• Home
• nz.ac.waikato.cms.weka
• multiInstanceLearning
• 1.0.9
• source code
• TLDSimple.java

×

Project download

Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $

You can buy this project and download/modify it how often you want.

weka.classifiers.mi.TLDSimple 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 .
 */

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

package weka.classifiers.mi;

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

import weka.classifiers.RandomizableClassifier;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Optimization;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;

/**
 *  A simpler version of TLD, mu random but sigma^2
 * fixed and estimated via data.

 * 

 * For more information see:

 * 

 * Xin Xu (2003). Statistical learning in multiple instance problem. Hamilton,
 * NZ.
 * 

 * 
 * 
 *  BibTeX:
 * 
 * 
 * @mastersthesis{Xu2003,
 *    address = {Hamilton, NZ},
 *    author = {Xin Xu},
 *    note = {0657.594},
 *    school = {University of Waikato},
 *    title = {Statistical learning in multiple instance problem},
 *    year = {2003}
 * }
 * 
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -C
 *  Set whether or not use empirical
 *  log-odds cut-off instead of 0
 * 
 * 
 * 
 * -R <numOfRuns>
 *  Set the number of multiple runs 
 *  needed for searching the MLE.
 * 
 * 
 * 
 * -S <num>
 *  Random number seed.
 *  (default 1)
 * 
 * 
 * 
 * 
 * @author Eibe Frank ([email protected])
 * @author Xin Xu ([email protected])
 * @version $Revision: 10369 $
 */
public class TLDSimple extends RandomizableClassifier implements OptionHandler,
  MultiInstanceCapabilitiesHandler, TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = 9040995947243286591L;

  /** The mean for each attribute of each positive exemplar */
  protected double[][] m_MeanP = null;

  /** The mean for each attribute of each negative exemplar */
  protected double[][] m_MeanN = null;

  /** The effective sum of weights of each positive exemplar in each dimension */
  protected double[][] m_SumP = null;

  /** The effective sum of weights of each negative exemplar in each dimension */
  protected double[][] m_SumN = null;

  /** Estimated sigma^2 in positive bags */
  protected double[] m_SgmSqP;

  /** Estimated sigma^2 in negative bags */
  protected double[] m_SgmSqN;

  /** The parameters to be estimated for each positive exemplar */
  protected double[] m_ParamsP = null;

  /** The parameters to be estimated for each negative exemplar */
  protected double[] m_ParamsN = null;

  /** The dimension of each exemplar, i.e. (numAttributes-2) */
  protected int m_Dimension = 0;

  /** The class label of each exemplar */
  protected double[] m_Class = null;

  /** The number of class labels in the data */
  protected int m_NumClasses = 2;

  /** The very small number representing zero */
  static public double ZERO = 1.0e-12;

  protected int m_Run = 1;

  protected double m_Cutoff;

  protected boolean m_UseEmpiricalCutOff = false;

  private double[] m_LkRatio;

  private Instances m_Attribute = null;

  /**
   * Returns a string describing this filter
   * 
   * @return a description of the filter suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "A simpler version of TLD, mu random but sigma^2 fixed and estimated "
      + "via data.\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;

    result = new TechnicalInformation(Type.MASTERSTHESIS);
    result.setValue(Field.AUTHOR, "Xin Xu");
    result.setValue(Field.YEAR, "2003");
    result.setValue(Field.TITLE,
      "Statistical learning in multiple instance problem");
    result.setValue(Field.SCHOOL, "University of Waikato");
    result.setValue(Field.ADDRESS, "Hamilton, NZ");
    result.setValue(Field.NOTE, "0657.594");

    return result;
  }

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

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.RELATIONAL_ATTRIBUTES);

    // class
    result.enable(Capability.BINARY_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);

    // other
    result.enable(Capability.ONLY_MULTIINSTANCE);

    return result;
  }

  /**
   * Returns the capabilities of this multi-instance classifier for the
   * relational data.
   * 
   * @return the capabilities of this object
   * @see Capabilities
   */
  @Override
  public Capabilities getMultiInstanceCapabilities() {
    Capabilities result = super.getCapabilities();
    result.disableAll();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.disableAllClasses();
    result.enable(Capability.NO_CLASS);

    return result;
  }

  /**
   * 
   * @param exs the training exemplars
   * @throws Exception if the model cannot be built properly
   */
  @Override
  public void buildClassifier(Instances exs) throws Exception {
    // can classifier handle the data?
    getCapabilities().testWithFail(exs);

    // remove instances with missing class
    exs = new Instances(exs);
    exs.deleteWithMissingClass();

    int numegs = exs.numInstances();
    m_Dimension = exs.attribute(1).relation().numAttributes();
    m_Attribute = exs.attribute(1).relation().stringFreeStructure();
    Instances pos = new Instances(exs, 0), neg = new Instances(exs, 0);

    // Divide into two groups
    for (int u = 0; u < numegs; u++) {
      Instance example = exs.instance(u);
      if (example.classValue() == 1) {
        pos.add(example);
      } else {
        neg.add(example);
      }
    }
    int pnum = pos.numInstances(), nnum = neg.numInstances();

    // xBar, n
    m_MeanP = new double[pnum][m_Dimension];
    m_SumP = new double[pnum][m_Dimension];
    m_MeanN = new double[nnum][m_Dimension];
    m_SumN = new double[nnum][m_Dimension];
    // w, m
    m_ParamsP = new double[2 * m_Dimension];
    m_ParamsN = new double[2 * m_Dimension];
    // \sigma^2
    m_SgmSqP = new double[m_Dimension];
    m_SgmSqN = new double[m_Dimension];
    // S^2
    double[][] varP = new double[pnum][m_Dimension], varN = new double[nnum][m_Dimension];
    // numOfEx 'e' without all missing
    double[] effNumExP = new double[m_Dimension], effNumExN = new double[m_Dimension];
    // For the starting values
    double[] pMM = new double[m_Dimension], nMM = new double[m_Dimension], pVM = new double[m_Dimension], nVM = new double[m_Dimension];
    // # of exemplars with only one instance
    double[] numOneInsExsP = new double[m_Dimension], numOneInsExsN = new double[m_Dimension];
    // sum_i(1/n_i)
    double[] pInvN = new double[m_Dimension], nInvN = new double[m_Dimension];

    // Extract metadata from both positive and negative bags
    for (int v = 0; v < pnum; v++) {
      // Instance px = pos.instance(v);
      Instances pxi = pos.instance(v).relationalValue(1);
      for (int k = 0; k < pxi.numAttributes(); k++) {
        m_MeanP[v][k] = pxi.meanOrMode(k);
        varP[v][k] = pxi.variance(k);
      }

      for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
        // if((t==m_ClassIndex) || (t==m_IdIndex))
        // t++;
        if (varP[v][w] <= 0.0) {
          varP[v][w] = 0.0;
        }
        if (!Double.isNaN(m_MeanP[v][w])) {

          for (int u = 0; u < pxi.numInstances(); u++) {
            if (!pxi.instance(u).isMissing(t)) {
              m_SumP[v][w] += pxi.instance(u).weight();
            }
          }

          pMM[w] += m_MeanP[v][w];
          pVM[w] += m_MeanP[v][w] * m_MeanP[v][w];
          if ((m_SumP[v][w] > 1) && (varP[v][w] > ZERO)) {

            m_SgmSqP[w] += varP[v][w] * (m_SumP[v][w] - 1.0) / m_SumP[v][w];

            // m_SgmSqP[w] += varP[v][w]*(m_SumP[v][w]-1.0);
            effNumExP[w]++; // Not count exemplars with 1 instance
            pInvN[w] += 1.0 / m_SumP[v][w];
            // pInvN[w] += m_SumP[v][w];
          } else {
            numOneInsExsP[w]++;
          }
        }

      }
    }

    for (int v = 0; v < nnum; v++) {
      // Instance nx = neg.instance(v);
      Instances nxi = neg.instance(v).relationalValue(1);
      for (int k = 0; k < nxi.numAttributes(); k++) {
        m_MeanN[v][k] = nxi.meanOrMode(k);
        varN[v][k] = nxi.variance(k);
      }
      // Instances nxi = nx.getInstances();

      for (int w = 0, t = 0; w < m_Dimension; w++, t++) {

        // if((t==m_ClassIndex) || (t==m_IdIndex))
        // t++;
        if (varN[v][w] <= 0.0) {
          varN[v][w] = 0.0;
        }
        if (!Double.isNaN(m_MeanN[v][w])) {
          for (int u = 0; u < nxi.numInstances(); u++) {
            if (!nxi.instance(u).isMissing(t)) {
              m_SumN[v][w] += nxi.instance(u).weight();
            }
          }

          nMM[w] += m_MeanN[v][w];
          nVM[w] += m_MeanN[v][w] * m_MeanN[v][w];
          if ((m_SumN[v][w] > 1) && (varN[v][w] > ZERO)) {
            m_SgmSqN[w] += varN[v][w] * (m_SumN[v][w] - 1.0) / m_SumN[v][w];
            // m_SgmSqN[w] += varN[v][w]*(m_SumN[v][w]-1.0);
            effNumExN[w]++; // Not count exemplars with 1 instance
            nInvN[w] += 1.0 / m_SumN[v][w];
            // nInvN[w] += m_SumN[v][w];
          } else {
            numOneInsExsN[w]++;
          }
        }
      }
    }

    // Expected \sigma^2
    /*
     * if m_SgmSqP[u] or m_SgmSqN[u] is 0, assign 0 to sigma^2. Otherwise, may
     * cause k m_SgmSqP / m_SgmSqN to be NaN. Modified by Lin Dong (Sep. 2005)
     */
    for (int u = 0; u < m_Dimension; u++) {
      // For exemplars with only one instance, use avg(\sigma^2) of other
      // exemplars
      if (m_SgmSqP[u] != 0) {
        m_SgmSqP[u] /= (effNumExP[u] - pInvN[u]);
      } else {
        m_SgmSqP[u] = 0;
      }
      if (m_SgmSqN[u] != 0) {
        m_SgmSqN[u] /= (effNumExN[u] - nInvN[u]);
      } else {
        m_SgmSqN[u] = 0;
      }

      // m_SgmSqP[u] /= (pInvN[u]-effNumExP[u]);
      // m_SgmSqN[u] /= (nInvN[u]-effNumExN[u]);
      effNumExP[u] += numOneInsExsP[u];
      effNumExN[u] += numOneInsExsN[u];
      pMM[u] /= effNumExP[u];
      nMM[u] /= effNumExN[u];
      pVM[u] = pVM[u] / (effNumExP[u] - 1.0) - pMM[u] * pMM[u] * effNumExP[u]
        / (effNumExP[u] - 1.0);
      nVM[u] = nVM[u] / (effNumExN[u] - 1.0) - nMM[u] * nMM[u] * effNumExN[u]
        / (effNumExN[u] - 1.0);
    }

    // Bounds and parameter values for each run
    double[][] bounds = new double[2][2];
    double[] pThisParam = new double[2], nThisParam = new double[2];

    // Initial values for parameters
    double w, m;
    Random whichEx = new Random(m_Seed);

    // Optimize for one dimension
    for (int x = 0; x < m_Dimension; x++) {
      // System.out.println("\n\n!!!!!!!!!!!!!!!!!!!!!!???Dimension #"+x);

      // Positive examplars: first run
      pThisParam[0] = pVM[x]; // w
      if (pThisParam[0] <= ZERO) {
        pThisParam[0] = 1.0;
      }
      pThisParam[1] = pMM[x]; // m

      // Negative examplars: first run
      nThisParam[0] = nVM[x]; // w
      if (nThisParam[0] <= ZERO) {
        nThisParam[0] = 1.0;
      }
      nThisParam[1] = nMM[x]; // m

      // Bound constraints
      bounds[0][0] = ZERO; // w > 0
      bounds[0][1] = Double.NaN;
      bounds[1][0] = Double.NaN;
      bounds[1][1] = Double.NaN;

      double pminVal = Double.MAX_VALUE, nminVal = Double.MAX_VALUE;
      TLDSimple_Optm pOp = null, nOp = null;
      boolean isRunValid = true;
      double[] sumP = new double[pnum], meanP = new double[pnum];
      double[] sumN = new double[nnum], meanN = new double[nnum];

      // One dimension
      for (int p = 0; p < pnum; p++) {
        sumP[p] = m_SumP[p][x];
        meanP[p] = m_MeanP[p][x];
      }
      for (int q = 0; q < nnum; q++) {
        sumN[q] = m_SumN[q][x];
        meanN[q] = m_MeanN[q][x];
      }

      for (int y = 0; y < m_Run; y++) {
        // System.out.println("\n\n!!!!!!!!!Positive exemplars: Run #"+y);
        double thisMin;
        pOp = new TLDSimple_Optm();
        pOp.setNum(sumP);
        pOp.setSgmSq(m_SgmSqP[x]);
        if (getDebug()) {
          System.out.println("m_SgmSqP[" + x + "]= " + m_SgmSqP[x]);
        }
        pOp.setXBar(meanP);
        // pOp.setDebug(true);
        pThisParam = pOp.findArgmin(pThisParam, bounds);
        while (pThisParam == null) {
          pThisParam = pOp.getVarbValues();
          if (getDebug()) {
            System.out.println("!!! 200 iterations finished, not enough!");
          }
          pThisParam = pOp.findArgmin(pThisParam, bounds);
        }

        thisMin = pOp.getMinFunction();
        if (!Double.isNaN(thisMin) && (thisMin < pminVal)) {
          pminVal = thisMin;
          for (int z = 0; z < 2; z++) {
            m_ParamsP[2 * x + z] = pThisParam[z];
          }
        }

        if (Double.isNaN(thisMin)) {
          pThisParam = new double[2];
          isRunValid = false;
        }
        if (!isRunValid) {
          y--;
          isRunValid = true;
        }

        // Change the initial parameters and restart
        int pone = whichEx.nextInt(pnum);

        // Positive exemplars: next run
        while (Double.isNaN(m_MeanP[pone][x])) {
          pone = whichEx.nextInt(pnum);
        }

        m = m_MeanP[pone][x];
        w = (m - pThisParam[1]) * (m - pThisParam[1]);
        pThisParam[0] = w; // w
        pThisParam[1] = m; // m
      }

      for (int y = 0; y < m_Run; y++) {
        // System.out.println("\n\n!!!!!!!!!Negative exemplars: Run #"+y);
        double thisMin;
        nOp = new TLDSimple_Optm();
        nOp.setNum(sumN);
        nOp.setSgmSq(m_SgmSqN[x]);
        if (getDebug()) {
          System.out.println(m_SgmSqN[x]);
        }
        nOp.setXBar(meanN);
        // nOp.setDebug(true);
        nThisParam = nOp.findArgmin(nThisParam, bounds);

        while (nThisParam == null) {
          nThisParam = nOp.getVarbValues();
          if (getDebug()) {
            System.out.println("!!! 200 iterations finished, not enough!");
          }
          nThisParam = nOp.findArgmin(nThisParam, bounds);
        }

        thisMin = nOp.getMinFunction();
        if (!Double.isNaN(thisMin) && (thisMin < nminVal)) {
          nminVal = thisMin;
          for (int z = 0; z < 2; z++) {
            m_ParamsN[2 * x + z] = nThisParam[z];
          }
        }

        if (Double.isNaN(thisMin)) {
          nThisParam = new double[2];
          isRunValid = false;
        }

        if (!isRunValid) {
          y--;
          isRunValid = true;
        }

        // Change the initial parameters and restart
        int none = whichEx.nextInt(nnum);// Randomly pick one pos. exmpl.

        // Negative exemplars: next run
        while (Double.isNaN(m_MeanN[none][x])) {
          none = whichEx.nextInt(nnum);
        }

        m = m_MeanN[none][x];
        w = (m - nThisParam[1]) * (m - nThisParam[1]);
        nThisParam[0] = w; // w
        nThisParam[1] = m; // m
      }
    }

    m_LkRatio = new double[m_Dimension];

    if (m_UseEmpiricalCutOff) {
      // Find the empirical cut-off
      double[] pLogOdds = new double[pnum], nLogOdds = new double[nnum];
      for (int p = 0; p < pnum; p++) {
        pLogOdds[p] = likelihoodRatio(m_SumP[p], m_MeanP[p]);
      }

      for (int q = 0; q < nnum; q++) {
        nLogOdds[q] = likelihoodRatio(m_SumN[q], m_MeanN[q]);
      }

      // Update m_Cutoff
      findCutOff(pLogOdds, nLogOdds);
    } else {
      m_Cutoff = -Math.log((double) pnum / (double) nnum);
    }

    /*
     * for(int x=0, y=0; x= neg[nOrder[n]]); n++, fstAccu++) {
      ;
    }

    if (n >= nNum) { // totally seperate
      m_Cutoff = (neg[nOrder[nNum - 1]] + pos[pOrder[0]]) / 2.0;
      // m_Cutoff = neg[nOrder[nNum-1]];
      return;
    }

    // count=n; NOT USED
    while ((p < pNum) && (n < nNum)) {
      // Compare the next in the two lists
      if (pos[pOrder[p]] >= neg[nOrder[n]]) { // Neg has less log-odds
        fstAccu += 1.0;
        split = neg[nOrder[n]];
        n++;
      } else {
        sndAccu -= 1.0;
        split = pos[pOrder[p]];
        p++;
      }
      // count++; NOT USED
      /*
       * double entropy=0.0, cover=(double)count; if(fstAccu>0.0) entropy -=
       * fstAccu*Math.log(fstAccu/cover); if(sndAccu>0.0) entropy -=
       * sndAccu*Math.log(sndAccu/(total-cover));
       * 
       * if(entropy < minEntropy){ minEntropy = entropy; //find the next
       * smallest //double next = neg[nOrder[n]];
       * //if(pos[pOrder[p]] maxAccu)
        || ((fstAccu + sndAccu == maxAccu) && (Math.abs(split) < minDistTo0))) {
        maxAccu = fstAccu + sndAccu;
        m_Cutoff = split;
        minDistTo0 = Math.abs(split);
      }
    }
  }

  /**
   * Returns an enumeration describing the available options
   * 
   * @return an enumeration of all the available options
   */
  @Override
  public Enumeration listOptions() {

    Vector result = new Vector();

    result.addElement(new Option("\tSet whether or not use empirical\n"
      + "\tlog-odds cut-off instead of 0", "C", 0, "-C"));

    result.addElement(new Option("\tSet the number of multiple runs \n"
      + "\tneeded for searching the MLE.", "R", 1, "-R "));

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

    return result.elements();
  }

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

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -C
   *  Set whether or not use empirical
   *  log-odds cut-off instead of 0
   * 
   * 
   * 
   * -R <numOfRuns>
   *  Set the number of multiple runs 
   *  needed for searching the MLE.
   * 
   * 
   * 
   * -S <num>
   *  Random number seed.
   *  (default 1)
   * 
   * 
   * 
   * 
   * @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 {

    setUsingCutOff(Utils.getFlag('C', options));

    String runString = Utils.getOption('R', options);
    if (runString.length() != 0) {
      setNumRuns(Integer.parseInt(runString));
    } else {
      setNumRuns(1);
    }

    super.setOptions(options);

    Utils.checkForRemainingOptions(options);
  }

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

    Vector result = new Vector();

    if (getUsingCutOff()) {
      result.add("-C");
    }

    result.add("-R");
    result.add("" + getNumRuns());

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

    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 numRunsTipText() {
    return "The number of runs to perform.";
  }

  /**
   * Sets the number of runs to perform.
   * 
   * @param numRuns the number of runs to perform
   */
  public void setNumRuns(int numRuns) {
    m_Run = numRuns;
  }

  /**
   * Returns the number of runs to perform.
   * 
   * @return the number of runs to perform
   */
  public int getNumRuns() {
    return m_Run;
  }

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

  /**
   * Sets whether to use an empirical cutoff.
   * 
   * @param cutOff whether to use an empirical cutoff
   */
  public void setUsingCutOff(boolean cutOff) {
    m_UseEmpiricalCutOff = cutOff;
  }

  /**
   * Returns whether an empirical cutoff is used
   * 
   * @return true if an empirical cutoff is used
   */
  public boolean getUsingCutOff() {
    return m_UseEmpiricalCutOff;
  }

  /**
   * Gets a string describing the classifier.
   * 
   * @return a string describing the classifer built.
   */
  @Override
  public String toString() {
    StringBuffer text = new StringBuffer("\n\nTLDSimple:\n");
    double sgm, w, m;
    for (int x = 0, y = 0; x < m_Dimension; x++, y++) {
      // if((x==m_ClassIndex) || (x==m_IdIndex))
      // y++;
      sgm = m_SgmSqP[x];
      w = m_ParamsP[2 * x];
      m = m_ParamsP[2 * x + 1];
      text.append("\n" + m_Attribute.attribute(y).name() + "\nPositive: "
        + "sigma^2=" + sgm + ", w=" + w + ", m=" + m + "\n");
      sgm = m_SgmSqN[x];
      w = m_ParamsN[2 * x];
      m = m_ParamsN[2 * x + 1];
      text.append("Negative: " + "sigma^2=" + sgm + ", w=" + w + ", m=" + m
        + "\n");
    }

    return text.toString();
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10369 $");
  }

  /**
   * Main method for testing.
   * 
   * @param args the options for the classifier
   */
  public static void main(String[] args) {
    runClassifier(new TLDSimple(), args);
  }
}

class TLDSimple_Optm extends Optimization {

  private double[] num;
  private double sSq;
  private double[] xBar;

  public void setNum(double[] n) {
    num = n;
  }

  public void setSgmSq(double s) {

    sSq = s;
  }

  public void setXBar(double[] x) {
    xBar = x;
  }

  /**
   * Implement this procedure to evaluate objective function to be minimized
   */
  @Override
  protected double objectiveFunction(double[] x) {
    int numExs = num.length;
    double NLL = 0; // Negative Log-Likelihood

    double w = x[0], m = x[1];
    for (int j = 0; j < numExs; j++) {

      if (Double.isNaN(xBar[j])) {
        continue; // All missing values
      }
      double bag = 0;

      bag += Math.log(w * num[j] + sSq);

      if (Double.isNaN(bag) && m_Debug) {
        System.out.println("???????????1: " + w + " " + m + "|x-: " + xBar[j]
          + "|n: " + num[j] + "|S^2: " + sSq);
        // System.exit(1);
      }

      bag += num[j] * (m - xBar[j]) * (m - xBar[j]) / (w * num[j] + sSq);
      if (Double.isNaN(bag) && m_Debug) {
        System.out.println("???????????2: " + w + " " + m + "|x-: " + xBar[j]
          + "|n: " + num[j] + "|S^2: " + sSq);
        // System.exit(1);
      }

      // if(bag<0) bag=0;
      NLL += bag;
    }

    // System.out.println("???????????NLL:"+NLL);
    return NLL;
  }

  /**
   * Subclass should implement this procedure to evaluate gradient of the
   * objective function
   */
  @Override
  protected double[] evaluateGradient(double[] x) {
    double[] g = new double[x.length];
    int numExs = num.length;

    double w = x[0], m = x[1];
    double dw = 0.0, dm = 0.0;

    for (int j = 0; j < numExs; j++) {

      if (Double.isNaN(xBar[j])) {
        continue; // All missing values
      }
      dw += num[j] / (w * num[j] + sSq) - num[j] * num[j] * (m - xBar[j])
        * (m - xBar[j]) / ((w * num[j] + sSq) * (w * num[j] + sSq));

      dm += 2.0 * num[j] * (m - xBar[j]) / (w * num[j] + sSq);
    }

    g[0] = dw;
    g[1] = dm;
    return g;
  }

  /**
   * Subclass should implement this procedure to evaluate second-order gradient
   * of the objective function
   */
  @Override
  protected double[] evaluateHessian(double[] x, int index) {
    double[] h = new double[x.length];

    // # of exemplars, # of dimensions
    // which dimension and which variable for 'index'
    int numExs = num.length;
    double w, m;
    // Take the 2nd-order derivative
    switch (index) {
    case 0: // w
      w = x[0];
      m = x[1];

      for (int j = 0; j < numExs; j++) {
        if (Double.isNaN(xBar[j])) {
          continue; // All missing values
        }

        h[0] += 2.0 * Math.pow(num[j], 3) * (m - xBar[j]) * (m - xBar[j])
          / Math.pow(w * num[j] + sSq, 3) - num[j] * num[j]
          / ((w * num[j] + sSq) * (w * num[j] + sSq));

        h[1] -= 2.0 * (m - xBar[j]) * num[j] * num[j]
          / ((num[j] * w + sSq) * (num[j] * w + sSq));
      }
      break;

    case 1: // m
      w = x[0];
      m = x[1];

      for (int j = 0; j < numExs; j++) {
        if (Double.isNaN(xBar[j])) {
          continue; // All missing values
        }

        h[0] -= 2.0 * (m - xBar[j]) * num[j] * num[j]
          / ((num[j] * w + sSq) * (num[j] * w + sSq));

        h[1] += 2.0 * num[j] / (w * num[j] + sSq);
      }
    }

    return h;
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10369 $");
  }
}