Java tutorial
/*********************************************************************** This file is part of KEEL-software, the Data Mining tool for regression, classification, clustering, pattern mining and so on. Copyright (C) 2004-2010 F. Herrera (herrera@decsai.ugr.es) L. Snchez (luciano@uniovi.es) J. Alcal-Fdez (jalcala@decsai.ugr.es) S. Garca (sglopez@ujaen.es) A. Fernndez (alberto.fernandez@ujaen.es) J. Luengo (julianlm@decsai.ugr.es) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/ **********************************************************************/ package keel.Algorithms.Genetic_Rule_Learning.Bojarczuk_GP; import java.io.File; import java.io.FileInputStream; import java.io.IOException; import java.io.InputStream; import java.util.Properties; import java.util.StringTokenizer; import org.apache.commons.configuration.ConfigurationException; import org.apache.commons.configuration.XMLConfiguration; /** * <p> * @author Written by Jose Maria Luna, Juan Luis Olmo, Alberto Cano (Universidad de Cordoba) 05/07/2010 * @version 0.1 * @since JDK1.5 * </p> */ public class Main { /** * <p> * Bojarczuk classification algorithm * </p> */ ///////////////////////////////////////////////////////////////// // ----------------------------------------------- Public methods ///////////////////////////////////////////////////////////////// /** * <p> * Main method * </p> */ public static void main(String[] args) { configureJob(args[0]); } ///////////////////////////////////////////////////////////////// // ---------------------------------------------- Private methods ///////////////////////////////////////////////////////////////// /** * <p> * Configure the execution of the algorithm. * * @param jobFilename Name of the KEEL file with properties of the execution * </p> */ 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); tokenizer = new StringTokenizer(props.getProperty("outputData")); String reportTrainFile = tokenizer.nextToken(); reportTrainFile = reportTrainFile.substring(1, reportTrainFile.length() - 1); String reportTestFile = tokenizer.nextToken(); reportTestFile = reportTestFile.substring(1, reportTestFile.length() - 1); String reportRulesFile = tokenizer.nextToken(); reportRulesFile = reportRulesFile.substring(1, reportRulesFile.length() - 1); // Algorithm auxiliar configuration XMLConfiguration algConf = new XMLConfiguration(); algConf.setRootElementName("experiment"); algConf.addProperty("process[@algorithm-type]", "net.sourceforge.jclec.problem.classification.freitas.FreitasAlgorithm"); algConf.addProperty("process.rand-gen-factory[@type]", "net.sourceforge.jclec.util.random.RanecuFactory"); algConf.addProperty("process.rand-gen-factory[@seed]", Integer.parseInt(props.getProperty("seed"))); algConf.addProperty("process.population-size", Integer.parseInt(props.getProperty("population-size"))); algConf.addProperty("process.max-of-generations", Integer.parseInt(props.getProperty("max-generations"))); algConf.addProperty("process.max-deriv-size", Integer.parseInt(props.getProperty("max-deriv-size"))); algConf.addProperty("process.dataset[@type]", "net.sourceforge.jclec.util.dataset.KeelDataSet"); algConf.addProperty("process.dataset.train-data.file-name", trainFile); algConf.addProperty("process.dataset.test-data.file-name", testFile); algConf.addProperty("process.species[@type]", "net.sourceforge.jclec.problem.classification.freitas.FreitasSyntaxTreeSpecies"); algConf.addProperty("process.evaluator[@type]", "net.sourceforge.jclec.problem.classification.freitas.FreitasEvaluator"); algConf.addProperty("process.provider[@type]", "net.sourceforge.jclec.syntaxtree.SyntaxTreeCreator"); algConf.addProperty("process.parents-selector[@type]", "net.sourceforge.jclec.selector.RouletteSelector"); algConf.addProperty("process.recombinator[@type]", "net.sourceforge.jclec.syntaxtree.SyntaxTreeRecombinator"); algConf.addProperty("process.recombinator[@rec-prob]", Double.parseDouble(props.getProperty("rec-prob"))); algConf.addProperty("process.recombinator.base-op[@type]", "net.sourceforge.jclec.problem.classification.freitas.FreitasCrossover"); algConf.addProperty("process.copy-prob", Double.parseDouble(props.getProperty("copy-prob"))); algConf.addProperty("process.listener[@type]", "net.sourceforge.jclec.problem.classification.freitas.KeelFreitasPopulationReport"); algConf.addProperty("process.listener.report-dir-name", "./"); algConf.addProperty("process.listener.train-report-file", reportTrainFile); algConf.addProperty("process.listener.test-report-file", reportTestFile); algConf.addProperty("process.listener.rules-report-file", reportRulesFile); algConf.addProperty("process.listener.global-report-name", "resumen"); algConf.addProperty("process.listener.report-frequency", 50); try { algConf.save(new File("configure.txt")); } catch (ConfigurationException e) { // TODO Auto-generated catch block e.printStackTrace(); } net.sourceforge.jclec.RunExperiment.main(new String[] { "configure.txt" }); } }