source: branches/MetisMQI/src/main/java/weka/datagenerators/classifiers/regression/Expression.java

/*
 *    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 2 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, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

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

package weka.datagenerators.classifiers.regression;

import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.DenseInstance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.filters.unsupervised.attribute.AddExpression;

import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

/**
 <!-- globalinfo-start -->
 * A data generator for generating y according to a given expression out of randomly generated x.<br/>
 * E.g., the mexican hat can be generated like this:<br/>
 *    sin(abs(a1)) / abs(a1)<br/>
 * In addition to this function, the amplitude can be changed and gaussian noise can be added.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -h
 *  Prints this help.</pre>
 * 
 * <pre> -o &lt;file&gt;
 *  The name of the output file, otherwise the generated data is
 *  printed to stdout.</pre>
 * 
 * <pre> -r &lt;name&gt;
 *  The name of the relation.</pre>
 * 
 * <pre> -d
 *  Whether to print debug informations.</pre>
 * 
 * <pre> -S
 *  The seed for random function (default 1)</pre>
 * 
 * <pre> -n &lt;num&gt;
 *  The number of examples to generate (default 100)</pre>
 * 
 * <pre> -A &lt;num&gt;
 *  The amplitude multiplier (default 1.0).</pre>
 * 
 * <pre> -R &lt;num&gt;..&lt;num&gt;
 *  The range x is randomly drawn from (default -10.0..10.0).</pre>
 * 
 * <pre> -N &lt;num&gt;
 *  The noise rate (default 0.0).</pre>
 * 
 * <pre> -V &lt;num&gt;
 *  The noise variance (default 1.0).</pre>
 * 
 * <pre> -E &lt;expression&gt;
 *  The expression to use for generating y out of x 
 *  (default sin(abs(a1)) / abs(a1)).</pre>
 * 
 <!-- options-end -->
 *
 * @author  FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 5987 $
 * @see     AddExpression
 * @see     MexicanHat
 */

public class Expression
  extends MexicanHat {

  /** for serialization */
  static final long serialVersionUID = -4237047357682277211L;  
  
  /** the expression for computing y */
  protected String m_Expression;

  /** the filter for generating y out of x */
  protected AddExpression m_Filter;

  /** the input data structure for the filter */
  protected Instances m_RawData;
  
  /**
   * initializes the generator
   */
  public Expression() {
    super();

    setExpression(defaultExpression());
  }
  
  /**
   * Returns a string describing this data generator.
   *
   * @return a description of the data generator suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "A data generator for generating y according to a given expression "
        + "out of randomly generated x.\n"
        + "E.g., the mexican hat can be generated like this:\n"
        + "   sin(abs(a1)) / abs(a1)\n"
        + "In addition to this function, the amplitude can be changed and "
        + "gaussian noise can be added.";
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options
   */
  public Enumeration listOptions() {
    Vector result = enumToVector(super.listOptions());

    result.addElement(new Option(
              "\tThe expression to use for generating y out of x \n"
              + "\t(default " + defaultExpression() + ").",
              "E", 1, "-E <expression>"));

    return result.elements();
  }
  
  /**
   * Parses a list of options for this object. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -h
   *  Prints this help.</pre>
   * 
   * <pre> -o &lt;file&gt;
   *  The name of the output file, otherwise the generated data is
   *  printed to stdout.</pre>
   * 
   * <pre> -r &lt;name&gt;
   *  The name of the relation.</pre>
   * 
   * <pre> -d
   *  Whether to print debug informations.</pre>
   * 
   * <pre> -S
   *  The seed for random function (default 1)</pre>
   * 
   * <pre> -n &lt;num&gt;
   *  The number of examples to generate (default 100)</pre>
   * 
   * <pre> -A &lt;num&gt;
   *  The amplitude multiplier (default 1.0).</pre>
   * 
   * <pre> -R &lt;num&gt;..&lt;num&gt;
   *  The range x is randomly drawn from (default -10.0..10.0).</pre>
   * 
   * <pre> -N &lt;num&gt;
   *  The noise rate (default 0.0).</pre>
   * 
   * <pre> -V &lt;num&gt;
   *  The noise variance (default 1.0).</pre>
   * 
   * <pre> -E &lt;expression&gt;
   *  The expression to use for generating y out of x 
   *  (default sin(abs(a1)) / abs(a1)).</pre>
   * 
   <!-- options-end -->
   *
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {
    String        tmpStr;
   
    super.setOptions(options);

    tmpStr = Utils.getOption('E', options);
    if (tmpStr.length() != 0)
      setExpression(tmpStr);
    else
      setExpression(defaultExpression());
  }

  /**
   * Gets the current settings of the datagenerator BIRCHCluster.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String[] getOptions() {
    Vector        result;
    String[]      options;
    int           i;
    
    result  = new Vector();
    options = super.getOptions();
    for (i = 0; i < options.length; i++)
      result.add(options[i]);
    
    result.add("-E"); 
    result.add("" + getExpression());
    
    return (String[]) result.toArray(new String[result.size()]);
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String amplitudeTipText() {
    return "The amplitude to multiply the y value with.";
  }

  /**
   * returns the default expression
   * 
   * @return the default expression
   */
  protected String defaultExpression() {
    return "sin(abs(a1)) / abs(a1)";
  }

  /**
   * Gets the mathematical expression for generating y out of x
   *
   * @return the expression for computing y
   */
  public String getExpression() { 
    return m_Expression; 
  }
  
  /**
   * Sets the mathematical expression to generate y out of x.
   *
   * @param value the expression for computing y
   */
  public void setExpression(String value) {
    if (value.length() != 0)
      m_Expression = value;
    else
      throw new IllegalArgumentException(
          "An expression has to be provided!");
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String expressionTipText() {
    return "The expression for generating y out of x.";
  }

  /**
   * Return if single mode is set for the given data generator
   * mode depends on option setting and or generator type.
   * 
   * @return single mode flag
   * @throws Exception if mode is not set yet
   */
  public boolean getSingleModeFlag() throws Exception {
    return true;
  }

  /**
   * Initializes the format for the dataset produced. 
   * Must be called before the generateExample or generateExamples
   * methods are used.
   * Re-initializes the random number generator with the given seed.
   *
   * @return the format for the dataset 
   * @throws Exception if the generating of the format failed
   * @see  #getSeed()
   */
  public Instances defineDataFormat() throws Exception {
    FastVector      atts;

    // initialize input format
    atts = new FastVector();
    atts.addElement(new Attribute("x"));
    
    m_RawData = new Instances(getRelationNameToUse(), atts, 0);

    m_Filter = new AddExpression();
    m_Filter.setName("y");
    m_Filter.setExpression(getExpression());
    m_Filter.setInputFormat(m_RawData);

    return super.defineDataFormat();
  }

  /**
   * Generates one example of the dataset. 
   *
   * @return the generated example
   * @throws Exception if the format of the dataset is not yet defined
   * @throws Exception if the generator only works with generateExamples
   * which means in non single mode
   */
  public Instance generateExample() throws Exception {
    Instance    result;
    Random      rand;
    double      x;
    double      y;
    double[]    atts;
    Instance    inst;

    result = null;
    rand   = getRandom();

    if (m_DatasetFormat == null)
      throw new Exception("Dataset format not defined.");

    // random x
    x = rand.nextDouble();
    // fit into range
    x = x * (getMaxRange() - getMinRange()) + getMinRange();
    
    // generate y
    atts    = new double[1];
    atts[0] = x;
    inst    = new DenseInstance(1.0, atts);
    m_Filter.input(inst);
    m_Filter.batchFinished();
    inst = m_Filter.output();
    
    // noise
    y = inst.value(1) + getAmplitude() 
            * m_NoiseRandom.nextGaussian() 
            * getNoiseRate() * getNoiseVariance();

    // generate attributes
    atts = new double[m_DatasetFormat.numAttributes()];
    
    atts[0] = x;
    atts[1] = y;
    result = new DenseInstance(1.0, atts);

    // dataset reference
    result.setDataset(m_DatasetFormat);
    
    return result;
  }

  /**
   * Generates all examples of the dataset. Re-initializes the random number
   * generator with the given seed, before generating instances.
   *
   * @return the generated dataset
   * @throws Exception if the format of the dataset is not yet defined
   * @throws Exception if the generator only works with generateExample,
   * which means in single mode
   * @see   #getSeed()
   */
  public Instances generateExamples() throws Exception {
    Instances       result;
    int             i;

    result   = new Instances(m_DatasetFormat, 0);
    m_Random = new Random(getSeed());

    for (i = 0; i < getNumExamplesAct(); i++)
      result.add(generateExample());
    
    return result;
  }

  /**
   * Generates a comment string that documentates the data generator.
   * By default this string is added at the beginning of the produced output
   * as ARFF file type, next after the options.
   * 
   * @return string contains info about the generated rules
   */
  public String generateStart () {
    return "";
  }

  /**
   * Generates a comment string that documentats the data generator.
   * By default this string is added at the end of theproduces output
   * as ARFF file type.
   * 
   * @return string contains info about the generated rules
   * @throws Exception if the generating of the documentaion fails
   */
  public String generateFinished() throws Exception {
    return "";
  }
  
  /**
   * Returns the revision string.
   * 
   * @return            the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 5987 $");
  }

  /**
   * Main method for testing this class.
   *
   * @param args should contain arguments for the data producer: 
   */
  public static void main(String[] args) {
    runDataGenerator(new Expression(), args);
  }
}