algConf.addProperty("evaluator.output-interval[@closure]", "closed-closed"); algConf.addProperty("evaluator.output-interval[@left]", 1.0); algConf.addProperty("evaluator.output-interval[@right]", 2.0); algConf.addProperty("provider[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetCreator"); algConf.addProperty("mutator1[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator"); algConf.addProperty("mutator1.temperature-exponent[@value]", 1.0); algConf.addProperty("mutator1.significative-weigth[@value]", 0.0000001); algConf.addProperty("mutator1.neuron-ranges.added[@min]", 1); algConf.addProperty("mutator1.neuron-ranges.added[@max]", 2); algConf.addProperty("mutator1.neuron-ranges.deleted[@min]", 1); algConf.addProperty("mutator1.neuron-ranges.deleted[@max]", 2); algConf.addProperty("mutator1.links-ranges[@relative]", false); algConf.addProperty("mutator1.links-ranges.added[@min]", 1); algConf.addProperty("mutator1.links-ranges.added[@max]", 6); algConf.addProperty("mutator1.links-ranges.deleted[@min]", 1); algConf.addProperty("mutator1.links-ranges.deleted[@max]", 6); algConf.addProperty("mutator2[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSAMutator"); algConf.addProperty("mutator2.temperature-exponent[@value]", 0.0); algConf.addProperty("mutator2.amplitude[@value]", 5.0); algConf.addProperty("mutator2.fitness-difference[@value]", 0.0000001); algConf.addProperty("mutator2.initial-alpha-values[@input]", 0.5); algConf.addProperty("mutator2.initial-alpha-values[@output]", 1.0); algConf.addProperty("rand-gen-factory[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnepFactory"); algConf.addProperty("rand-gen-factory[@seed]", Integer.parseInt(props.getProperty("seed"))); // Neural Net Algorithm algorithm = new NeuralNetAlgorithm<NeuralNetIndividual>(); algorithm.configure(algConf); // Read data ProblemEvaluator evaluator = (ProblemEvaluator) algorithm.getEvaluator(); evaluator.readData(schema, new KeelDataSet(trainFile), new KeelDataSet(testFile)); ((NeuralNetIndividualSpecies) algorithm.getSpecies()).setNOfInputs(evaluator.getTrainData().getNofinputs()); ((NeuralNetIndividualSpecies) algorithm.getSpecies()) .setNOfOutputs(evaluator.getTrainData().getNofoutputs()); // Read output files tokenizer = new StringTokenizer(props.getProperty("outputData")); String trainResultFile = tokenizer.nextToken(); trainResultFile = trainResultFile.substring(1, trainResultFile.length() - 1); consoleReporter.setTrainResultFile(trainResultFile); String testResultFile = tokenizer.nextToken(); testResultFile = testResultFile.substring(1, testResultFile.length() - 1); consoleReporter.setTestResultFile(testResultFile); String bestModelResultFile = tokenizer.nextToken(); bestModelResultFile = bestModelResultFile.substring(1, bestModelResultFile.length() - 1); consoleReporter.setBestModelResultFile(bestModelResultFile); listeners.add(consoleReporter); }
From source file:br.gov.frameworkdemoiselle.internal.implementation.ConfigurationLoader.java
private org.apache.commons.configuration.Configuration createConfiguration() { AbstractConfiguration config;/*ww w . j a v a2s. c o m*/ switch (this.type) { case XML: config = new XMLConfiguration(); break; case SYSTEM: config = new SystemConfiguration(); break; default: config = new PropertiesConfiguration(); } config.setDelimiterParsingDisabled(true); return config; }
From source file:keel.Algorithms.Neural_Networks.NNEP_Clas.KEELWrapperClas.java
/** * <p>//from ww w.j a v a2s.c o m * Configure the execution of the algorithm. * * @param jobFilename Name of the KEEL file with properties of the * execution * </p> */ @SuppressWarnings("unchecked") private static void configureJob(String jobFilename) { Properties props = new Properties(); try { InputStream paramsFile = new FileInputStream(jobFilename); props.load(paramsFile); paramsFile.close(); } catch (IOException ioe) { ioe.printStackTrace(); System.exit(0); } // Files training and test String trainFile; String testFile; StringTokenizer tokenizer = new StringTokenizer(props.getProperty("inputData")); tokenizer.nextToken(); trainFile = tokenizer.nextToken(); trainFile = trainFile.substring(1, trainFile.length() - 1); testFile = tokenizer.nextToken(); testFile = testFile.substring(1, testFile.length() - 1); // Classification or Regression ?? byte[] schema = null; try { schema = readSchema(trainFile); } catch (IOException e) { e.printStackTrace(); } catch (DatasetException e) { e.printStackTrace(); } // Algorithm auxiliar configuration XMLConfiguration algConf = new XMLConfiguration(); algConf.setRootElementName("algorithm"); algConf.addProperty("population-size", 1000); algConf.addProperty("max-of-generations", Integer.parseInt(props.getProperty("Generations"))); algConf.addProperty("creation-ratio", 10.0); algConf.addProperty("percentage-second-mutator", 10); algConf.addProperty("max-generations-without-improving-mean", 20); algConf.addProperty("max-generations-without-improving-best", 20); algConf.addProperty("fitness-difference", 0.0000001); algConf.addProperty("species[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies"); algConf.addProperty("species.neural-net-type", "keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet.NeuralNetClassifier"); if (props.getProperty("Transfer").equals("Product_Unit")) { algConf.addProperty("species.hidden-layer[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpLayer"); algConf.addProperty("species.hidden-layer[@biased]", false); algConf.addProperty("evaluator[@log-input-data]", true); } else { algConf.addProperty("species.hidden-layer[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmLayer"); algConf.addProperty("species.hidden-layer[@biased]", true); } int neurons = Integer.parseInt(props.getProperty("Hidden_nodes")); algConf.addProperty("species.hidden-layer.minimum-number-of-neurons", (neurons / 3) != 0 ? (neurons / 3) : 1); algConf.addProperty("species.hidden-layer.initial-maximum-number-of-neurons", (neurons / 2) != 0 ? (neurons / 2) : 1); algConf.addProperty("species.hidden-layer.maximum-number-of-neurons", neurons); algConf.addProperty("species.hidden-layer.initiator-of-links", "keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator"); algConf.addProperty("species.hidden-layer.weight-range[@type]", "net.sf.jclec.util.range.Interval"); algConf.addProperty("species.hidden-layer.weight-range[@closure]", "closed-closed"); algConf.addProperty("species.hidden-layer.weight-range[@left]", -5.0); algConf.addProperty("species.hidden-layer.weight-range[@right]", 5.0); algConf.addProperty("species.output-layer[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearLayer"); algConf.addProperty("species.output-layer[@biased]", true); algConf.addProperty("species.output-layer.initiator-of-links", "keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator"); algConf.addProperty("species.output-layer.weight-range[@type]", "net.sf.jclec.util.range.Interval"); algConf.addProperty("species.output-layer.weight-range[@closure]", "closed-closed"); algConf.addProperty("species.output-layer.weight-range[@left]", -5.0); algConf.addProperty("species.output-layer.weight-range[@right]", 5.0); algConf.addProperty("evaluator[@type]", "keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.SoftmaxClassificationProblemEvaluator"); algConf.addProperty("evaluator[@normalize-data]", true); algConf.addProperty("evaluator.error-function", "keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions.LogisticErrorFunction"); algConf.addProperty("evaluator.input-interval[@closure]", "closed-closed"); if (props.getProperty("Transfer").equals("Product_Unit")) { algConf.addProperty("evaluator.input-interval[@left]", 1.0); algConf.addProperty("evaluator.input-interval[@right]", 2.0); } else { algConf.addProperty("evaluator.input-interval[@left]", 0.1); algConf.addProperty("evaluator.input-interval[@right]", 0.9); } algConf.addProperty("evaluator.output-interval[@closure]", "closed-closed"); algConf.addProperty("evaluator.output-interval[@left]", 0.0); algConf.addProperty("evaluator.output-interval[@right]", 1.0); algConf.addProperty("provider[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetCreator"); algConf.addProperty("mutator1[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator"); algConf.addProperty("mutator1.temperature-exponent[@value]", 1.0); algConf.addProperty("mutator1.significative-weigth[@value]", 0.0000001); algConf.addProperty("mutator1.neuron-ranges.added[@min]", 1); algConf.addProperty("mutator1.neuron-ranges.added[@max]", 2); algConf.addProperty("mutator1.neuron-ranges.deleted[@min]", 1); algConf.addProperty("mutator1.neuron-ranges.deleted[@max]", 2); algConf.addProperty("mutator1.links-ranges[@relative]", true); algConf.addProperty("mutator1.links-ranges.percentages[@hidden]", 30); algConf.addProperty("mutator1.links-ranges.percentages[@output]", 5); algConf.addProperty("mutator2[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSRMutator"); algConf.addProperty("mutator2.temperature-exponent[@value]", 0.0); algConf.addProperty("mutator2.amplitude[@value]", 5.0); algConf.addProperty("mutator2.fitness-difference[@value]", 0.0000001); algConf.addProperty("mutator2.initial-alpha-values[@input]", 0.5); algConf.addProperty("mutator2.initial-alpha-values[@output]", 1.0); algConf.addProperty("rand-gen-factory[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnepFactory"); algConf.addProperty("rand-gen-factory[@seed]", Integer.parseInt(props.getProperty("seed"))); // Neural Net Algorithm algorithm = new CCRElitistNeuralNetAlgorithm(); algorithm.configure(algConf); // Read data ProblemEvaluator evaluator = (ProblemEvaluator) algorithm.getEvaluator(); evaluator.readData(schema, new KeelDataSet(trainFile), new KeelDataSet(testFile)); ((NeuralNetIndividualSpecies) algorithm.getSpecies()).setNOfInputs(evaluator.getTrainData().getNofinputs()); ((NeuralNetIndividualSpecies) algorithm.getSpecies()) .setNOfOutputs(evaluator.getTrainData().getNofoutputs() - 1); // Read output files tokenizer = new StringTokenizer(props.getProperty("outputData")); String trainResultFile = tokenizer.nextToken(); trainResultFile = trainResultFile.substring(1, trainResultFile.length() - 1); consoleReporter.setTrainResultFile(trainResultFile); String testResultFile = tokenizer.nextToken(); testResultFile = testResultFile.substring(1, testResultFile.length() - 1); consoleReporter.setTestResultFile(testResultFile); String bestModelResultFile = tokenizer.nextToken(); bestModelResultFile = bestModelResultFile.substring(1, bestModelResultFile.length() - 1); consoleReporter.setBestModelResultFile(bestModelResultFile); listeners.add(consoleReporter); }
From source file:com.aol.advertising.qiao.config.AgentXmlConfiguration.java
/** * Load configuration file and interpolate variables. Note that due to the * way Apache Commons Configuration treats property keys, user should avoid * using dot ('.') in the property key. Otherwise ACC may not substitute * some value if the variable contains valid element name(s). * * @param xmlConfigFile// w w w . j av a 2 s . c o m * @param propConfigFiles * @return * @throws ConfigurationException */ protected HierarchicalConfiguration readConfigurationFiles(String xmlConfigFile, String propConfigFiles) throws ConfigurationException { try { // convert URI to URL URL[] prop_urls = null; URL xml_url = CommonUtils.uriToURL(xmlConfigFile); if (propConfigFiles != null) { String[] prop_files = propConfigFiles.split(","); prop_urls = new URL[prop_files.length]; for (int i = 0; i < prop_files.length; i++) { prop_urls[i] = CommonUtils.uriToURL(prop_files[i]); } } // combine xml and properties configurations CombinedConfiguration combined_cfg = new CombinedConfiguration(new OverrideCombiner()); XMLConfiguration cfg_xml = new XMLConfiguration(); cfg_xml.setDelimiterParsingDisabled(true); cfg_xml.setAttributeSplittingDisabled(true); cfg_xml.load(xml_url); combined_cfg.addConfiguration(cfg_xml); if (prop_urls != null) { // properties in the earlier files take precedence if duplicate for (int i = 0; i < prop_urls.length; i++) { PropertiesConfiguration cfg_props = new PropertiesConfiguration(); cfg_props.setDelimiterParsingDisabled(true); cfg_props.load(prop_urls[i]); combined_cfg.addConfiguration(cfg_props); } } HierarchicalConfiguration config = (HierarchicalConfiguration) combined_cfg.interpolatedConfiguration(); // !!! resolve variables return config; } catch (Exception e) { throw new ConfigurationException(e.getMessage(), e); } }
From source file:keel.Algorithms.Neural_Networks.IRPropPlus_Clas.KEELIRPropPlusWrapperClas.java
/** * <p>/* w w w .jav a 2 s . c o m*/ * Configure the execution of the algorithm. * * @param jobFilename Name of the KEEL file with properties of the * execution * </p> */ @SuppressWarnings("unchecked") private static void configureJob(String jobFilename) { Properties props = new Properties(); try { InputStream paramsFile = new FileInputStream(jobFilename); props.load(paramsFile); paramsFile.close(); } catch (IOException ioe) { ioe.printStackTrace(); System.exit(0); } // Files training and test String trainFile; String testFile; StringTokenizer tokenizer = new StringTokenizer(props.getProperty("inputData")); tokenizer.nextToken(); trainFile = tokenizer.nextToken(); trainFile = trainFile.substring(1, trainFile.length() - 1); testFile = tokenizer.nextToken(); testFile = testFile.substring(1, testFile.length() - 1); // Configure schema byte[] schema = null; try { schema = readSchema(trainFile); } catch (IOException e) { e.printStackTrace(); } catch (DatasetException e) { e.printStackTrace(); } // Auxiliar configuration file XMLConfiguration conf = new XMLConfiguration(); conf.setRootElementName("algorithm"); // Configure randGenFactory randGenFactory = new RanNnepFactory(); conf.addProperty("rand-gen-factory[@seed]", Integer.parseInt(props.getProperty("seed"))); if (randGenFactory instanceof IConfigure) ((IConfigure) randGenFactory).configure(conf.subset("rand-gen-factory")); // Configure species NeuralNetIndividualSpecies nnspecies = new NeuralNetIndividualSpecies(); species = (ISpecies) nnspecies; if (props.getProperty("Transfer").equals("Product_Unit")) { conf.addProperty("species.neural-net-type", "keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizablePUNeuralNetClassifier"); conf.addProperty("species.hidden-layer[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpLayer"); conf.addProperty("species.hidden-layer[@biased]", false); } else { conf.addProperty("species.neural-net-type", "keel.Algorithms.Neural_Networks.IRPropPlus_Clas.MSEOptimizableSigmNeuralNetClassifier"); conf.addProperty("species.hidden-layer[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmLayer"); conf.addProperty("species.hidden-layer[@biased]", true); } int neurons = Integer.parseInt(props.getProperty("Hidden_nodes")); conf.addProperty("species.hidden-layer.minimum-number-of-neurons", neurons); conf.addProperty("species.hidden-layer.initial-maximum-number-of-neurons", neurons); conf.addProperty("species.hidden-layer.maximum-number-of-neurons", neurons); conf.addProperty("species.hidden-layer.initiator-of-links", "keel.Algorithms.Neural_Networks.IRPropPlus_Clas.FullRandomInitiator"); conf.addProperty("species.hidden-layer.weight-range[@type]", "net.sf.jclec.util.range.Interval"); conf.addProperty("species.hidden-layer.weight-range[@closure]", "closed-closed"); if (props.getProperty("Transfer").equals("Product_Unit")) { conf.addProperty("species.hidden-layer.weight-range[@left]", -0.1); conf.addProperty("species.hidden-layer.weight-range[@right]", 0.1); } else { conf.addProperty("species.hidden-layer.weight-range[@left]", -5.0); conf.addProperty("species.hidden-layer.weight-range[@right]", 5.0); } conf.addProperty("species.output-layer[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearLayer"); conf.addProperty("species.output-layer[@biased]", true); conf.addProperty("species.output-layer.initiator-of-links", "keel.Algorithms.Neural_Networks.IRPropPlus_Clas.FullRandomInitiator"); conf.addProperty("species.output-layer.weight-range[@type]", "net.sf.jclec.util.range.Interval"); conf.addProperty("species.output-layer.weight-range[@closure]", "closed-closed"); conf.addProperty("species.output-layer.weight-range[@left]", -5.0); conf.addProperty("species.output-layer.weight-range[@right]", 5.0); if (species instanceof IConfigure) ((IConfigure) species).configure(conf.subset("species")); // Configure evaluator evaluator = (IEvaluator) new SoftmaxClassificationProblemEvaluator(); if (props.getProperty("Transfer").equals("Product_Unit")) conf.addProperty("evaluator[@log-input-data]", true); conf.addProperty("evaluator[@normalize-data]", true); conf.addProperty("evaluator.error-function", "keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions.LogisticErrorFunction"); conf.addProperty("evaluator.input-interval[@closure]", "closed-closed"); conf.addProperty("evaluator.input-interval[@left]", 0.1); conf.addProperty("evaluator.input-interval[@right]", 0.9); conf.addProperty("evaluator.output-interval[@closure]", "closed-closed"); conf.addProperty("evaluator.output-interval[@left]", 0.0); conf.addProperty("evaluator.output-interval[@right]", 1.0); if (evaluator instanceof IConfigure) ((IConfigure) evaluator).configure(conf.subset("evaluator")); // Configure provider provider = new NeuralNetCreator(); KEELIRPropPlusWrapperClas system = new KEELIRPropPlusWrapperClas(); provider.contextualize(system); // Configure iRProp+ algorithm algorithm = new IRPropPlus(); conf.addProperty("algorithm.initial-step-size[@value]", 0.0125); conf.addProperty("algorithm.minimum-delta[@value]", 0.0); conf.addProperty("algorithm.maximum-delta[@value]", 50.0); conf.addProperty("algorithm.positive-eta[@value]", 1.2); conf.addProperty("algorithm.negative-eta[@value]", 0.2); conf.addProperty("algorithm.cycles[@value]", Integer.parseInt(props.getProperty("Epochs"))); if (algorithm instanceof IConfigure) ((IConfigure) algorithm).configure(conf.subset("algorithm")); // Read data ProblemEvaluator<AbstractIndividual<INeuralNet>> evaluator2 = (ProblemEvaluator<AbstractIndividual<INeuralNet>>) evaluator; evaluator2.readData(schema, new KeelDataSet(trainFile), new KeelDataSet(testFile)); nnspecies.setNOfInputs(evaluator2.getTrainData().getNofinputs()); nnspecies.setNOfOutputs(evaluator2.getTrainData().getNofoutputs() - 1); algorithm.setTrainingData(evaluator2.getTrainData()); // Read output files tokenizer = new StringTokenizer(props.getProperty("outputData")); String trainResultFile = tokenizer.nextToken(); trainResultFile = trainResultFile.substring(1, trainResultFile.length() - 1); consoleReporter.setTrainResultFile(trainResultFile); String testResultFile = tokenizer.nextToken(); testResultFile = testResultFile.substring(1, testResultFile.length() - 1); consoleReporter.setTestResultFile(testResultFile); String bestModelResultFile = tokenizer.nextToken(); bestModelResultFile = bestModelResultFile.substring(1, bestModelResultFile.length() - 1); consoleReporter.setBestModelResultFile(bestModelResultFile); }