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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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/*
 *   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 .
 */

/*
 * ExperimenterDefaults.java
 * Copyright (C) 2005-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.gui.experiment;

import weka.core.Utils;
import weka.experiment.PairedCorrectedTTester;
import weka.experiment.ResultMatrix;
import weka.experiment.ResultMatrixPlainText;
import weka.experiment.Tester;

import java.io.File;
import java.io.Serializable;
import java.util.Collections;
import java.util.Enumeration;
import java.util.Properties;
import java.util.Vector;

/**
 * This class offers get methods for the default Experimenter settings in the
 * props file weka/gui/experiment/Experimenter.props.
 * 
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 11944 $
 * @see #PROPERTY_FILE
 */
public class ExperimenterDefaults implements Serializable {

  /** for serialization. */
  static final long serialVersionUID = -2835933184632147981L;

  /** The name of the properties file. */
  public final static String PROPERTY_FILE = "weka/gui/experiment/Experimenter.props";

  /** Properties associated with the experimenter options. */
  protected static Properties PROPERTIES;
  static {
    try {
      PROPERTIES = Utils.readProperties(PROPERTY_FILE);
    } catch (Exception e) {
      System.err.println("Problem reading properties. Fix before continuing.");
      e.printStackTrace();
      PROPERTIES = new Properties();
    }
  }

  /**
   * returns the value for the specified property, if non-existent then the
   * default value.
   * 
   * @param property the property to retrieve the value for
   * @param defaultValue the default value for the property
   * @return the value of the specified property
   */
  public static String get(String property, String defaultValue) {
    return PROPERTIES.getProperty(property, defaultValue);
  }

  /**
   * returns the associated properties file.
   * 
   * @return the props file
   */
  public final static Properties getProperties() {
    return PROPERTIES;
  }

  /**
   * returns the class name of the default setup panel.
   *
   * @return the class name
   */
  public final static String getSetupPanel() {
    return get("SetupPanel", SimpleSetupPanel.class.getName());
  }

  /**
   * returns the default experiment extension.
   * 
   * @return the extension (incl. dot)
   */
  public final static String getExtension() {
    return get("Extension", ".exp");
  }

  /**
   * returns the default destination.
   * 
   * @return the destination
   */
  public final static String getDestination() {
    return get("Destination", "ARFF file");
  }

  /**
   * returns the default experiment type.
   * 
   * @return the type
   */
  public final static String getExperimentType() {
    return get("ExperimentType", "Cross-validation");
  }

  /**
   * whether classification or regression is used.
   * 
   * @return true if classification
   */
  public final static boolean getUseClassification() {
    return Boolean.valueOf(get("UseClassification", "true")).booleanValue();
  }

  /**
   * returns the number of folds used for cross-validation.
   * 
   * @return the number of folds
   */
  public final static int getFolds() {
    return Integer.parseInt(get("Folds", "10"));
  }

  /**
   * returns the training percentage in case of splits.
   * 
   * @return the percentage (0-100)
   */
  public final static double getTrainPercentage() {
    return Integer.parseInt(get("TrainPercentage", "66"));
  }

  /**
   * returns the number of repetitions to use.
   * 
   * @return the repetitions/runs
   */
  public final static int getRepetitions() {
    return Integer.parseInt(get("Repetitions", "10"));
  }

  /**
   * whether datasets or algorithms are iterated first.
   * 
   * @return true if datasets are iterated first
   */
  public final static boolean getDatasetsFirst() {
    return Boolean.valueOf(get("DatasetsFirst", "true")).booleanValue();
  }

  /**
   * returns the initial directory for the datasets (if empty, it returns the
   * user's home directory).
   * 
   * @return the directory
   */
  public final static File getInitialDatasetsDirectory() {
    String dir;

    dir = get("InitialDatasetsDirectory", "");
    if (dir.equals("")) {
      dir = System.getProperty("user.dir");
    }

    return new File(dir);
  }

  /**
   * whether relative paths are used by default.
   * 
   * @return true if relative paths are used
   */
  public final static boolean getUseRelativePaths() {
    return Boolean.valueOf(get("UseRelativePaths", "false")).booleanValue();
  }

  /**
   * returns the display name of the preferred Tester algorithm.
   * 
   * @return the display name
   * @see Tester
   * @see PairedCorrectedTTester
   */
  public final static String getTester() {
    return get("Tester", new PairedCorrectedTTester().getDisplayName());
  }

  /**
   * the comma-separated list of attribute names that identify a row.
   * 
   * @return the attribute list
   */
  public final static String getRow() {
    return get("Row", "Key_Dataset");
  }

  /**
   * the comma-separated list of attribute names that identify a column.
   * 
   * @return the attribute list
   */
  public final static String getColumn() {
    return get("Column", "Key_Scheme,Key_Scheme_options,Key_Scheme_version_ID");
  }

  /**
   * returns the name of the field used for comparison.
   * 
   * @return the comparison field
   */
  public final static String getComparisonField() {
    return get("ComparisonField", "percent_correct");
  }

  /**
   * returns the default significance.
   * 
   * @return the significance (0.0-1.0)
   */
  public final static double getSignificance() {
    return Double.parseDouble(get("Significance", "0.05"));
  }

  /**
   * returns the default sorting (empty string means none).
   * 
   * @return the sorting field
   */
  public final static String getSorting() {
    return get("Sorting", "");
  }

  /**
   * returns whether StdDevs are shown by default.
   * 
   * @return true if stddevs are shown
   */
  public final static boolean getShowStdDevs() {
    return Boolean.valueOf(get("ShowStdDev", "false")).booleanValue();
  }

  /**
   * returns whether the Average is shown by default.
   * 
   * @return true if the average is shown
   */
  public final static boolean getShowAverage() {
    return Boolean.valueOf(get("ShowAverage", "false")).booleanValue();
  }

  /**
   * returns the default precision for the mean.
   * 
   * @return the decimals of the mean
   */
  public final static int getMeanPrecision() {
    return Integer.parseInt(get("MeanPrecision", "2"));
  }

  /**
   * returns the default precision for the stddevs.
   * 
   * @return the decimals of the stddevs
   */
  public final static int getStdDevPrecision() {
    return Integer.parseInt(get("StdDevPrecision", "2"));
  }

  /**
   * returns the classname (and optional options) of the ResultMatrix class,
   * responsible for the output format.
   * 
   * @return the classname and options
   * @see ResultMatrix
   * @see ResultMatrixPlainText
   */
  public final static ResultMatrix getOutputFormat() {
    ResultMatrix result;

    try {
      String[] options = Utils
        .splitOptions(get(
          "OutputFormat",
          ResultMatrix.class.getName()
            + " -col-name-width 0 -row-name-width 25 -mean-width 0 -stddev-width 0 -sig-width 0 -count-width 5 -print-col-names -print-row-names -enum-col-names"));
      String classname = options[0];
      options[0] = "";
      result = (ResultMatrix) Utils.forName(ResultMatrix.class, classname,
        options);
    } catch (Exception e) {
      e.printStackTrace();
      result = new ResultMatrixPlainText();
    }

    // override with other default properties
    result.setMeanPrec(getMeanPrecision());
    result.setStdDevPrec(getStdDevPrecision());
    result.setShowAverage(getShowAverage());
    result.setShowStdDev(getShowStdDevs());
    result.setRemoveFilterName(getRemoveFilterClassnames());

    return result;
  }

  /**
   * whether the filter classnames in the dataset names are removed by default.
   * 
   * @return true if filter names are removed
   */
  public final static boolean getRemoveFilterClassnames() {
    return Boolean.valueOf(get("RemoveFilterClassnames", "false"))
      .booleanValue();
  }

  /**
   * only for testing - prints the content of the props file.
   * 
   * @param args commandline parameters - ignored
   */
  public static void main(String[] args) {
    Enumeration names;
    String name;
    Vector sorted;

    System.out.println("\nExperimenter defaults:");
    names = PROPERTIES.propertyNames();

    // sort names
    sorted = new Vector();
    while (names.hasMoreElements()) {
      sorted.add(names.nextElement().toString());
    }
    Collections.sort(sorted);
    names = sorted.elements();

    // output
    while (names.hasMoreElements()) {
      name = names.nextElement().toString();
      System.out.println("- " + name + ": " + PROPERTIES.getProperty(name, ""));
    }
    System.out.println();
  }
}