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weka.classifiers.functions.supportVector.StringKernel Maven / Gradle / Ivy

/*
 *   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 .
 */

/*
 * StringKernel.java
 * Copyright (C) 2006-2012 University of Waikato, Hamilton, New Zealand
 */

package weka.classifiers.functions.supportVector;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;

/**
 *  Implementation of the subsequence kernel (SSK) as
 * described in [1] and of the subsequence kernel with lambda pruning (SSK-LP)
 * as described in [2].

 * 

 * For more information, see

 * 

 * Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher
 * J. C. H. Watkins (2002). Text Classification using String Kernels. Journal of
 * Machine Learning Research. 2:419-444.

 * 

 * F. Kleedorfer, A. Seewald (2005). Implementation of a String Kernel for WEKA.
 * Wien, Austria.
 * 

 * 
 * 
 *  BibTeX:
 * 
 * 
 * @article{Lodhi2002,
 *    author = {Huma Lodhi and Craig Saunders and John Shawe-Taylor and Nello Cristianini and Christopher J. C. H. Watkins},
 *    journal = {Journal of Machine Learning Research},
 *    pages = {419-444},
 *    title = {Text Classification using String Kernels},
 *    volume = {2},
 *    year = {2002},
 *    HTTP = {http://www.jmlr.org/papers/v2/lodhi02a.html}
 * }
 * 
 * @techreport{Kleedorfer2005,
 *    address = {Wien, Austria},
 *    author = {F. Kleedorfer and A. Seewald},
 *    institution = {Oesterreichisches Forschungsinstitut fuer Artificial Intelligence},
 *    number = {TR-2005-13},
 *    title = {Implementation of a String Kernel for WEKA},
 *    year = {2005}
 * }
 * 
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -D
 *  Enables debugging output (if available) to be printed.
 *  (default: off)
 * 
 *
 * 
 * -P <0|1>
 *  The pruning method to use:
 *  0 = No pruning
 *  1 = Lambda pruning
 *  (default: 0)
 * 
 * 
 * 
 * -C <num>
 *  The size of the cache (a prime number).
 *  (default: 250007)
 * 
 * 
 * 
 * -IC <num>
 *  The size of the internal cache (a prime number).
 *  (default: 200003)
 * 
 * 
 * 
 * -L <num>
 *  The lambda constant. Penalizes non-continuous subsequence
 *  matches. Must be in (0,1).
 *  (default: 0.5)
 * 
 * 
 * 
 * -ssl <num>
 *  The length of the subsequence.
 *  (default: 3)
 * 
 * 
 * 
 * -ssl-max <num>
 *  The maximum length of the subsequence.
 *  (default: 9)
 * 
 * 
 * 
 * -N
 *  Use normalization.
 *  (default: no)
 * 
 * 
 * 
 * 
 * 
Theory
 * 
Overview
 * The algorithm computes a measure of similarity between two texts based on the
 * number and form of their common subsequences, which need not be contiguous.
 * This method can be parametrized by specifying the subsequence length k, the
 * penalty factor lambda, which penalizes non-contiguous matches, and optional
 * 'lambda pruning', which takes maxLambdaExponent, m, as
 * parameter. Lambda pruning causes very 'stretched' substring matches not to be
 * counted, thus speeding up the computation. The functionality of SSK and
 * SSK-LP is explained in the following using simple examples.
 * 
 * 
Explanation & Examples
 * for all of the following examples, we assume these parameter values:
 * 
 * 
 * k=2
 * lambda=0.5
 * m=8 (for SSK-LP examples)
 * 
 * 
 * 
SSK
 * 
 * 
Example 1
 * 
 * 
 * SSK(2,"ab","axb")=0.5^5 = 0,03125
 * 
 * 
 * There is one subsequence of the length of 2 that both strings have in common,
 * "ab". The result of SSK is computed by raising lambda to the power of L,
 * where L is the length of the subsequence match in the one string plus the
 * length of the subsequence match in the other, in our case:
 * 
 * 
 *    ab    axb
 * L= 2  +   3 = 5
 * 
 * 
 * hence, the kernel yields 0.5^5 = 0,03125
 * 
 * 
Example 2
 * 
 * 
 * SSK(2,"ab","abb")=0.5^5 + 0.5^4 = 0,09375
 * 
 * 
 * Here, we also have one subsequence of the length of 2 that both strings have
 * in common, "ab". The result of SSK is actually computed by summing over all
 * values computed for each occurrence of a common subsequence match. In this
 * example, there are two possible cases:
 * 
 * 
 * ab    abb
 * --    --  L=4
 * --    - - L=5
 * 
 * 
 * we have two matches, one of the length of 2+2=4, one of the length of 2+3=5,
 * so we get the result 0.5^5 + 0.5^4 = 0,09375.
 * 
 * 
SSK-LP
 * Without lambda pruning, the string kernel finds *all* common subsequences of
 * the given length, whereas with lambda pruning, common subsequence matches
 * that are too much stretched in both strings are not taken into account. It is
 * argued that the value yielded for such a common subsequence is too low (
 * lambda ^(length[match_in_s] + length[match_in_t]) . Tests have
 * shown that a tremendous speedup can be achieved using this technique while
 * suffering from very little quality loss. 

 * Lambda pruning is parametrized by the maximum lambda exponent. It is a good
 * idea to choose that value to be about 3 or 4 times the subsequence length as
 * a rule of thumb. YMMV.
 * 
 * 
Example 3
 * Without lambda pruning, one common subsequence, "AB" would be found in the
 * following two strings. (With k=2)
 * 
 * 
 * SSK(2,"ab","axb")=0.5^14 = 0,00006103515625
 * 
 * 
 * lambda pruning allows for the control of the match length. So, if m (the
 * maximum lambda exponent) is e.g. 8, these two strings would yield a kernel
 * value of 0:
 * 
 * 
 * with lambda pruning:    SSK-LP(2,8,"AxxxxxxxxxB","AyB")= 0
 * without lambda pruning: SSK(2,"AxxxxxxxxxB","AyB")= 0.5^14 = 0,00006103515625
 * 
 * 
 * This is because the exponent for lambda (=the length of the subsequence
 * match) would be 14, which is > 8. In Contrast, the next result is > 0
 * 
 * 
 * m=8
 * SSK-LP(2,8,"AxxB","AyyB")=0.5^8 = 0,00390625
 * 
 * 
 * because the lambda exponent would be 8, which is just accepted by lambda
 * pruning.
 * 
 * 
Normalization
 * When the string kernel is used for its main purpose, as the kernel of a
 * support vector machine, it is not normalized. The normalized kernel can be
 * switched on by -F (feature space normalization) but is much slower. Like most
 * unnormalized kernels, K(x,x) is not a fixed value, see the next example.
 * 
 * 
Example 4
 * 
 * 
 * SSK(2,"ab","ab")=0.5^4 = 0.0625
 * SSK(2,"AxxxxxxxxxB","AxxxxxxxxxB") = 12.761724710464478
 * 
 * 
 * SSK is evaluated twice, each time for two identical strings. A good measure
 * of similarity would produce the same value in both cases, which should
 * indicate the same level of similarity. The value of the normalized SSK would
 * be 1.0 in both cases. So for the purpose of computing string similarity the
 * normalized kernel should be used. For SVM the unnormalized kernel is usually
 * sufficient.
 * 
 * 
Complexity of SSK and SSK-LP
 * The time complexity of this method (without lambda pruning and with an
 * infinitely large cache) is

 * 
 * 
 * O(k*|s|*|t|)
 * 
 * 
 * Lambda Pruning has a complexity (without caching) of

 * 
 * 
 * O(m*binom(m,k)^2*(|s|+n)*|t|)
 * 
 * 
 * 

 * 
 * 
 * k...          subsequence length (ssl)
 * s,t...        strings
 * |s|...        length of string s
 * binom(x,y)... binomial coefficient (x!/[(x-y)!y!])
 * m...          maxLambdaExponent (ssl-max)
 * 
 * 
 * Keep in mind that execution time can increase fast for long strings and big
 * values for k, especially if you don't use lambda pruning. With lambda
 * pruning, computation is usually so fast that switching on the cache leads to
 * slower computation because of setup costs. Therefore caching is switched off
 * for lambda pruning. 

 * 

 * For details and qualitative experiments about SSK, see [1] 

 * For details about lambda pruning and performance comparison of SSK and SSK-LP
 * (SSK with lambda pruning), see [2] Note that the complexity estimation in [2]
 * assumes no caching of intermediate results, which has been implemented in the
 * meantime and greatly improves the speed of the SSK without lambda pruning. 

 * 
 * 
Notes for usage within Weka
 * Only instances of the following form can be processed using string kernels:
 * 
 * 
 * +----------+-------------+---------------+
 * |attribute#|     0       |       1       |
 * +----------+-------------+---------------+
 * | content  | [text data] | [class label] |
 * +----------------------------------------+
 *  ... or ...
 * +----------+---------------+-------------+
 * |attribute#|     0         |     1       |
 * +----------+---------------+-------------+
 * | content  | [class label] | [text data] |
 * +----------------------------------------+
 * 
 * 
 * @author Florian Kleedorfer ([email protected])
 * @author Alexander K. Seewald ([email protected])
 * @version $Revision: 14512 $
 */
public class StringKernel extends Kernel implements TechnicalInformationHandler {

  /** for serialization */
  private static final long serialVersionUID = -4902954211202690123L;

  /** The size of the cache (a prime number) */
  private int m_cacheSize = 250007;

  /** The size of the internal cache for intermediate results (a prime number) */
  private int m_internalCacheSize = 200003;

  /** The attribute number of the string attribute */
  private int m_strAttr;

  /** Kernel cache (i.e., cache for kernel evaluations) */
  private double[] m_storage;
  private long[] m_keys;

  /** Counts the number of kernel evaluations. */
  private int m_kernelEvals;

  /** The number of instance in the dataset */
  private int m_numInsts;

  /** Pruning method: No Pruning */
  public final static int PRUNING_NONE = 0;
  /** Pruning method: Lambda See [2] for details. */
  public final static int PRUNING_LAMBDA = 1;
  /** Pruning methods */
  public static final Tag[] TAGS_PRUNING = {
    new Tag(PRUNING_NONE, "No pruning"),
    new Tag(PRUNING_LAMBDA, "Lambda pruning"), };

  /** the pruning method */
  protected int m_PruningMethod = PRUNING_NONE;

  /**
   * the decay factor that penalizes non-continuous substring matches. See [1]
   * for details.
   */
  protected double m_lambda = 0.5;

  /** The substring length */
  private int m_subsequenceLength = 3;

  /** The maximum substring length for lambda pruning */
  private int m_maxSubsequenceLength = 9;

  /**
   * powers of lambda are prepared prior to kernel evaluations. all powers
   * between 0 and this value are precalculated
   */
  protected static final int MAX_POWER_OF_LAMBDA = 10000;

  /** the precalculated powers of lambda */
  protected double[] m_powersOflambda = null;

  /**
   * flag for switching normalization on or off. This defaults to false and can
   * be turned on by the switch for feature space normalization in SMO
   */
  private boolean m_normalize = false;

  /** private cache for intermediate results */
  private int maxCache; // is set in unnormalizedKernel(s1,s2)
  private double[] cachekh;
  private int[] cachekhK;
  private double[] cachekh2;
  private int[] cachekh2K;
  /** cached indexes for private cache */
  private int m_multX;
  private int m_multY;
  private int m_multZ;
  private int m_multZZ;

  private boolean m_useRecursionCache = true;

  /**
   * default constructor
   */
  public StringKernel() {
    super();
  }

  /**
   * creates a new StringKernel object. Initializes the kernel cache and the
   * 'lambda cache', i.e. the precalculated powers of lambda from lambda^2 to
   * lambda^MAX_POWER_OF_LAMBDA
   * 
   * @param data the dataset to use
   * @param cacheSize the size of the cache
   * @param subsequenceLength the subsequence length
   * @param lambda the lambda value
   * @param debug whether to output debug information
   * @throws Exception if something goes wrong
   */
  public StringKernel(Instances data, int cacheSize, int subsequenceLength,
    double lambda, boolean debug) throws Exception {

    setDebug(debug);
    setCacheSize(cacheSize);
    setInternalCacheSize(200003);
    setSubsequenceLength(subsequenceLength);
    setMaxSubsequenceLength(-1);
    setLambda(lambda);

    buildKernel(data);
  }

  /**
   * Returns a string describing the kernel
   * 
   * @return a description suitable for displaying in the explorer/experimenter
   *         gui
   */
  @Override
  public String globalInfo() {
    return "Implementation of the subsequence kernel (SSK) as described in [1] "
      + "and of the subsequence kernel with lambda pruning (SSK-LP) as "
      + "described in [2].\n\n"
      + "For more information, see\n\n"
      + getTechnicalInformation().toString();
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing detailed
   * information about the technical background of this class, e.g., paper
   * reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  @Override
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation result;
    TechnicalInformation additional;

    result = new TechnicalInformation(Type.ARTICLE);
    result
      .setValue(
        Field.AUTHOR,
        "Huma Lodhi and Craig Saunders and John Shawe-Taylor and Nello Cristianini and Christopher J. C. H. Watkins");
    result.setValue(Field.YEAR, "2002");
    result.setValue(Field.TITLE, "Text Classification using String Kernels");
    result.setValue(Field.JOURNAL, "Journal of Machine Learning Research");
    result.setValue(Field.VOLUME, "2");
    result.setValue(Field.PAGES, "419-444");
    result.setValue(Field.HTTP, "http://www.jmlr.org/papers/v2/lodhi02a.html");

    additional = result.add(Type.TECHREPORT);
    additional.setValue(Field.AUTHOR, "F. Kleedorfer and A. Seewald");
    additional.setValue(Field.YEAR, "2005");
    additional.setValue(Field.TITLE,
      "Implementation of a String Kernel for WEKA");
    additional.setValue(Field.INSTITUTION,
      "Oesterreichisches Forschungsinstitut fuer Artificial Intelligence");
    additional.setValue(Field.ADDRESS, "Wien, Austria");
    additional.setValue(Field.NUMBER, "TR-2005-13");

    return result;
  }

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

    String desc;
    String param;
    int i;
    SelectedTag tag;

    result.addAll(Collections.list(super.listOptions()));

    desc = "";
    param = "";
    for (i = 0; i < TAGS_PRUNING.length; i++) {
      if (i > 0) {
        param += "|";
      }
      tag = new SelectedTag(TAGS_PRUNING[i].getID(), TAGS_PRUNING);
      param += "" + tag.getSelectedTag().getID();
      desc += "\t" + tag.getSelectedTag().getID() + " = "
        + tag.getSelectedTag().getReadable() + "\n";
    }

    result.addElement(new Option("\tThe pruning method to use:\n" + desc
      + "\t(default: " + PRUNING_NONE + ")", "P", 1, "-P <" + param + ">"));

    result.addElement(new Option("\tThe size of the cache (a prime number).\n"
      + "\t(default: 250007)", "C", 1, "-C "));

    result.addElement(new Option(
      "\tThe size of the internal cache (a prime number).\n"
        + "\t(default: 200003)", "IC", 1, "-IC "));

    result.addElement(new Option(
      "\tThe lambda constant. Penalizes non-continuous subsequence\n"
        + "\tmatches. Must be in (0,1).\n" + "\t(default: 0.5)", "L", 1,
      "-L "));

    result.addElement(new Option("\tThe length of the subsequence.\n"
      + "\t(default: 3)", "ssl", 1, "-ssl "));

    result.addElement(new Option("\tThe maximum length of the subsequence.\n"
      + "\t(default: 9)", "ssl-max", 1, "-ssl-max "));

    result.addElement(new Option("\tUse normalization.\n" + "\t(default: no)",
      "N", 0, "-N"));

    return result.elements();
  }

  /**
   * Parses a given list of options.
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -D
   *  Enables debugging output (if available) to be printed.
   *  (default: off)
   * 
   *
   * 
   * -P <0|1>
   *  The pruning method to use:
   *  0 = No pruning
   *  1 = Lambda pruning
   *  (default: 0)
   * 
   * 
   * 
   * -C <num>
   *  The size of the cache (a prime number).
   *  (default: 250007)
   * 
   * 
   * 
   * -IC <num>
   *  The size of the internal cache (a prime number).
   *  (default: 200003)
   * 
   * 
   * 
   * -L <num>
   *  The lambda constant. Penalizes non-continuous subsequence
   *  matches. Must be in (0,1).
   *  (default: 0.5)
   * 
   * 
   * 
   * -ssl <num>
   *  The length of the subsequence.
   *  (default: 3)
   * 
   * 
   * 
   * -ssl-max <num>
   *  The maximum length of the subsequence.
   *  (default: 9)
   * 
   * 
   * 
   * -N
   *  Use normalization.
   *  (default: no)
   * 
   * 
   * 
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {
    String tmpStr;

    tmpStr = Utils.getOption('P', options);
    if (tmpStr.length() != 0) {
      setPruningMethod(new SelectedTag(Integer.parseInt(tmpStr), TAGS_PRUNING));
    } else {
      setPruningMethod(new SelectedTag(PRUNING_NONE, TAGS_PRUNING));
    }

    tmpStr = Utils.getOption('C', options);
    if (tmpStr.length() != 0) {
      setCacheSize(Integer.parseInt(tmpStr));
    } else {
      setCacheSize(250007);
    }

    tmpStr = Utils.getOption("IC", options);
    if (tmpStr.length() != 0) {
      setInternalCacheSize(Integer.parseInt(tmpStr));
    } else {
      setInternalCacheSize(200003);
    }

    tmpStr = Utils.getOption('L', options);
    if (tmpStr.length() != 0) {
      setLambda(Double.parseDouble(tmpStr));
    } else {
      setLambda(0.5);
    }

    tmpStr = Utils.getOption("ssl", options);
    if (tmpStr.length() != 0) {
      setSubsequenceLength(Integer.parseInt(tmpStr));
    } else {
      setSubsequenceLength(3);
    }

    tmpStr = Utils.getOption("ssl-max", options);
    if (tmpStr.length() != 0) {
      setMaxSubsequenceLength(Integer.parseInt(tmpStr));
    } else {
      setMaxSubsequenceLength(9);
    }

    setUseNormalization(Utils.getFlag('N', options));

    if (getMaxSubsequenceLength() < 2 * getSubsequenceLength()) {
      throw new IllegalArgumentException(
        "Lambda Pruning forbids even contiguous substring matches! "
          + "Use a bigger value for ssl-max (at least 2*ssl).");
    }

    super.setOptions(options);
  }

  /**
   * Gets the current settings of the Kernel.
   * 
   * @return an array of strings suitable for passing to setOptions
   */
  @Override
  public String[] getOptions() {

    Vector result = new Vector();

    Collections.addAll(result, super.getOptions());

    result.add("-P");
    result.add("" + m_PruningMethod);

    result.add("-C");
    result.add("" + getCacheSize());

    result.add("-IC");
    result.add("" + getInternalCacheSize());

    result.add("-L");
    result.add("" + getLambda());

    result.add("-ssl");
    result.add("" + getSubsequenceLength());

    result.add("-ssl-max");
    result.add("" + getMaxSubsequenceLength());

    if (getUseNormalization()) {
      result.add("-L");
    }

    return 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 pruningMethodTipText() {
    return "The pruning method.";
  }

  /**
   * Sets the method used to for pruning.
   * 
   * @param value the pruning method to use.
   */
  public void setPruningMethod(SelectedTag value) {
    if (value.getTags() == TAGS_PRUNING) {
      m_PruningMethod = value.getSelectedTag().getID();
    }
  }

  /**
   * Gets the method used for pruning.
   * 
   * @return the pruning method to use.
   */
  public SelectedTag getPruningMethod() {
    return new SelectedTag(m_PruningMethod, TAGS_PRUNING);
  }

  /**
   * Sets the size of the cache to use (a prime number)
   * 
   * @param value the size of the cache
   */
  public void setCacheSize(int value) {
    if (value >= 0) {
      m_cacheSize = value;
      clean();
    } else {
      System.out.println("Cache size cannot be smaller than 0 (provided: "
        + value + ")!");
    }
  }

  /**
   * Gets the size of the cache
   * 
   * @return the cache size
   */
  public int getCacheSize() {
    return m_cacheSize;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String cacheSizeTipText() {
    return "The size of the cache (a prime number).";
  }

  /**
   * sets the size of the internal cache for intermediate results. Memory
   * consumption is about 16x this amount in bytes. Only use when lambda pruning
   * is switched off.
   * 
   * @param value the size of the internal cache
   */
  public void setInternalCacheSize(int value) {
    if (value >= 0) {
      m_internalCacheSize = value;
      clean();
    } else {
      System.out.println("Cache size cannot be smaller than 0 (provided: "
        + value + ")!");
    }
  }

  /**
   * Gets the size of the internal cache
   * 
   * @return the cache size
   */
  public int getInternalCacheSize() {
    return m_internalCacheSize;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String internalCacheSizeTipText() {
    return "The size of the internal cache (a prime number).";
  }

  /**
   * Sets the length of the subsequence.
   * 
   * @param value the length
   */
  public void setSubsequenceLength(int value) {
    m_subsequenceLength = value;
  }

  /**
   * Returns the length of the subsequence
   * 
   * @return the length
   */
  public int getSubsequenceLength() {
    return m_subsequenceLength;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String subsequenceLengthTipText() {
    return "The subsequence length.";
  }

  /**
   * Sets the maximum length of the subsequence.
   * 
   * @param value the maximum length
   */
  public void setMaxSubsequenceLength(int value) {
    m_maxSubsequenceLength = value;
  }

  /**
   * Returns the maximum length of the subsequence
   * 
   * @return the maximum length
   */
  public int getMaxSubsequenceLength() {
    return m_maxSubsequenceLength;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String maxSubsequenceLengthTipText() {
    return "The maximum subsequence length (theta in the paper)";
  }

  /**
   * Sets the lambda constant used in the string kernel
   * 
   * @param value the lambda value to use
   */
  public void setLambda(double value) {
    m_lambda = value;
  }

  /**
   * Gets the lambda constant used in the string kernel
   * 
   * @return the current lambda constant
   */
  public double getLambda() {
    return m_lambda;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String lambdaTipText() {
    return "Penalizes non-continuous subsequence matches, from (0,1)";
  }

  /**
   * Sets whether to use normalization. Each time this value is changed, the
   * kernel cache is cleared.
   * 
   * @param value whether to use normalization
   */
  public void setUseNormalization(boolean value) {
    if (value != m_normalize) {
      clean();
    }

    m_normalize = value;
  }

  /**
   * Returns whether normalization is used.
   * 
   * @return true if normalization is used
   */
  public boolean getUseNormalization() {
    return m_normalize;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String useNormalizationTipText() {
    return "Whether to use normalization.";
  }

  /**
   * Computes the result of the kernel function for two instances. If id1 == -1,
   * eval use inst1 instead of an instance in the dataset.
   * 
   * @param id1 the index of the first instance in the dataset
   * @param id2 the index of the second instance in the dataset
   * @param inst1 the instance corresponding to id1 (used if id1 == -1)
   * @return the result of the kernel function
   * @throws Exception if something goes wrong
   */
  @Override
  public double eval(int id1, int id2, Instance inst1) throws Exception {
    if (m_Debug && id1 > -1 && id2 > -1) {
      System.err.println("\nEvaluation of string kernel for");
      System.err.println(m_data.instance(id1).stringValue(m_strAttr));
      System.err.println("and");
      System.err.println(m_data.instance(id2).stringValue(m_strAttr));
    }

    // the normalized kernel returns 1 for comparison of
    // two identical strings
    if (id1 == id2 && m_normalize) {
      return 1.0;
    }

    double result = 0;
    long key = -1;
    int location = -1;

    // we can only cache if we know the indexes
    if ((id1 >= 0) && (m_keys != null)) {
      if (id1 > id2) {
        key = (long) id1 * m_numInsts + id2;
      } else {
        key = (long) id2 * m_numInsts + id1;
      }
      if (key < 0) {
        throw new Exception("Cache overflow detected!");
      }
      location = (int) (key % m_keys.length);
      if (m_keys[location] == (key + 1)) {
        if (m_Debug) {
          System.err.println("result (cached): " + m_storage[location]);
        }
        return m_storage[location];
      }
    }

    m_kernelEvals++;
    long start = System.currentTimeMillis();

    Instance inst2 = m_data.instance(id2);
    char[] s1 = inst1.stringValue(m_strAttr).toCharArray();
    char[] s2 = inst2.stringValue(m_strAttr).toCharArray();

    // prevent the kernel from returning NaN
    if (s1.length == 0 || s2.length == 0) {
      return 0;
    }

    if (m_normalize) {
      result = normalizedKernel(s1, s2);
    } else {
      result = unnormalizedKernel(s1, s2);
    }

    if (m_Debug) {
      long duration = System.currentTimeMillis() - start;
      System.err.println("result: " + result);
      System.err.println("evaluation time:" + duration + "\n");
    }

    // store result in cache
    if (key != -1) {
      m_storage[location] = result;
      m_keys[location] = (key + 1);
    }
    return result;
  }

  /**
   * Frees the memory used by the kernel. (Useful with kernels which use cache.)
   * This function is called when the training is done. i.e. after that, eval
   * will be called with id1 == -1.
   */
  @Override
  public void clean() {
    m_storage = null;
    m_keys = null;
  }

  /**
   * Returns the number of kernel evaluation performed.
   * 
   * @return the number of kernel evaluation performed.
   */
  @Override
  public int numEvals() {
    return m_kernelEvals;
  }

  /**
   * Returns the number of dot product cache hits.
   * 
   * @return the number of dot product cache hits, or -1 if not supported by
   *         this kernel.
   */
  @Override
  public int numCacheHits() {
    // TODO: implement!
    return -1;
  }

  /**
   * evaluates the normalized kernel between s and t. See [1] for details about
   * the normalized SSK.
   * 
   * @param s first input string
   * @param t second input string
   * @return a double indicating their distance, or similarity
   */
  public double normalizedKernel(char[] s, char[] t) {
    double k1 = unnormalizedKernel(s, s);
    double k2 = unnormalizedKernel(t, t);
    double normTerm = Math.sqrt(k1 * k2);
    return unnormalizedKernel(s, t) / normTerm;
  }

  /**
   * evaluates the unnormalized kernel between s and t. See [1] for details
   * about the unnormalized SSK.
   * 
   * @param s first input string
   * @param t second input string
   * @return a double indicating their distance, or similarity
   */
  public double unnormalizedKernel(char[] s, char[] t) {
    if (t.length > s.length) {
      // swap because the algorithm is faster if s is
      // the longer string
      char[] buf = s;
      s = t;
      t = buf;
    }
    if (m_PruningMethod == PRUNING_NONE) {
      m_multX = (s.length + 1) * (t.length + 1);
      m_multY = (t.length + 1);
      m_multZ = 1;
      maxCache = m_internalCacheSize;
      if (maxCache == 0) {
        maxCache = (m_subsequenceLength + 1) * m_multX;
      } else if ((m_subsequenceLength + 1) * m_multX < maxCache) {
        maxCache = (m_subsequenceLength + 1) * m_multX;
      }
      m_useRecursionCache = true;
      cachekhK = new int[maxCache];
      cachekh2K = new int[maxCache];
      cachekh = new double[maxCache];
      cachekh2 = new double[maxCache];
    } else if (m_PruningMethod == PRUNING_LAMBDA) {
      maxCache = 0;
      m_useRecursionCache = false;
    }

    double res;
    if (m_PruningMethod == PRUNING_LAMBDA) {
      res = kernelLP(m_subsequenceLength, s, s.length - 1, t, t.length - 1,
        m_maxSubsequenceLength);
    } else {
      res = kernel(m_subsequenceLength, s, s.length - 1, t, t.length - 1);
    }
    cachekh = null;
    cachekhK = null;
    cachekh2 = null;
    cachekh2K = null;

    return res;
  }

  /**
   * Recursion-ending function that is called at the end of each recursion
   * branch.
   * 
   * @param n
   * @return
   */
  protected double getReturnValue(int n) {
    if (n == 0) {
      return 1;
    } else {
      return 0;
    }
  }

  /**
   * the kernel function (Kn). This function performs the outer loop
   * character-wise over the first input string s. For each character
   * encountered, a recursion branch is started that identifies all subsequences
   * in t starting with that character. 

   * See [1] for details but note that this code is optimized and may be hard to
   * recognize.
   * 
   * @param n the current length of the matching subsequence
   * @param s first string, as a char array
   * @param t second string, as a char array
   * @param endIndexS the portion of s currently regarded is s[1:endIndexS]
   * @param endIndexT the portion of t currently regarded is t[1:endIndexT]
   * @return a double indicating the distance or similarity between s and t,
   *         according to and depending on the initial value for n.
   */
  protected double kernel(int n, char[] s, int endIndexS, char[] t,
    int endIndexT) {

    // normal recursion ending case
    if (Math.min(endIndexS + 1, endIndexT + 1) < n) {
      return getReturnValue(n);
    }

    // accumulate all recursion results in one:
    double result = 0;

    // the tail-recursive function defined in [1] is turned into a
    // loop here, preventing stack overflows.
    // skim s from back to front
    for (int iS = endIndexS; iS > n - 2; iS--) {
      double buf = 0;
      // let the current character in s be x
      char x = s[iS];
      // iterate over all occurrences of x in t
      for (int j = 0; j <= endIndexT; j++) {
        if (t[j] == x) {
          // this is a match for the current character, hence
          // 1. use previous chars in both strings (iS-1, j-1)
          // 2. decrement the remainingMatchLength (n-1)
          // and start a recursion branch for these parameters
          buf += kernelHelper(n - 1, s, iS - 1, t, j - 1);
        }
      }
      // ok, all occurrences of x in t have been found
      // multiply the result with lambda^2
      // (one lambda for x, and the other for all matches of x in t)
      result += buf * m_powersOflambda[2];
    }
    return result;
  }

  /**
   * The kernel helper function, called K' in [1] and [2].
   * 
   * @param n the current length of the matching subsequence
   * @param s first string, as a char array
   * @param t second string, as a char array
   * @param endIndexS the portion of s currently regarded is s[1:endIndexS]
   * @param endIndexT the portion of t currently regarded is t[1:endIndexT]
   * @return a partial result for K
   */
  protected double kernelHelper(int n, char[] s, int endIndexS, char[] t,
    int endIndexT) {

    // recursion ends if the current subsequence has maximal length,
    // which is the case here
    if (n <= 0) {
      return getReturnValue(n);
    }

    // recursion ends, too, if the current subsequence is shorter than
    // maximal length, but there is no chance that it will reach maximal length.
    // in this case, normally 0 is returned, but the EXPERIMENTAL
    // minSubsequenceLength feature allows shorter subsequence matches
    // also to contribute
    if (Math.min(endIndexS + 1, endIndexT + 1) < n) {
      return getReturnValue(n);
    }
    int adr = 0;
    if (m_useRecursionCache) {
      adr = m_multX * n + m_multY * endIndexS + m_multZ * endIndexT;
      if (cachekhK[adr % maxCache] == adr + 1) {
        return cachekh[adr % maxCache];
      }
    }

    // the tail-recursive function defined in [1] is turned into a
    // loop here, preventing stack overflows.
    // loop over s, nearly from the start (skip the first n-1 characters)
    // and only up until endIndexS, and recursively apply K''. Thus, every
    // character between n-1 and endIndexS in s is counted once as
    // being part of the subsequence match and once just as a gap.
    // In both cases lambda is multiplied with the result.
    double result = 0;
    /*
     * for (int iS = n-1; iS <= endIndexS;iS++) { result *= m_lambda; result +=
     * kernelHelper2(n,s,iS, t, endIndexT); } if (m_useRecursionCache) {
     * cachekhK[adr % maxCache]=adr+1; cachekh[adr % maxCache]=result; } return
     * result;
     */
    /* ^^^ again, above code segment does not store some intermediate results... */
    result = m_lambda * kernelHelper(n, s, endIndexS - 1, t, endIndexT)
      + kernelHelper2(n, s, endIndexS, t, endIndexT);
    if (m_useRecursionCache) {
      cachekhK[adr % maxCache] = adr + 1;
      cachekh[adr % maxCache] = result;
    }
    return result;
  }

  /**
   * helper function for the evaluation of the kernel K'' see section 'Efficient
   * Computation of SSK' in [1]
   * 
   * @param n the current length of the matching subsequence
   * @param s first string, as a char array
   * @param t second string, as a char array
   * @param endIndexS the portion of s currently regarded is s[1:endIndexS]
   * @param endIndexT the portion of t currently regarded is t[1:endIndexT]
   * @return a partial result for K'
   */
  protected double kernelHelper2(int n, char[] s, int endIndexS, char[] t,
    int endIndexT) {

    // recursion ends if one of the indices in both strings is <0
    if (endIndexS < 0 || endIndexT < 0) {
      return getReturnValue(n);
    }

    int adr = 0;
    if (m_useRecursionCache) {
      adr = m_multX * n + m_multY * endIndexS + m_multZ * endIndexT;
      if (cachekh2K[adr % maxCache] == adr + 1) {
        return cachekh2[adr % maxCache];
      }
    }

    // spot the last character in s, we'll need it
    char x = s[endIndexS];

    // recurse if the last characters of s and t, x (and y) are identical.
    // which is an easy case: just add up two recursions,
    // 1. one that counts x and y as a part of the subsequence match
    // -> n, endIndexS and endIndexT are decremented for next recursion level
    // -> lambda^2 is multiplied with the result to account for the length
    // of 2 that has been added to the length of the subsequence match
    // by accepting x and y.
    // 2. one that counts y as a gap in the match
    // -> only endIndexT is decremented for next recursion level
    // -> lambda is multiplied with the result to account for the length
    // of 1 that has been added to the length of the subsequence match
    // by omitting y.
    if (x == t[endIndexT]) {
      double ret = m_lambda
        * (kernelHelper2(n, s, endIndexS, t, endIndexT - 1) + m_lambda
          * kernelHelper(n - 1, s, endIndexS - 1, t, endIndexT - 1));
      if (m_useRecursionCache) {
        cachekh2K[adr % maxCache] = adr + 1;
        cachekh2[adr % maxCache] = ret;
      }
      return ret;
    } else {
      double ret = m_lambda * kernelHelper2(n, s, endIndexS, t, endIndexT - 1);
      if (m_useRecursionCache) {
        cachekh2K[adr % maxCache] = adr + 1;
        cachekh2[adr % maxCache] = ret;
      }
      return ret;
    }

    // look for x in t from back to front.
    // this is actually an optimization from [1] that spares unneccessary
    // recursions iff
    // x is actually found in t, but not at the last position.
    /*
     * int i; int threshold = n>0?n-1:0; for (i=endIndexT-1; i >= threshold;i--)
     * { if (x == t[i]) { double ret=getPowerOfLambda(endIndexT-i) *
     * kernelHelper2(n,s,endIndexS, t, i); if (m_useRecursionCache) {
     * cachekh2K[adr % maxCache]=adr+1; cachekh2[adr % maxCache]=ret; } return
     * ret; } }
     */
    // end the recursion if x is not found in t.
    /*
     * double ret = getReturnValue(n); if (m_useRecursionCache) { cachekh2K[adr
     * % maxCache]=adr+1; cachekh2[adr % maxCache]=ret; } return ret;
     */
  }

  /**
   * the kernel function K explained in [1] using lambda pruning, explained in
   * [2]. An additional parameter is introduced, which denotes the maximum
   * length of a subsequence match. This allows for the control of how relaxed
   * the subsequence matches are. 

   * 
   * @param n the current length of the matching subsequence
   * @param s first string, as a char array
   * @param t second string, as a char array
   * @param endIndexS the portion of s currently regarded is s[1:endIndexS]
   * @param endIndexT the portion of t currently regarded is t[1:endIndexT]
   * @param remainingMatchLength actually the initial value for
   *          maxLambdaExponent
   * @return a double indicating the distance or similarity between s and t,
   *         according to and depending on the initial value for n.
   */
  protected double kernelLP(int n, char[] s, int endIndexS, char[] t,
    int endIndexT, int remainingMatchLength) {
    // see code docs in kernel()
    if (Math.min(endIndexS + 1, endIndexT + 1) < n) {
      return getReturnValue(n);
    }
    // lambda pruning check
    // stops recursion if the match is so long that the resulting
    // power of lambda is smaller than minLambda
    // if lambda pruning is not used, the remainingMatchLength is < 0
    // and this check never stops the recursion
    if (remainingMatchLength == 0) {
      return getReturnValue(n);
    }
    double result = 0;
    // see code docs in kernel()
    for (int iS = endIndexS; iS > n - 2; iS--) {
      double buf = 0;
      char x = s[iS];
      for (int j = 0; j <= endIndexT; j++) {
        if (t[j] == x) {
          // both t[j] and x are considered part of the subsequence match, hence
          // subtract 2 from the remainingMatchLength
          buf += kernelHelperLP(n - 1, s, iS - 1, t, j - 1,
            remainingMatchLength - 2);
        }
      }
      result += buf * m_powersOflambda[2];
    }
    return result;
  }

  /**
   * helper function for the evaluation of the kernel (K'n) using lambda pruning
   * 
   * @param n the current length of the matching subsequence
   * @param s first string, as a char array
   * @param t second string, as a char array
   * @param endIndexS the portion of s currently regarded is s[1:endIndexS]
   * @param endIndexT the portion of t currently regarded is t[1:endIndexT]
   * @param remainingMatchLength the number of characters that may still be used
   *          for matching (i.e. gaps + matches in both strings)
   * @return a partial result for K
   */
  protected double kernelHelperLP(int n, char[] s, int endIndexS, char[] t,
    int endIndexT, int remainingMatchLength) {

    // see code docs in kernelHelper()
    if (n == 0) {
      return getReturnValue(n);

    }
    // see code docs in kernelHelper()
    if (Math.min(endIndexS + 1, endIndexT + 1) < n) {
      ;
      return getReturnValue(n);
    }

    // lambda pruning check
    // stops recursion if the match is so long that the resulting
    // power of lambda is smaller than minLambda
    // if lambda pruning is not used, the remainingMatchLength is < 0
    // and this check never stops the recursion
    if (remainingMatchLength < 2 * n) {
      return getReturnValue(n);
    }
    int adr = 0;
    if (m_useRecursionCache) {
      adr = m_multX * n + m_multY * endIndexS + m_multZ * endIndexT + m_multZZ
        * remainingMatchLength;
      if (cachekh2K[adr % maxCache] == adr + 1) {
        return cachekh2[adr % maxCache];
      }
    }

    int rml = 0; // counts the remaining match length
    double result = 0;
    // see code docs in kernelHelper()
    // difference to implementation in kernelHelper:
    // *)choose different starting point, which is found counting
    // the maximal remaining match length from endIndexS.
    // *)keep track of the remaining match length, rml, which is
    // incremented each loop
    for (int iS = (endIndexS - remainingMatchLength); iS <= endIndexS; iS++) {
      result *= m_lambda;
      result += kernelHelper2LP(n, s, iS, t, endIndexT, rml++);
    }

    if (m_useRecursionCache && endIndexS >= 0 && endIndexT >= 0 && n >= 0) {
      cachekhK[adr % maxCache] = adr + 1;
      cachekh[adr % maxCache] = result;
    }
    return result;
  }

  /**
   * helper function for the evaluation of the kernel (K''n) using lambda
   * pruning
   * 
   * @param n the current length of the matching subsequence
   * @param s first string, as a char array
   * @param t second string, as a char array
   * @param endIndexS the portion of s currently regarded is s[1:endIndexS]
   * @param endIndexT the portion of t currently regarded is t[1:endIndexT]
   * @param remainingMatchLength the number of characters that may still be used
   *          for matching (i.e. gaps + matches in both strings)
   * @return a partial result for K'
   */
  protected double kernelHelper2LP(int n, char[] s, int endIndexS, char[] t,
    int endIndexT, int remainingMatchLength) {

    // lambda pruning check
    // stops recursion if the match is so long that the resulting
    // power of lambda is smaller than minLambda
    // if lambda pruning is not used, the remainingMatchLength is < 0
    // and this check never stops the recursion
    // if (remainingMatchLength <= 0) return 0;
    if (remainingMatchLength < 2 * n) {
      return getReturnValue(n);
    }

    // see code docs in kernelHelper2()
    if (endIndexS < 0 || endIndexT < 0) {
      return getReturnValue(n);
    }
    int adr = 0;
    if (m_useRecursionCache) {
      adr = m_multX * n + m_multY * endIndexS + m_multZ * endIndexT + m_multZZ
        * remainingMatchLength;
      if (cachekh2K[adr % maxCache] == adr + 1) {
        return cachekh2[adr % maxCache];
      }
    }

    char x = s[endIndexS];
    if (x == t[endIndexT]) {
      double ret = m_lambda
        * (kernelHelper2LP(n, s, endIndexS, t, endIndexT - 1,
          remainingMatchLength - 1) + m_lambda
          * kernelHelperLP(n - 1, s, endIndexS - 1, t, endIndexT - 1,
            remainingMatchLength - 2));
      if (m_useRecursionCache && endIndexS >= 0 && endIndexT >= 0 && n >= 0) {
        cachekh2K[adr % maxCache] = adr + 1;
        cachekh2[adr % maxCache] = ret;
      }
      return ret;
    }

    // see code docs in kernelHelper()
    // differences to implementation in kernelHelper():
    // *) choose a different ending point for the loop
    // based on the remaining match length
    int i;
    int minIndex = endIndexT - remainingMatchLength;
    if (minIndex < 0) {
      minIndex = 0;
    }
    for (i = endIndexT; i >= minIndex; i--) {
      if (x == t[i]) {
        int skipLength = endIndexT - i;
        double ret = getPowerOfLambda(skipLength)
          * kernelHelper2LP(n, s, endIndexS, t, i, remainingMatchLength
            - skipLength);
        if (m_useRecursionCache && endIndexS >= 0 && endIndexT >= 0 && n >= 0) {
          cachekh2K[adr % maxCache] = adr + 1;
          cachekh2[adr % maxCache] = ret;
        }
        return ret;
      }
    }
    double ret = getReturnValue(n);
    if (m_useRecursionCache && endIndexS >= 0 && endIndexT >= 0 && n >= 0) {
      cachekh2K[adr % maxCache] = adr + 1;
      cachekh2[adr % maxCache] = ret;
    }
    return ret;
  }

  /**
   * precalculates small powers of lambda to speed up the kernel evaluation
   * 
   * @return the powers
   */
  private double[] calculatePowersOfLambda() {
    double[] powers = new double[MAX_POWER_OF_LAMBDA + 1];
    powers[0] = 1.0;
    double val = 1.0;
    for (int i = 1; i <= MAX_POWER_OF_LAMBDA; i++) {
      val *= m_lambda;
      powers[i] = val;
    }
    return powers;
  }

  /**
   * retrieves a power of lambda from the lambda cache or calculates it directly
   * 
   * @param exponent the exponent to calculate
   * @return the exponent-th power of lambda
   */
  private double getPowerOfLambda(int exponent) {
    if (exponent > MAX_POWER_OF_LAMBDA) {
      return Math.pow(m_lambda, exponent);
    }

    if (exponent < 0) {
      throw new IllegalArgumentException(
        "only positive powers of lambda may be computed");
    }

    return m_powersOflambda[exponent];
  }

  /**
   * initializes variables etc.
   * 
   * @param data the data to use
   */
  @Override
  protected void initVars(Instances data) {
    super.initVars(data);

    m_kernelEvals = 0;
    // take the first string attribute
    m_strAttr = -1;
    for (int i = 0; i < data.numAttributes(); i++) {
      if (i == data.classIndex()) {
        continue;
      }
      if (data.attribute(i).type() == Attribute.STRING) {
        m_strAttr = i;
        break;
      }
    }
    m_numInsts = m_data.numInstances();
    m_storage = new double[m_cacheSize];
    m_keys = new long[m_cacheSize];
    m_powersOflambda = calculatePowersOfLambda();
  }

  /**
   * Returns the Capabilities of this kernel.
   * 
   * @return the capabilities of this object
   * @see Capabilities
   */
  @Override
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();
    result.disableAll();

    result.enable(Capability.STRING_ATTRIBUTES);
    result.enableAllClasses();
    result.enable(Capability.MISSING_CLASS_VALUES);
    result.enable(Capability.NO_CLASS);

    return result;
  }

  /**
   * builds the kernel with the given data.
   * 
   * @param data the data to base the kernel on
   * @throws Exception if something goes wrong, e.g., the data does not consist
   *           of one string attribute and the class
   */
  @Override
  public void buildKernel(Instances data) throws Exception {
    super.buildKernel(data);
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 14512 $");
  }
}