jboost-cvs — Sends email for any commits to CVS

[Jboost-cvs] jboost/src/jboost/booster AbstractBooster.java, 1.7, 1.8 AbstractBoosterTest.java, 1.2, 1.3 AdaBoost.java, 1.5, 1.6 AdaBoostTest.java, 1.1.1.1, 1.2 Bag.java, 1.2, 1.3 BinaryPrediction.java, 1.1.1.1, 1.2 Booster.java, 1.3, 1.4 BoosterTestSuite.java, 1.3, 1.4 BrownBoost.java, 1.10, 1.11 BrownBoostTest.java, 1.3, 1.4 DebugWrap.java, 1.3, 1.4 LogLossBoost.java, 1.2, 1.3 LogLossBoostTest.java, 1.1.1.1, 1.2 MixedBinaryPrediction.java, 1.5, 1.6 MixedBinaryPredictionTest.java, 1.1, 1.2 MulticlassWrapMH.java, 1.5, 1.6 MulticlassWrapOC.java, 1.1.1.1, 1.2 NormalizedPrediction.java, 1.2, 1.3 NotNormalizedPredException.java, 1.1.1.1, 1.2 Prediction.java, 1.2, 1.3 YabaBoost.java, 1.7, 1.8 YabaBoostTest.java, 1.1.1.1, 1.2

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/booster
In directory sc8-pr-cvs17.sourceforge.net:/tmp/cvs-serv8493

Modified Files:
	AbstractBooster.java AbstractBoosterTest.java AdaBoost.java 
	AdaBoostTest.java Bag.java BinaryPrediction.java Booster.java 
	BoosterTestSuite.java BrownBoost.java BrownBoostTest.java 
	DebugWrap.java LogLossBoost.java LogLossBoostTest.java 
	MixedBinaryPrediction.java MixedBinaryPredictionTest.java 
	MulticlassWrapMH.java MulticlassWrapOC.java 
	NormalizedPrediction.java NotNormalizedPredException.java 
	Prediction.java YabaBoost.java YabaBoostTest.java 
Log Message:
Small comments and sed commands to reformat

Index: Bag.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/Bag.java,v
retrieving revision 1.2
retrieving revision 1.3
diff -C2 -d -r1.2 -r1.3
*** Bag.java	24 Jan 2008 22:30:16 -0000	1.2
--- Bag.java	25 Mar 2008 01:00:27 -0000	1.3
***************
*** 21,26 ****
     */
    public abstract void reset();
!     
!     /** 
       * Get the weights of the bag.
       */
--- 21,26 ----
     */
    public abstract void reset();
! 
!     /**
       * Get the weights of the bag.
       */
***************
*** 28,32 ****
  	return m_w;
      }
!     
    /**
     * If the bag has no weight, then it is considered to be weightless.
--- 28,32 ----
  	return m_w;
      }
! 
    /**
     * If the bag has no weight, then it is considered to be weightless.
***************
*** 106,110 ****
       * When data splitting is used, this is the same as getLoss() but
       * computed using only examples in part s of the split.  s<0
!      * indicates that the entire dataset should be used.  
       *
       * The default implementation of this method simply throws an
--- 106,110 ----
       * When data splitting is used, this is the same as getLoss() but
       * computed using only examples in part s of the split.  s<0
!      * indicates that the entire dataset should be used.
       *
       * The default implementation of this method simply throws an
***************
*** 114,118 ****
  	throw new NotSupportedException("getLoss",this.getClass().toString());
      }
!     
      /*
       * Computes a non-default loss for this bag.  The loss types are
--- 114,118 ----
  	throw new NotSupportedException("getLoss",this.getClass().toString());
      }
! 
      /*
       * Computes a non-default loss for this bag.  The loss types are
***************
*** 122,126 ****
       */
      /*  public abstract double getSpecialLoss(int lossType); */
!     
      /*
       * When data splitting is used, this is the same as
--- 122,126 ----
       */
      /*  public abstract double getSpecialLoss(int lossType); */
! 
      /*
       * When data splitting is used, this is the same as
***************
*** 135,139 ****
       }
      */
!     
      /**
       * Checks if a given loss type is allowed for this bag.
--- 135,139 ----
       }
      */
! 
      /**
       * Checks if a given loss type is allowed for this bag.

Index: MulticlassWrapOC.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/MulticlassWrapOC.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** MulticlassWrapOC.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- MulticlassWrapOC.java	25 Mar 2008 01:00:28 -0000	1.2
***************
*** 14,16 ****
     MulticlassWrapOC(AbstractBooster booster) {}
  
! } 
--- 14,16 ----
     MulticlassWrapOC(AbstractBooster booster) {}
  
! }

Index: BoosterTestSuite.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/BoosterTestSuite.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -C2 -d -r1.3 -r1.4
*** BoosterTestSuite.java	2 Oct 2007 02:32:07 -0000	1.3
--- BoosterTestSuite.java	25 Mar 2008 01:00:27 -0000	1.4
***************
*** 30,33 ****
          return suite;
      }
!     
  }
--- 30,33 ----
          return suite;
      }
! 
  }

Index: AdaBoostTest.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/AdaBoostTest.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** AdaBoostTest.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- AdaBoostTest.java	25 Mar 2008 01:00:27 -0000	1.2
***************
*** 74,78 ****
          //TODO Implement newBag().
      }
!     
      public final void testUpdate() {
        //TODO Implement update().
--- 74,78 ----
          //TODO Implement newBag().
      }
! 
      public final void testUpdate() {
        //TODO Implement update().
***************
*** 103,107 ****
          }
        }
!       
        for (int i=0; i < ones.length; i++) {
          ones[i]= indices[COUNT/2 + i];
--- 103,107 ----
          }
        }
! 
        for (int i=0; i < ones.length; i++) {
          ones[i]= indices[COUNT/2 + i];
***************
*** 116,120 ****
        }
      }
!     
      final public void testGetPredictions() {
          //TODO Implement getPredictions().
--- 116,120 ----
        }
      }
! 
      final public void testGetPredictions() {
          //TODO Implement getPredictions().

Index: NormalizedPrediction.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/NormalizedPrediction.java,v
retrieving revision 1.2
retrieving revision 1.3
diff -C2 -d -r1.2 -r1.3
*** NormalizedPrediction.java	24 Jan 2008 22:30:16 -0000	1.2
--- NormalizedPrediction.java	25 Mar 2008 01:00:28 -0000	1.3
***************
*** 2,6 ****
  
  /**
!  * A normalized predictor.  When a prediction is added, we 
   * ensure that the margin is normalized.  This normalization is done
   * by "mixing" in the new prediction.
--- 2,6 ----
  
  /**
!  * A normalized predictor.  When a prediction is added, we
   * ensure that the margin is normalized.  This normalization is done
   * by "mixing" in the new prediction.
***************
*** 10,14 ****
  public interface NormalizedPrediction {
  
!     /** 
       * Must be used very carefully.  An example:
       *
--- 10,14 ----
  public interface NormalizedPrediction {
  
!     /**
       * Must be used very carefully.  An example:
       *
***************
*** 22,28 ****
       */
      public Prediction add(Prediction p);
!     
      /*    public static boolean isNormPred(Prediction p); */
!     public double getDeltaT();    
  
  }
--- 22,28 ----
       */
      public Prediction add(Prediction p);
! 
      /*    public static boolean isNormPred(Prediction p); */
!     public double getDeltaT();
  
  }

Index: LogLossBoost.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/LogLossBoost.java,v
retrieving revision 1.2
retrieving revision 1.3
diff -C2 -d -r1.2 -r1.3
*** LogLossBoost.java	2 Oct 2007 02:32:07 -0000	1.2
--- LogLossBoost.java	25 Mar 2008 01:00:27 -0000	1.3
***************
*** 6,10 ****
   * AdaBoost-like implementation for the Log-Loss. The only differnece is weight
   * update.
!  * 
   * @author Yoram Singer
   * @version $Header:
--- 6,10 ----
   * AdaBoost-like implementation for the Log-Loss. The only differnece is weight
   * update.
!  *
   * @author Yoram Singer
   * @version $Header:
***************
*** 17,21 ****
    /**
     * Default constructors call AdaBoost constructors
!    * Uses a value of 0.0 for the smoothing term 
     */
    public LogLossBoost() {
--- 17,21 ----
    /**
     * Default constructors call AdaBoost constructors
!    * Uses a value of 0.0 for the smoothing term
     */
    public LogLossBoost() {
***************
*** 59,63 ****
      }
  
!   
    /**
     * The LogLoss example weight is set to 1/(1 + e^(margin))
--- 59,63 ----
      }
  
! 
    /**
     * The LogLoss example weight is set to 1/(1 + e^(margin))
***************
*** 67,71 ****
    }
  
!   /** 
     * Update the examples m_margins and m_weights using the
     * logistic loss computation
--- 67,71 ----
    }
  
!   /**
     * Update the examples m_margins and m_weights using the
     * logistic loss computation
***************
*** 78,82 ****
        m_oldWeights[i]= m_weights[i];
      }
!    
      // update m_weights and m_margins
      for (int i= 0; i < exampleIndex.length; i++) {
--- 78,82 ----
        m_oldWeights[i]= m_weights[i];
      }
! 
      // update m_weights and m_margins
      for (int i= 0; i < exampleIndex.length; i++) {

Index: BinaryPrediction.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/BinaryPrediction.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** BinaryPrediction.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- BinaryPrediction.java	25 Mar 2008 01:00:27 -0000	1.2
***************
*** 13,17 ****
      public BinaryPrediction(double p) {prediction=p;}
      public BinaryPrediction() {prediction=0.0;}
!     
      public Object clone(){
  	Object a = new BinaryPrediction(prediction);
--- 13,17 ----
      public BinaryPrediction(double p) {prediction=p;}
      public BinaryPrediction() {prediction=0.0;}
! 
      public Object clone(){
  	Object a = new BinaryPrediction(prediction);
***************
*** 28,37 ****
  	return this;
      }
!     
      public Prediction add(double w, Prediction p) {
  	prediction += w * ((BinaryPrediction) p).prediction;
  	return this;
      }
!     
      public double[] getClassScores() {
  	double[] a = {-prediction,prediction};
--- 28,37 ----
  	return this;
      }
! 
      public Prediction add(double w, Prediction p) {
  	prediction += w * ((BinaryPrediction) p).prediction;
  	return this;
      }
! 
      public double[] getClassScores() {
  	double[] a = {-prediction,prediction};
***************
*** 55,61 ****
      public boolean equals(Prediction other) {
        BinaryPrediction bp= (BinaryPrediction) other;
!       return (prediction == bp.prediction); 
      }
!     
      /**
       * computes margin as absolute value of prediction.
--- 55,61 ----
      public boolean equals(Prediction other) {
        BinaryPrediction bp= (BinaryPrediction) other;
!       return (prediction == bp.prediction);
      }
! 
      /**
       * computes margin as absolute value of prediction.

Index: Booster.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/Booster.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -C2 -d -r1.3 -r1.4
*** Booster.java	13 Oct 2007 04:32:28 -0000	1.3
--- Booster.java	25 Mar 2008 01:00:27 -0000	1.4
***************
*** 1,5 ****
  /*
   * Created on Jan 12, 2004
!  *  
   */
  package jboost.booster;
--- 1,5 ----
  /*
   * Created on Jan 12, 2004
!  *
   */
  package jboost.booster;
***************
*** 9,13 ****
  
  /**
!  * This is the definition of the required interface for a booster. 
   * A skeletal implementation is provided by AbstractBooster.
   *
--- 9,13 ----
  
  /**
!  * This is the definition of the required interface for a booster.
   * A skeletal implementation is provided by AbstractBooster.
   *
***************
*** 15,44 ****
   */
  public interface Booster {
!   
    /**
     * Factory method to create an instance of a Booster
     * @param configuration
!    */ 
    public abstract void init(Configuration config);
!   
    /**
     * Create and return a new Bag which initially contains the elements in the
     * list 'defaults'.
!    * 
     * @param defaults the initial items to add to the Bag
     */
    public abstract Bag newBag(int[] defaults);
!   
    /**
     * Create and return a new Bag which is a clone of 'original'
!    * 
     * @param original the bag to copy into a new Bag
     */
    public abstract Bag newBag(Bag original);
!  
    /**
     * Find the best binary split for a sorted list of example indices with given
     * split points.
!    * 
     * @param l an array of example indices, sorted.
     * @param sp an array with true in position i when a split between positions
--- 15,44 ----
   */
  public interface Booster {
! 
    /**
     * Factory method to create an instance of a Booster
     * @param configuration
!    */
    public abstract void init(Configuration config);
! 
    /**
     * Create and return a new Bag which initially contains the elements in the
     * list 'defaults'.
!    *
     * @param defaults the initial items to add to the Bag
     */
    public abstract Bag newBag(int[] defaults);
! 
    /**
     * Create and return a new Bag which is a clone of 'original'
!    *
     * @param original the bag to copy into a new Bag
     */
    public abstract Bag newBag(Bag original);
! 
    /**
     * Find the best binary split for a sorted list of example indices with given
     * split points.
!    *
     * @param l an array of example indices, sorted.
     * @param sp an array with true in position i when a split between positions
***************
*** 50,64 ****
     */
    public abstract int findBestSplit(Bag b0, Bag b1, int[] l, boolean[] sp);
!   
    /**
     * Compute the loss associated with an array of bags where small loss is
     * considered "better". The default implementation of this procedure assumes
     * that the loss is additive across bags.
!    * 
     * @param bags array of bags whose losses will be added up and returned
     * @return loss the sum of the losses for all the bags
     */
    public abstract double getLoss(Bag[] bags);
!   
    /**
     * Returns a list of margin values for the training data. The actual number
--- 50,64 ----
     */
    public abstract int findBestSplit(Bag b0, Bag b1, int[] l, boolean[] sp);
! 
    /**
     * Compute the loss associated with an array of bags where small loss is
     * considered "better". The default implementation of this procedure assumes
     * that the loss is additive across bags.
!    *
     * @param bags array of bags whose losses will be added up and returned
     * @return loss the sum of the losses for all the bags
     */
    public abstract double getLoss(Bag[] bags);
! 
    /**
     * Returns a list of margin values for the training data. The actual number
***************
*** 77,86 ****
      public abstract String getParamString();
  
!   
    /**
     * Returns the current theoretical bound on the training error.
     */
    public abstract double getTheoryBound();
!   
    /**
     * Returns the predictions associated with a list of bags representing a
--- 77,86 ----
      public abstract String getParamString();
  
! 
    /**
     * Returns the current theoretical bound on the training error.
     */
    public abstract double getTheoryBound();
! 
    /**
     * Returns the predictions associated with a list of bags representing a
***************
*** 94,102 ****
     */
    public abstract Prediction[] getPredictions(Bag[] b, int[][] partition);
!   
    /**
     * Updates training set data structures to reflect the addition of a new base
     * classifier.
!    * 
     * @param predictions an array of predictions
     * @param examples an array of arrays of example indexes for which the
--- 94,102 ----
     */
    public abstract Prediction[] getPredictions(Bag[] b, int[][] partition);
! 
    /**
     * Updates training set data structures to reflect the addition of a new base
     * classifier.
!    *
     * @param predictions an array of predictions
     * @param examples an array of arrays of example indexes for which the
***************
*** 105,111 ****
     *            predictions</i>. the second index runs over the examples
     *            indices that correspond to that part.
!    */ 
    public abstract void update(Prediction[] predictions, int[][] elements);
!   
    /**
     * Use the margin value to calculate the weight for the example
--- 105,111 ----
     *            predictions</i>. the second index runs over the examples
     *            indices that correspond to that part.
!    */
    public abstract void update(Prediction[] predictions, int[][] elements);
! 
    /**
     * Use the margin value to calculate the weight for the example
***************
*** 114,128 ****
     */
    public double calculateWeight(double margin);
!   
    /**
     * Create and return a new Bag which contains no elements
     */
    public abstract Bag newBag();
!   
    /**
     * Clear the examples list
     */
    public abstract void clear();
!   
    /**
     * Finalize the data structures of the booster. Execute after the constructor
--- 114,128 ----
     */
    public double calculateWeight(double margin);
! 
    /**
     * Create and return a new Bag which contains no elements
     */
    public abstract Bag newBag();
! 
    /**
     * Clear the examples list
     */
    public abstract void clear();
! 
    /**
     * Finalize the data structures of the booster. Execute after the constructor
***************
*** 130,138 ****
     */
    public abstract void finalizeData();
!   
    /**
     * Add an example to location index in the list of examples handled by the
     * booster.
!    * 
     * @param index the position where the example should be added
     * @param label the label to add
--- 130,138 ----
     */
    public abstract void finalizeData();
! 
    /**
     * Add an example to location index in the list of examples handled by the
     * booster.
!    *
     * @param index the position where the example should be added
     * @param label the label to add
***************
*** 140,144 ****
    public abstract void addExample(int index, Label label);
    public abstract void addExample(int index, Label label, double weight);
!   
  }
  
--- 140,144 ----
    public abstract void addExample(int index, Label label);
    public abstract void addExample(int index, Label label, double weight);
! 
  }
  

Index: AdaBoost.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/AdaBoost.java,v
retrieving revision 1.5
retrieving revision 1.6
diff -C2 -d -r1.5 -r1.6
*** AdaBoost.java	24 Jan 2008 22:30:16 -0000	1.5
--- AdaBoost.java	25 Mar 2008 01:00:27 -0000	1.6
***************
*** 9,12 ****
--- 9,14 ----
  import jboost.controller.Configuration;
  import jboost.examples.Label;
+ import jboost.booster.BrownBoost;
+ import jboost.booster.BrownBoost.BrownBag;
  
  
***************
*** 14,18 ****
   * The simplest possible implementation of a booster. confidence-rated adaboost
   * based on equality/inequality of m_labels
!  * 
   * @author Yoav Freund
   * @version $Header:
--- 16,20 ----
   * The simplest possible implementation of a booster. confidence-rated adaboost
   * based on equality/inequality of m_labels
!  *
   * @author Yoav Freund
   * @version $Header:
***************
*** 34,37 ****
--- 36,46 ----
    protected double[] m_potentials;
  
+   /**
+    * The predictions from a hypothesis for an iteration.
+    * HACK: This should eventually be removed for efficiency concerns.
+    */
+   protected double[] m_hypPredictions;
+ 
+ 
    /** */
    protected int[] m_posExamples;
***************
*** 55,59 ****
    /** if false, then assume all sample weights are 1 */
    //protected boolean m_useSampleWeights;
!   
    protected double m_totalWeight; // total weight of all examples
    protected int m_numExamples= 0; // number of examples in training set
--- 64,68 ----
    /** if false, then assume all sample weights are 1 */
    //protected boolean m_useSampleWeights;
! 
    protected double m_totalWeight; // total weight of all examples
    protected int m_numExamples= 0; // number of examples in training set
***************
*** 67,72 ****
  
  
!   /** 
!    * default constructor 
     *
     */
--- 76,81 ----
  
  
!   /**
!    * default constructor
     *
     */
***************
*** 77,81 ****
    /**
     * Constructor which takes a smoothing factor
!    * 
     * @param smooth "smoothing" factor
     */
--- 86,90 ----
    /**
     * Constructor which takes a smoothing factor
!    *
     * @param smooth "smoothing" factor
     */
***************
*** 94,100 ****
      m_smooth= config.getDouble(PREFIX + "smooth", 0.5);
    }
!   
   /**
!   * Add an example to the data set of this booster 
    * @param index
    * @param label
--- 103,109 ----
      m_smooth= config.getDouble(PREFIX + "smooth", 0.5);
    }
! 
   /**
!   * Add an example to the data set of this booster
    * @param index
    * @param label
***************
*** 114,118 ****
        }
      } else {
!       failed= "Adaboost.addExample expected a label which is either 0 or 1. It received " + 
                 l;
      }
--- 123,127 ----
        }
      } else {
!       failed= "Adaboost.addExample expected a label which is either 0 or 1. It received " +
                 l;
      }
***************
*** 122,129 ****
      }
    }
!   
    /**
     * Add an example to the dataset
!    * Default the weight for this example to 1  
     * If this method is used, then this booster will assume
     * that all the sample weights are 1
--- 131,138 ----
      }
    }
! 
    /**
     * Add an example to the dataset
!    * Default the weight for this example to 1
     * If this method is used, then this booster will assume
     * that all the sample weights are 1
***************
*** 167,179 ****
        m_weights[index]= m_oldWeights[index]= defaultWeight;
        m_labels[index]= a.getLabel();
!       if (a.getLabel()==POSITIVE_LABEL)  
!         m_posExamples[m_posIndex++] = index; 
        else if (a.getLabel()==NEGATIVE_LABEL)
!         m_negExamples[m_negIndex++] = index;  
        else {
          System.err.println("Label of example is unknown to adaboost");
          System.exit(2);
        }
!       
        m_sampleWeights[index]= a.getWeight();
      }
--- 176,188 ----
        m_weights[index]= m_oldWeights[index]= defaultWeight;
        m_labels[index]= a.getLabel();
!       if (a.getLabel()==POSITIVE_LABEL)
!         m_posExamples[m_posIndex++] = index;
        else if (a.getLabel()==NEGATIVE_LABEL)
!         m_negExamples[m_negIndex++] = index;
        else {
          System.err.println("Label of example is unknown to adaboost");
          System.exit(2);
        }
! 
        m_sampleWeights[index]= a.getWeight();
      }
***************
*** 181,185 ****
      m_tmpList.clear(); // free the memory
    }
!   
    public void finalizeData() {
      finalizeData(1.0);
--- 190,194 ----
      m_tmpList.clear(); // free the memory
    }
! 
    public void finalizeData() {
      finalizeData(1.0);
***************
*** 213,217 ****
      return r;
    }
!   
    /**
     *
--- 222,226 ----
      return r;
    }
! 
    /**
     *
***************
*** 230,234 ****
      return m_numExamples;
    }
!   
    /**
     *
--- 239,243 ----
      return m_numExamples;
    }
! 
    /**
     *
***************
*** 237,242 ****
      return m_totalWeight;
    }
!   
!     
      /**
       * Returns a string with all the weights, margins, etc
--- 246,251 ----
      return m_totalWeight;
    }
! 
! 
      /**
       * Returns a string with all the weights, margins, etc
***************
*** 246,251 ****
          ret.append(getParamString());
          for (int i=0; i<m_margins.length; i++){
!             ret.append(String.format("[%d];[%.4f];[%.4f];[%.4f];\n", 
! 				     m_labels[i], m_margins[i], m_weights[i], 
  				     m_potentials[i]));
          }
--- 255,260 ----
          ret.append(getParamString());
          for (int i=0; i<m_margins.length; i++){
!             ret.append(String.format("[%d];[%.4f];[%.4f];[%.4f];\n",
! 				     m_labels[i], m_margins[i], m_weights[i],
  				     m_potentials[i]));
          }
***************
*** 318,321 ****
--- 327,426 ----
  
  
+ 
+   /**
+    * @param z - any double
+    * @return If z is negative return -1 else return 1
+    */
+   public static double sign(double z){
+     if( Double.compare(z, 0.0) == 0 ){
+       return 1.0;
+     } else if( Double.compare(z, -0.0) == 0 ){
+       return -1.0;
+     }
+ 
+     if(z > 0){
+       return 1.0;
+     } else {
+       return -1.0;
+     }
+   }
+ 
+   /**
+    * Get the "step" of the hypothesis on an example.  The step is
+    * defined as $y_j * h_i(x_j)$ for example j and hypothesis i.
+    * @param simple_label - The label of the example as given by m_labels
+    * @param hyp_pred - The hypothesized value of the example
+    * @return +1 if label matches hyp, -1 if label doesn't match hyp, 0 if no hyp
+    */
+   public double getStep(short simple_label, double hyp_pred) {
+     double step = getLabel(simple_label)*hyp_pred;
+     double EPS = 0.000001;
+     if(Math.abs(step) < EPS) return 0.0;
+     return sign(step);
+   }
+ 
+   public double getLabel(short simple_label) {
+     return sign(-simple_label+0.5);
+   }
+ 
+   protected double getHypErr(Bag[] bags, int[][] exampleIndex) {
+     double hyp_err = 0.0;
+     double gamma = 0.0;
+     double total_weight = 0.0;
+ 
+     // Keep track of which hypotheses had hypotheses associated with them.
+     boolean[] examplesWithHyp = new boolean[m_margins.length];
+     m_hypPredictions = new double[m_margins.length];
+     for (int i=0; i < exampleIndex.length; i++){
+       int[] index = exampleIndex[i];
+       BinaryBag b = (BinaryBag) bags[i];
+       for (int j=0; j < index.length; j++){
+         int example = index[j];
+         if (this instanceof BrownBoost)
+             m_hypPredictions[example] = ((BrownBag)b).calcPrediction(1.0,1.0).getClassScores()[0];
+           else
+             m_hypPredictions[example] = b.calcPrediction().getClassScores()[0];
+       }
+     }
+ 
+ 
+     int numExamplesWithHyps = 0;
+     double weight;
+     // Get all examples that have a hypothesis associated with them
+     for (int i= 0; i < exampleIndex.length; i++) {
+       int[] indexes= exampleIndex[i];
+       for (int j= 0; j < indexes.length; j++) {
+         int example   = indexes[j];
+         examplesWithHyp[example] = true;
+         numExamplesWithHyps += 1;
+ 
+         double step   = getStep(m_labels[example], m_hypPredictions[example]);
+         gamma += m_weights[example]*step;
+         if (step < 0)   // We got it wrong
+           hyp_err += 1;
+       }
+     }
+ 
+     // Get all examples that have no hypothesis associated with them.
+     for (int i=0; i < m_margins.length; i++) {
+       total_weight += m_weights[i];
+       if(!examplesWithHyp[i]){
+         m_hypPredictions[i] = 0;
+         //System.out.println("m_hypPredictions[" + i + "," + example + "]: " + 0 + " (No hyp for example " + example + ")");
+       }
+     }
+ 
+     hyp_err /= numExamplesWithHyps;
+     gamma /= (double)total_weight;
+ 
+     if (numExamplesWithHyps > 0) {
+       System.out.println("Num Examples with predictions: " + numExamplesWithHyps + "/" + m_margins.length);
+       System.out.println("Gamma: " + gamma);
+     }
+     return gamma;
+   }
+ 
+ 
+ 
    /**
     * Returns the predictions associated with a list of bags representing a
***************
*** 337,345 ****
        }
      }*/
      return getPredictions(bags);
    }
  
!   
!   
    /**
     * AdaBoost uses e^(-margin) as the weight calculation
--- 442,451 ----
        }
      }*/
+     double gamma = getHypErr(bags, exampleIndex);
      return getPredictions(bags);
    }
  
! 
! 
    /**
     * AdaBoost uses e^(-margin) as the weight calculation
***************
*** 348,358 ****
    	return Math.exp(-1 * margin);
    }
!   
    /**
!    *  Update the examples m_margins and m_weights using the 
     *  exponential update
     *  @param predictions values for examples
     *  @param exampleIndex the list of examples to update
!    */ 
    public void update(Prediction[] predictions, int[][] exampleIndex) {
      // save old m_weights
--- 454,464 ----
    	return Math.exp(-1 * margin);
    }
! 
    /**
!    *  Update the examples m_margins and m_weights using the
     *  exponential update
     *  @param predictions values for examples
     *  @param exampleIndex the list of examples to update
!    */
    public void update(Prediction[] predictions, int[][] exampleIndex) {
      // save old m_weights
***************
*** 375,380 ****
    }
  
!   /** 
!    * Defines the state of an example 
     * Inner class used to store a list of Examples
     * The list is converted into the internal data structures for the
--- 481,486 ----
    }
  
!   /**
!    * Defines the state of an example
     * Inner class used to store a list of Examples
     * The list is converted into the internal data structures for the
***************
*** 385,389 ****
      short m_label;
      double m_weight;
!     
      /**
       * Ctor for a TmpData object
--- 491,495 ----
      short m_label;
      double m_weight;
! 
      /**
       * Ctor for a TmpData object
***************
*** 397,401 ****
        m_weight= weight;
      }
!     
      /**
       * Get the index for this example
--- 503,507 ----
        m_weight= weight;
      }
! 
      /**
       * Get the index for this example
***************
*** 405,409 ****
        return m_index;
      }
!     
      /**
       * Get the label for this example
--- 511,515 ----
        return m_index;
      }
! 
      /**
       * Get the label for this example
***************
*** 413,417 ****
        return m_label;
      }
!     
      /**
       * Get the weigh for this example
--- 519,523 ----
        return m_label;
      }
! 
      /**
       * Get the weigh for this example
***************
*** 423,427 ****
    }
  
!   
    /**
     * This is the definition of a bag for AdaBoost. The two m_labels are
--- 529,533 ----
    }
  
! 
    /**
     * This is the definition of a bag for AdaBoost. The two m_labels are
***************
*** 482,487 ****
       * Update the weights in this bag using the weights from the booster
       * The example index is used to find the label and weight for this example
!      * 
!      * @param index the example that is being added to this bag. The index refers to the booster's 
       *        internal data structures
       */
--- 588,593 ----
       * Update the weights in this bag using the weights from the booster
       * The example index is used to find the label and weight for this example
!      *
!      * @param index the example that is being added to this bag. The index refers to the booster's
       *        internal data structures
       */
***************
*** 553,561 ****
        double smoothFactor= m_epsilon * m_totalWeight;
        double EPS = 1e-50;
!       if (Double.isNaN(smoothFactor) || (Math.abs(m_totalWeight)<EPS) || 
            (Math.abs(smoothFactor)<EPS) || Double.isNaN(m_totalWeight)) {
          return new BinaryPrediction(0.0);
        }
!       
        BinaryPrediction p = new BinaryPrediction(
          m_w[1] == m_w[0]
--- 659,667 ----
        double smoothFactor= m_epsilon * m_totalWeight;
        double EPS = 1e-50;
!       if (Double.isNaN(smoothFactor) || (Math.abs(m_totalWeight)<EPS) ||
            (Math.abs(smoothFactor)<EPS) || Double.isNaN(m_totalWeight)) {
          return new BinaryPrediction(0.0);
        }
! 
        BinaryPrediction p = new BinaryPrediction(
          m_w[1] == m_w[0]
***************
*** 568,572 ****
      /**
       * Compare a bag to this bag and output true if they are equal
!      * 
       * @param other
       *            bag to compare to this bag
--- 674,678 ----
      /**
       * Compare a bag to this bag and output true if they are equal
!      *
       * @param other
       *            bag to compare to this bag

Index: NotNormalizedPredException.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/NotNormalizedPredException.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** NotNormalizedPredException.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- NotNormalizedPredException.java	25 Mar 2008 01:00:28 -0000	1.2
***************
*** 1,11 ****
  package jboost.booster;
  
! /** 
   * This exception gets thrown if a prediction that is supposed to be
!  * normalized, becomes unnormalized. 
   * @author Aaron Arvey
   */
  public class NotNormalizedPredException  extends RuntimeException {
!     NotNormalizedPredException (String m) { 
          message = m;
      }
--- 1,11 ----
  package jboost.booster;
  
! /**
   * This exception gets thrown if a prediction that is supposed to be
!  * normalized, becomes unnormalized.
   * @author Aaron Arvey
   */
  public class NotNormalizedPredException  extends RuntimeException {
!     NotNormalizedPredException (String m) {
          message = m;
      }

Index: BrownBoostTest.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/BrownBoostTest.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -C2 -d -r1.3 -r1.4
*** BrownBoostTest.java	10 Jul 2007 01:32:52 -0000	1.3
--- BrownBoostTest.java	25 Mar 2008 01:00:27 -0000	1.4
***************
*** 76,83 ****
          //TODO Implement newBag().
      }
!     
!     
      /**
!      * This test indicates whether or not the update algorithm in 
       * BrownBoost is producing the correct weights.  This is the core
       * of the BrownBoost algorithm and is thus the most important to test.
--- 76,83 ----
          //TODO Implement newBag().
      }
! 
! 
      /**
!      * This test indicates whether or not the update algorithm in
       * BrownBoost is producing the correct weights.  This is the core
       * of the BrownBoost algorithm and is thus the most important to test.
***************
*** 123,127 ****
          }
        }
!       
        for (int i=0; i < ones.length; i++) {
          ones[i]= indices[COUNT/2 + i];
--- 123,127 ----
          }
        }
! 
        for (int i=0; i < ones.length; i++) {
          ones[i]= indices[COUNT/2 + i];
***************
*** 146,150 ****
        */
      }
!     
      final public void testGetPredictions() {
          //TODO Implement getPredictions().
--- 146,150 ----
        */
      }
! 
      final public void testGetPredictions() {
          //TODO Implement getPredictions().

Index: LogLossBoostTest.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/LogLossBoostTest.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** LogLossBoostTest.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- LogLossBoostTest.java	25 Mar 2008 01:00:27 -0000	1.2
***************
*** 10,16 ****
   */
  public class LogLossBoostTest extends AbstractBoosterTest {
!   
    LogLossBoost m_logLoss;
!             
    /**
     * @param arg0
--- 10,16 ----
   */
  public class LogLossBoostTest extends AbstractBoosterTest {
! 
    LogLossBoost m_logLoss;
! 
    /**
     * @param arg0
***************
*** 37,42 ****
      m_logLoss= new LogLossBoost();
    }
!   
!   
    public final void testFinalizeData() {
      //TODO Implement finalizeData().
--- 37,42 ----
      m_logLoss= new LogLossBoost();
    }
! 
! 
    public final void testFinalizeData() {
      //TODO Implement finalizeData().
***************
*** 71,75 ****
        }
      }
!     
      for (int i=0; i < ones.length; i++) {
        ones[i]= indices[COUNT/2 + i];
--- 71,75 ----
        }
      }
! 
      for (int i=0; i < ones.length; i++) {
        ones[i]= indices[COUNT/2 + i];

Index: MixedBinaryPrediction.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/MixedBinaryPrediction.java,v
retrieving revision 1.5
retrieving revision 1.6
diff -C2 -d -r1.5 -r1.6
*** MixedBinaryPrediction.java	24 Jan 2008 22:30:16 -0000	1.5
--- MixedBinaryPrediction.java	25 Mar 2008 01:00:27 -0000	1.6
***************
*** 16,20 ****
      public MixedBinaryPrediction() {super();}
  
!     public MixedBinaryPrediction(double p) 
  	throws NotNormalizedPredException {
  	(new Exception("asdf")).printStackTrace();
--- 16,20 ----
      public MixedBinaryPrediction() {super();}
  
!     public MixedBinaryPrediction(double p)
  	throws NotNormalizedPredException {
  	(new Exception("asdf")).printStackTrace();
***************
*** 28,32 ****
      }
  
!     public MixedBinaryPrediction(double p, double dt, double nc) 
  	throws NotNormalizedPredException {
  	super();
--- 28,32 ----
      }
  
!     public MixedBinaryPrediction(double p, double dt, double nc)
  	throws NotNormalizedPredException {
  	super();
***************
*** 35,41 ****
  	normalizingConstant = nc;
      }
-     
  
!     public MixedBinaryPrediction(double p, double dt) 
  	throws NotNormalizedPredException {
  	super();
--- 35,41 ----
  	normalizingConstant = nc;
      }
  
! 
!     public MixedBinaryPrediction(double p, double dt)
  	throws NotNormalizedPredException {
  	super();
***************
*** 44,48 ****
  	normalizingConstant = 1;
      }
!     
  
  
--- 44,48 ----
  	normalizingConstant = 1;
      }
! 
  
  
***************
*** 52,56 ****
      }
  
!     /** 
       * Be very careful with how this is used. See NormalizedPrediction
       * for details.
--- 52,56 ----
      }
  
!     /**
       * Be very careful with how this is used. See NormalizedPrediction
       * for details.
***************
*** 94,98 ****
  	return normalizingConstant;
      }
!     
      public Prediction add(double w, Prediction p) {
  	((MixedBinaryPrediction) p).scale(w);
--- 94,98 ----
  	return normalizingConstant;
      }
! 
      public Prediction add(double w, Prediction p) {
  	((MixedBinaryPrediction) p).scale(w);
***************
*** 100,109 ****
  	return this;
      }
!     
      public boolean equals(Prediction other) {
        MixedBinaryPrediction bp= (MixedBinaryPrediction) other;
!       return (prediction == bp.prediction && deltaT == bp.deltaT && normalizingConstant == bp.normalizingConstant); 
      }
!     
      public String toString() {
  	return "MixedBinaryPrediction. p(1)= " + prediction;
--- 100,109 ----
  	return this;
      }
! 
      public boolean equals(Prediction other) {
        MixedBinaryPrediction bp= (MixedBinaryPrediction) other;
!       return (prediction == bp.prediction && deltaT == bp.deltaT && normalizingConstant == bp.normalizingConstant);
      }
! 
      public String toString() {
  	return "MixedBinaryPrediction. p(1)= " + prediction;

Index: MulticlassWrapMH.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/MulticlassWrapMH.java,v
retrieving revision 1.5
retrieving revision 1.6
diff -C2 -d -r1.5 -r1.6
*** MulticlassWrapMH.java	13 Oct 2007 04:32:28 -0000	1.5
--- MulticlassWrapMH.java	25 Mar 2008 01:00:28 -0000	1.6
***************
*** 11,15 ****
   * no knowledge of the surrounding wrapper.
   *
!  * The multiclass problem is reduce to a binary problem using a 
   * "one-against-all" reduction.  This reduction maps a multiclass
   * example to k (the # of classes) binary examples.  Namely, example
--- 11,15 ----
   * no knowledge of the surrounding wrapper.
   *
!  * The multiclass problem is reduce to a binary problem using a
   * "one-against-all" reduction.  This reduction maps a multiclass
   * example to k (the # of classes) binary examples.  Namely, example
***************
*** 25,36 ****
  
      /** The underlying m_booster */
!     private AbstractBooster m_booster; 
  
      /** The number of labels */
!     private int m_numLabels;  
!   
      /** The number of labels */
!     private boolean m_isMultiLabel;  
!   
      /**
       * constructor
--- 25,36 ----
  
      /** The underlying m_booster */
!     private AbstractBooster m_booster;
  
      /** The number of labels */
!     private int m_numLabels;
! 
      /** The number of labels */
!     private boolean m_isMultiLabel;
! 
      /**
       * constructor
***************
*** 43,47 ****
  	m_isMultiLabel = isMultiLabel;
      }
!   
      public String toString() {
  	if (m_booster==null) {
--- 43,47 ----
  	m_isMultiLabel = isMultiLabel;
      }
! 
      public String toString() {
  	if (m_booster==null) {
***************
*** 52,64 ****
  	    System.err.println(msg);
  	}
!     
  	return ("MulticlassWrapMH.  # of classes = " + m_numLabels +
  		".\nUnderlying m_booster:\n" + m_booster);
      }
!   
      public void addExample(int index, Label l) {
  	addExample(index, l, 1.0);
      }
!   
      /**
       * @see jboost.booster.Booster#addExample(int, jboost.examples.Label, double)
--- 52,64 ----
  	    System.err.println(msg);
  	}
! 
  	return ("MulticlassWrapMH.  # of classes = " + m_numLabels +
  		".\nUnderlying m_booster:\n" + m_booster);
      }
! 
      public void addExample(int index, Label l) {
  	addExample(index, l, 1.0);
      }
! 
      /**
       * @see jboost.booster.Booster#addExample(int, jboost.examples.Label, double)
***************
*** 80,88 ****
  	m_booster.finalizeData();
      }
!   
      public void clear() {
  	m_booster.clear();
      }
!   
      public Bag newBag() {
  	return new MultiBag();
--- 80,88 ----
  	m_booster.finalizeData();
      }
! 
      public void clear() {
  	m_booster.clear();
      }
! 
      public Bag newBag() {
  	return new MultiBag();
***************
*** 98,102 ****
  	double[][] weights = m_booster.getWeights();
  	for (int i= 0; i < numExamples; i++)
! 	    for (int j=0; j < m_numLabels; j++) 
  		r[i][j]= weights[i*m_numLabels+j][0];
  	return r;
--- 98,102 ----
  	double[][] weights = m_booster.getWeights();
  	for (int i= 0; i < numExamples; i++)
! 	    for (int j=0; j < m_numLabels; j++)
  		r[i][j]= weights[i*m_numLabels+j][0];
  	return r;
***************
*** 126,130 ****
  
  
!   
      /**
       * According to Rob: "This method should never be called.  We need
--- 126,130 ----
  
  
! 
      /**
       * According to Rob: "This method should never be called.  We need
***************
*** 142,147 ****
    	return m_booster.calculateWeight(margin);
      }
!   
!     
      public void update(Prediction[] preds, int[][] index) {
  	int num_preds = preds.length;
--- 142,147 ----
    	return m_booster.calculateWeight(margin);
      }
! 
! 
      public void update(Prediction[] preds, int[][] index) {
  	int num_preds = preds.length;
***************
*** 149,153 ****
  	int[][] uindex = new int[index.length * m_numLabels][];
  	int i, j, t, k;
! 	
  
  	// create array of predictions to pass to underlying booster
--- 149,153 ----
  	int[][] uindex = new int[index.length * m_numLabels][];
  	int i, j, t, k;
! 
  
  	// create array of predictions to pass to underlying booster
***************
*** 159,163 ****
  	    }
  	}
!     
  	// create array of indices to pass to underlying booster
  	t = 0;
--- 159,163 ----
  	    }
  	}
! 
  	// create array of indices to pass to underlying booster
  	t = 0;
***************
*** 172,176 ****
  	m_booster.update(upreds, uindex);
      }
!   
      /**
       * computes theoretical bound as (m_numLabels/2) * theoretical
--- 172,176 ----
  	m_booster.update(upreds, uindex);
      }
! 
      /**
       * computes theoretical bound as (m_numLabels/2) * theoretical
***************
*** 186,190 ****
  	return m_booster.getTotalWeight();
      }
!   
      /**
       * returns the margin values of all of the "examples" used to
--- 186,190 ----
  	return m_booster.getTotalWeight();
      }
! 
      /**
       * returns the margin values of all of the "examples" used to
***************
*** 194,198 ****
  	return m_booster.getMargins();
      }
!   
      /**
       * Returns the predictions associated with a list of bags representing a
--- 194,198 ----
  	return m_booster.getMargins();
      }
! 
      /**
       * Returns the predictions associated with a list of bags representing a
***************
*** 201,220 ****
      public Prediction[] getPredictions(Bag[] b, int[][] exampleIndex) {
  	Bag[] ubags = new Bag[b.length * m_numLabels];
! 	    
  	for (int i = 0; i < b.length; i++)
  	    for (int j = 0; j < m_numLabels; j++)
  		ubags[i * m_numLabels + j] = ((MultiBag) b[i]).bags[j];
! 	    
  	Prediction[] upreds = m_booster.getPredictions(ubags, exampleIndex);
  	Prediction[] preds = new Prediction[b.length];
! 	    
  	for (int i = 0; i < b.length; i++) {
  	    preds[i] = new MultiPrediction();
  	    for (int j = 0; j < m_numLabels; j++)
! 		((MultiPrediction) preds[i]).preds[j] = 
  		    upreds[i * m_numLabels + j];
  	}
  	return preds;
!     }  	
  
      public Prediction[] getPredictions(Bag[] b) {
--- 201,220 ----
      public Prediction[] getPredictions(Bag[] b, int[][] exampleIndex) {
  	Bag[] ubags = new Bag[b.length * m_numLabels];
! 
  	for (int i = 0; i < b.length; i++)
  	    for (int j = 0; j < m_numLabels; j++)
  		ubags[i * m_numLabels + j] = ((MultiBag) b[i]).bags[j];
! 
  	Prediction[] upreds = m_booster.getPredictions(ubags, exampleIndex);
  	Prediction[] preds = new Prediction[b.length];
! 
  	for (int i = 0; i < b.length; i++) {
  	    preds[i] = new MultiPrediction();
  	    for (int j = 0; j < m_numLabels; j++)
! 		((MultiPrediction) preds[i]).preds[j] =
  		    upreds[i * m_numLabels + j];
  	}
  	return preds;
!     }
  
      public Prediction[] getPredictions(Bag[] b) {
***************
*** 223,238 ****
  	return nothing;
      }
!   
!   
      public double getLoss(Bag[] b) {
  	Bag[] ubags = new Bag[b.length * m_numLabels];
!     
  	for (int i = 0; i < b.length; i++)
  	    for (int j = 0; j < m_numLabels; j++)
  		ubags[i * m_numLabels + j] = ((MultiBag) b[i]).bags[j];
!     
  	return m_booster.getLoss(ubags);
      }
!   
      /**
       * This is the bag class associated with this booster.  Each bag
--- 223,238 ----
  	return nothing;
      }
! 
! 
      public double getLoss(Bag[] b) {
  	Bag[] ubags = new Bag[b.length * m_numLabels];
! 
  	for (int i = 0; i < b.length; i++)
  	    for (int j = 0; j < m_numLabels; j++)
  		ubags[i * m_numLabels + j] = ((MultiBag) b[i]).bags[j];
! 
  	return m_booster.getLoss(ubags);
      }
! 
      /**
       * This is the bag class associated with this booster.  Each bag
***************
*** 241,247 ****
       */
      class MultiBag extends Bag {
!     
  	private Bag[] bags;      // underlying bags
!     
  	private MultiBag() {
  	    bags = new Bag[m_numLabels];
--- 241,247 ----
       */
      class MultiBag extends Bag {
! 
  	private Bag[] bags;      // underlying bags
! 
  	private MultiBag() {
  	    bags = new Bag[m_numLabels];
***************
*** 249,253 ****
  		bags[j] = m_booster.newBag();
  	}
!     
  	public String toString() {
  	    String s = "MultiBag.\n";
--- 249,253 ----
  		bags[j] = m_booster.newBag();
  	}
! 
  	public String toString() {
  	    String s = "MultiBag.\n";
***************
*** 256,265 ****
  	    return s;
  	}
!     
  	public void reset() {
  	    for (int j = 0; j < m_numLabels; j++)
  		bags[j].reset();
  	}
!     
  	public boolean isWeightless() {
  	    for (int j = 0; j < m_numLabels; j++)
--- 256,265 ----
  	    return s;
  	}
! 
  	public void reset() {
  	    for (int j = 0; j < m_numLabels; j++)
  		bags[j].reset();
  	}
! 
  	public boolean isWeightless() {
  	    for (int j = 0; j < m_numLabels; j++)
***************
*** 275,279 ****
  		bags[j].addExample(s + j);
  	}
!     
  	public void subtractExample(int index) {
  	    int s = index * m_numLabels;
--- 275,279 ----
  		bags[j].addExample(s + j);
  	}
! 
  	public void subtractExample(int index) {
  	    int s = index * m_numLabels;
***************
*** 281,285 ****
  		bags[j].subtractExample(s + j);
  	}
!     
  	public void addExampleList(int[] l) {
  	    int i;
--- 281,285 ----
  		bags[j].subtractExample(s + j);
  	}
! 
  	public void addExampleList(int[] l) {
  	    int i;
***************
*** 294,298 ****
  	    }
  	}
!     
  	public void subtractExampleList(int[] l) {
  	    int i;
--- 294,298 ----
  	    }
  	}
! 
  	public void subtractExampleList(int[] l) {
  	    int i;
***************
*** 307,311 ****
  	    }
  	}
!     
  	public void addBag(Bag b) {
  	    MultiBag other = (MultiBag) b;
--- 307,311 ----
  	    }
  	}
! 
  	public void addBag(Bag b) {
  	    MultiBag other = (MultiBag) b;
***************
*** 313,317 ****
  		bags[j].addBag(other.bags[j]);
  	}
!     
  	public void subtractBag(Bag b) {
  	    MultiBag other = (MultiBag) b;
--- 313,317 ----
  		bags[j].addBag(other.bags[j]);
  	}
! 
  	public void subtractBag(Bag b) {
  	    MultiBag other = (MultiBag) b;
***************
*** 319,323 ****
  		bags[j].subtractBag(other.bags[j]);
  	}
!     
  	public void copyBag(Bag b) {
  	    MultiBag other = (MultiBag) b;
--- 319,323 ----
  		bags[j].subtractBag(other.bags[j]);
  	}
! 
  	public void copyBag(Bag b) {
  	    MultiBag other = (MultiBag) b;
***************
*** 325,329 ****
  		bags[j].copyBag(other.bags[j]);
  	}
!     
  	public void refresh(int index) {
  	    int s = index * m_numLabels;
--- 325,329 ----
  		bags[j].copyBag(other.bags[j]);
  	}
! 
  	public void refresh(int index) {
  	    int s = index * m_numLabels;
***************
*** 331,335 ****
  		bags[j].refresh(s + j);
  	}
!     
  	public void refreshList(int[] l) {
  	    int i;
--- 331,335 ----
  		bags[j].refresh(s + j);
  	}
! 
  	public void refreshList(int[] l) {
  	    int i;
***************
*** 344,348 ****
  	    }
  	}
!     
  	/**
  	 * Computes the loss for this bag.  This loss is only meaningful for
--- 344,348 ----
  	    }
  	}
! 
  	/**
  	 * Computes the loss for this bag.  This loss is only meaningful for
***************
*** 355,359 ****
  	    return loss;
  	}
!     
  	public double getLoss(int s)
  	    throws jboost.NotSupportedException {
--- 355,359 ----
  	    return loss;
  	}
! 
  	public double getLoss(int s)
  	    throws jboost.NotSupportedException {
***************
*** 364,368 ****
  	}
      }
!   
  
  
--- 364,368 ----
  	}
      }
! 
  
  
***************
*** 370,374 ****
       * This is the prediction class associated with this booster.
       * Each prediction is composed of an array of predictions from the
!      * underlying booster, one for each class.  
       */
      class MultiPrediction extends Prediction {
--- 370,374 ----
       * This is the prediction class associated with this booster.
       * Each prediction is composed of an array of predictions from the
!      * underlying booster, one for each class.
       */
      class MultiPrediction extends Prediction {
***************
*** 377,381 ****
  	 */
  	private Prediction[] preds;
!     
  	/**
  	 * Constructor.
--- 377,381 ----
  	 */
  	private Prediction[] preds;
! 
  	/**
  	 * Constructor.
***************
*** 384,391 ****
  	    preds = new Prediction[m_numLabels];
  	}
!     
  	public Object clone() {
  	    MultiPrediction newpred = new MultiPrediction();
!       
  	    for (int j = 0; j < m_numLabels; j++) {
  		newpred.preds[j] = (Prediction) preds[j].clone();
--- 384,391 ----
  	    preds = new Prediction[m_numLabels];
  	}
! 
  	public Object clone() {
  	    MultiPrediction newpred = new MultiPrediction();
! 
  	    for (int j = 0; j < m_numLabels; j++) {
  		newpred.preds[j] = (Prediction) preds[j].clone();
***************
*** 393,397 ****
  	    return newpred;
  	}
!     
  	public Prediction add(Prediction p) {
  	    for (int j = 0; j < m_numLabels; j++) {
--- 393,397 ----
  	    return newpred;
  	}
! 
  	public Prediction add(Prediction p) {
  	    for (int j = 0; j < m_numLabels; j++) {
***************
*** 400,404 ****
  	    return this;
  	}
!     
  	public Prediction scale(double w) {
  	    for (int j = 0; j < m_numLabels; j++)
--- 400,404 ----
  	    return this;
  	}
! 
  	public Prediction scale(double w) {
  	    for (int j = 0; j < m_numLabels; j++)
***************
*** 406,410 ****
  	    return this;
  	}
!     
  	public Prediction add(double w, Prediction p) {
  	    for (int j = 0; j < m_numLabels; j++) {
--- 406,410 ----
  	    return this;
  	}
! 
  	public Prediction add(double w, Prediction p) {
  	    for (int j = 0; j < m_numLabels; j++) {
***************
*** 413,417 ****
  	    return this;
  	}
!     
  	public double[] getMarginsSingleLabel(Label l) {
  	    int maxClass = -1;
--- 413,417 ----
  	    return this;
  	}
! 
  	public double[] getMarginsSingleLabel(Label l) {
  	    int maxClass = -1;
***************
*** 433,437 ****
  	    return ret;
  	}
! 	
  	public double[] getMarginsMultiLabel(Label l) {
  	    double[] ret = new double[m_numLabels];
--- 433,437 ----
  	    return ret;
  	}
! 
  	public double[] getMarginsMultiLabel(Label l) {
  	    double[] ret = new double[m_numLabels];
***************
*** 444,457 ****
  
  	public double[] getMargins(Label l) {
! 	    if (m_isMultiLabel) 
  		return getMarginsMultiLabel(l);
  	    return getMarginsSingleLabel(l);
  	}
!     
  
  	public double[] getClassScores() {
  	    double[] scores = new double[m_numLabels];
  	    double[] uscore;
!       
  	    for (int j = 0; j < m_numLabels; j++) {
  		uscore = preds[j].getClassScores();
--- 444,457 ----
  
  	public double[] getMargins(Label l) {
! 	    if (m_isMultiLabel)
  		return getMarginsMultiLabel(l);
  	    return getMarginsSingleLabel(l);
  	}
! 
  
  	public double[] getClassScores() {
  	    double[] scores = new double[m_numLabels];
  	    double[] uscore;
! 
  	    for (int j = 0; j < m_numLabels; j++) {
  		uscore = preds[j].getClassScores();
***************
*** 460,464 ****
  	    return scores;
  	}
!     
  	/**
  	 * Check to see if this MultiPrediction is the same as the other
--- 460,464 ----
  	    return scores;
  	}
! 
  	/**
  	 * Check to see if this MultiPrediction is the same as the other
***************
*** 476,480 ****
  	    return retval;
  	}
!     
  	public String toString() {
  	    String s = "MultiPrediction.\n";
--- 476,480 ----
  	    return retval;
  	}
! 
  	public String toString() {
  	    String s = "MultiPrediction.\n";
***************
*** 483,487 ****
  	    return s;
  	}
!     
  	public String shortText() {
  	    String s = "[,"+preds[0];
--- 483,487 ----
  	    return s;
  	}
! 
  	public String shortText() {
  	    String s = "[,"+preds[0];
***************
*** 490,504 ****
  	    return s+"]";
  	}
!     
  	public String cPreamble() {
  	    String code = "";
!       
  	    code +=  "typedef double Prediction_t[" + m_numLabels + "];\n";
!       
  	    code +=  "#define reset_pred()  { \\\n";
  	    for (int i = 0; i < m_numLabels; i++)
  		code += "        p["+i+"] = 0.0; \\\n";
  	    code += "     }\n";
!       
  	    code +=  "#define add_pred(";
  	    for (int i = 0; i < m_numLabels; i++)
--- 490,504 ----
  	    return s+"]";
  	}
! 
  	public String cPreamble() {
  	    String code = "";
! 
  	    code +=  "typedef double Prediction_t[" + m_numLabels + "];\n";
! 
  	    code +=  "#define reset_pred()  { \\\n";
  	    for (int i = 0; i < m_numLabels; i++)
  		code += "        p["+i+"] = 0.0; \\\n";
  	    code += "     }\n";
! 
  	    code +=  "#define add_pred(";
  	    for (int i = 0; i < m_numLabels; i++)
***************
*** 508,512 ****
  		code += "        p["+i+"] += X"+i+"; \\\n";
  	    code += "     }\n";
!       
  	    code += "#define finalize_pred() \\\n";
  	    code += "        (r ? ( \\\n";
--- 508,512 ----
  		code += "        p["+i+"] += X"+i+"; \\\n";
  	    code += "     }\n";
! 
  	    code += "#define finalize_pred() \\\n";
  	    code += "        (r ? ( \\\n";
***************
*** 514,524 ****
  		code += "                r["+i+"] = p["+i+"], \\\n";
  	    code += "                p[0])       : p[0])\n";
!       
  	    return code;
  	}
!     
  	public String javaPreamble() {
  	    String code = "";
!       
  	    code += ""
  		+ "  static private double[] p = new double[" + m_numLabels + "];\n"
--- 514,524 ----
  		code += "                r["+i+"] = p["+i+"], \\\n";
  	    code += "                p[0])       : p[0])\n";
! 
  	    return code;
  	}
! 
  	public String javaPreamble() {
  	    String code = "";
! 
  	    code += ""
  		+ "  static private double[] p = new double[" + m_numLabels + "];\n"
***************
*** 539,548 ****
  	    return code;
  	}
!     
  	public double[] toCodeArray() {
  	    return getClassScores();
  	}
      }
!   
      /** a main for testing */
      public static void main(String[] argv) {
--- 539,548 ----
  	    return code;
  	}
! 
  	public double[] toCodeArray() {
  	    return getClassScores();
  	}
      }
! 
      /** a main for testing */
      public static void main(String[] argv) {
***************
*** 550,561 ****
  	    AbstractBooster ada =
  		new DebugWrap(new MulticlassWrapMH(new DebugWrap(new AdaBoost(0.0)), 2, true));
!       
  	    for(int i=0; i< 10; i++) {
  		ada.addExample(i,new Label(i % 2));
  	    }
!       
  	    ada.finalizeData();
  	    if(Monitor.logLevel>3) Monitor.log(ada);
!       
  	    int[] elements = {1,2,6,3,4};
  	    String s="\n Generating a bag with elements:\n" +elements[0];
--- 550,561 ----
  	    AbstractBooster ada =
  		new DebugWrap(new MulticlassWrapMH(new DebugWrap(new AdaBoost(0.0)), 2, true));
! 
  	    for(int i=0; i< 10; i++) {
  		ada.addExample(i,new Label(i % 2));
  	    }
! 
  	    ada.finalizeData();
  	    if(Monitor.logLevel>3) Monitor.log(ada);
! 
  	    int[] elements = {1,2,6,3,4};
  	    String s="\n Generating a bag with elements:\n" +elements[0];
***************
*** 564,573 ****
  	    Bag bag = ada.newBag(elements);
  	    if(Monitor.logLevel>3) Monitor.log(bag);
!       
  	    Prediction[] p;
  	    p = ada.getPredictions(new Bag[] {bag}); // calc optimal prediction for this bag
! 	    if(Monitor.logLevel>3) Monitor.log("best prediction for this bag is " + 
  					       p[0].getClassScores()[1]);
!       
  	    int[][] exampleList = new int[1][];
  	    exampleList[0] = elements;
--- 564,573 ----
  	    Bag bag = ada.newBag(elements);
  	    if(Monitor.logLevel>3) Monitor.log(bag);
! 
  	    Prediction[] p;
  	    p = ada.getPredictions(new Bag[] {bag}); // calc optimal prediction for this bag
! 	    if(Monitor.logLevel>3) Monitor.log("best prediction for this bag is " +
  					       p[0].getClassScores()[1]);
! 
  	    int[][] exampleList = new int[1][];
  	    exampleList[0] = elements;
***************
*** 579,583 ****
  	    if(Monitor.logLevel>3) Monitor.log("now best prediction for this bag is "+
  					       (ada.getPredictions(new Bag[] {bag})[0]).getClassScores()[1]);
!       
  	}
  	catch(Exception e) {
--- 579,583 ----
  	    if(Monitor.logLevel>3) Monitor.log("now best prediction for this bag is "+
  					       (ada.getPredictions(new Bag[] {bag})[0]).getClassScores()[1]);
! 
  	}
  	catch(Exception e) {
***************
*** 585,592 ****
  	    e.printStackTrace();
  	}
!     
!     
      }
!   
      /* (non-Javadoc)
       * @see jboost.booster.Booster#init(jboost.controller.Configuration)
--- 585,592 ----
  	    e.printStackTrace();
  	}
! 
! 
      }
! 
      /* (non-Javadoc)
       * @see jboost.booster.Booster#init(jboost.controller.Configuration)
***************
*** 594,598 ****
      public void init(Configuration config) {
  	// TODO Auto-generated method stub
!     
      }
  }
--- 594,598 ----
      public void init(Configuration config) {
  	// TODO Auto-generated method stub
! 
      }
  }

Index: MixedBinaryPredictionTest.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/MixedBinaryPredictionTest.java,v
retrieving revision 1.1
retrieving revision 1.2
diff -C2 -d -r1.1 -r1.2
*** MixedBinaryPredictionTest.java	2 Oct 2007 02:32:48 -0000	1.1
--- MixedBinaryPredictionTest.java	25 Mar 2008 01:00:27 -0000	1.2
***************
*** 15,19 ****
  
      /**
!      * Constructor for 
       * @param arg0
       */
--- 15,19 ----
  
      /**
!      * Constructor for
       * @param arg0
       */
***************
*** 22,26 ****
  	System.out.println("Making MixedPred class");
      }
!     
      final public void testAdd() {
  	System.out.println("test add");
--- 22,26 ----
  	System.out.println("Making MixedPred class");
      }
! 
      final public void testAdd() {
  	System.out.println("test add");
***************
*** 29,33 ****
  	MixedBinaryPrediction np = new MixedBinaryPrediction();
  	assertEquals(true, Prediction.isNormPred(np));
! 	
      }
  
--- 29,33 ----
  	MixedBinaryPrediction np = new MixedBinaryPrediction();
  	assertEquals(true, Prediction.isNormPred(np));
! 
      }
  
***************
*** 68,75 ****
  	boolean exceptionThrown = false;
  	try {
! 	    p = new MixedBinaryPrediction(5); 
  	} catch (NotNormalizedPredException e) {
...
 
[truncated message content]

[Jboost-cvs] jboost/src/jboost/examples TextDescription.java, 1.1.1.1, 1.2

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/examples
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv31648

Modified Files:
	TextDescription.java 
Log Message:
Put in a hack for text processing (ngrams are removed)

Index: TextDescription.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/examples/TextDescription.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** TextDescription.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- TextDescription.java	16 Feb 2008 00:02:19 -0000	1.2
***************
*** 102,107 ****
  	    st = new FixedNgram(string, ngramsize);
  	try {
! 	    while ( st.hasMoreElements()) {
! 	   	word = (String) st.nextElement();
  		// if(Monitor.logLevel>3) Monitor.log("DIAG TextAttReader.str2Att: word=" +word); 
  		if (wt.frozen && !wt.map.containsKey(word))
--- 102,110 ----
  	    st = new FixedNgram(string, ngramsize);
  	try {
! 	    String[] wordArr = string.split(" ");
! 	    //while ( st.hasMoreElements()) {
! 	    for (int i=0; i<wordArr.length; i++) {
! 	   	//word = (String) st.nextElement();
! 		word = wordArr[i];
  		// if(Monitor.logLevel>3) Monitor.log("DIAG TextAttReader.str2Att: word=" +word); 
  		if (wt.frozen && !wt.map.containsKey(word))

[Jboost-cvs] jboost/src/jboost/learner InequalitySplitter.java, 1.1.1.1, 1.2 InequalitySplitterBuilder.java, 1.2, 1.3 StumpSplitterBuilderFamily.java, 1.1.1.1, 1.2

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/learner
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv26189/learner

Modified Files:
	InequalitySplitter.java InequalitySplitterBuilder.java 
	StumpSplitterBuilderFamily.java 
Log Message:
Cost sensitive normalboost, fixed compile bug in BooleanAttribute

Index: StumpSplitterBuilderFamily.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/learner/StumpSplitterBuilderFamily.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** StumpSplitterBuilderFamily.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- StumpSplitterBuilderFamily.java	24 Jan 2008 22:48:54 -0000	1.2
***************
*** 9,12 ****
--- 9,13 ----
  import jboost.examples.RealAttribute;
  import jboost.examples.SetAttribute;
+ import jboost.examples.*;
  import jboost.monitor.Monitor;
  
***************
*** 43,46 ****
--- 44,49 ----
      DiscreteAttribute discreteAttribute=new DiscreteAttribute(0);
      SetAttribute setAttribute=new SetAttribute();
+     IntegerAttribute integerAttribute=new IntegerAttribute();
+     BooleanAttribute booleanAttribute=new BooleanAttribute();
      
      Vector retval=new Vector();
***************
*** 57,60 ****
--- 60,66 ----
            tmpSB= new InequalitySplitterBuilder(attr[i], booster,
                new AttributeDescription[] {attrDesc[attr[i]]});
+ 	} else if (attributeClass.equals(integerAttribute.getClass())) {
+           tmpSB= new InequalitySplitterBuilder(attr[i], booster,
+               new AttributeDescription[] {attrDesc[attr[i]]});
          } else if (attributeClass.equals(discreteAttribute.getClass())) {
            tmpSB= new EqualitySplitterBuilder(attr[i], booster,

Index: InequalitySplitterBuilder.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/learner/InequalitySplitterBuilder.java,v
retrieving revision 1.2
retrieving revision 1.3
diff -C2 -d -r1.2 -r1.3
*** InequalitySplitterBuilder.java	13 Oct 2007 04:32:28 -0000	1.2
--- InequalitySplitterBuilder.java	24 Jan 2008 22:48:54 -0000	1.3
***************
*** 12,15 ****
--- 12,16 ----
  import jboost.examples.Example;
  import jboost.examples.Label;
+ import jboost.examples.*;
  import jboost.examples.RealAttribute;
  import jboost.monitor.Monitor;
***************
*** 290,297 ****
              throw new RuntimeException("Attempt to addExample() to non-root or finalized SplitterBuilder");
          RealAttribute a= null;
          Attribute t= example.getAttribute(attributeIndex);
          // check that attribute is of the correct class
          try {
!             a= (RealAttribute) t; // try downcasting 
          } catch (ClassCastException e) {
              throw new IncompAttException(
--- 291,319 ----
              throw new RuntimeException("Attempt to addExample() to non-root or finalized SplitterBuilder");
          RealAttribute a= null;
+         IntegerAttribute b= null;
          Attribute t= example.getAttribute(attributeIndex);
+         Label l= example.getLabel();
          // check that attribute is of the correct class
          try {
! 	    if (t instanceof RealAttribute) {
! 		a = (RealAttribute) t; 
! 		if (a.isDefined() && (l != null)) {
! 		    IVL ivl= new IVL(index, a.getValue(), l);
! 		    tempList.add(ivl);
! 		}
! 	    } else if (t instanceof IntegerAttribute) {
! 		b = (IntegerAttribute) t;
! 		if (b.isDefined() && (l != null)) {
! 		    IVL ivl= new IVL(index, b.getValue(), l);
! 		    tempList.add(ivl);
! 		}
! 	    } else {
! 		throw new IncompAttException(
! 					     index,
! 					     attributeIndex,
! 					     "IntegerAttribute or RealAttribute",
! 					     t.getClass());
! 	    }
! 	    // check if attribute and label are defined
          } catch (ClassCastException e) {
              throw new IncompAttException(
***************
*** 301,314 ****
                  t.getClass());
          }
-         Label l= example.getLabel();
          largestIndex= (largestIndex > index) ? largestIndex : index;
          // remember the largest index seen
          //	if(Monitor.logLevel>3) Monitor.log("In RootInequalitySplitterBuilder: a="+a+" t="+t
          //			   +" example="+example);
-         if (a.isDefined()
-             && (l != null)) { // check if attribute and label are defined
-             IVL ivl= new IVL(index, a.getValue(), l);
-             tempList.add(ivl); // add item to list
-         }
          // if(Monitor.logLevel>3) Monitor.log("at end of addExample("+index+")\n"+tempList);
      }
--- 323,330 ----
***************
*** 397,400 ****
--- 413,417 ----
      of the previous (unmasked) example in sortedIndices */
      protected boolean[] potentialSplits;
+ 
      //----------------------------- Private Members -------------------------------------//
      /** an internal class defining the elements of {@link tempList} */
***************
*** 405,408 ****
--- 422,430 ----
              label= l;
          }
+         IVL(int i, int v, Label l) {
+             index= i;
+             value= v;
+             label= l;
+         }
          public int compareTo(Object o) { // defined so that we can use "Collections.sort"
              double value2= ((IVL) o).value;

Index: InequalitySplitter.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/learner/InequalitySplitter.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** InequalitySplitter.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- InequalitySplitter.java	24 Jan 2008 22:48:54 -0000	1.2
***************
*** 5,8 ****
--- 5,9 ----
  import jboost.examples.Instance;
  import jboost.examples.RealAttribute;
+ import jboost.examples.*;
  
  /**
***************
*** 59,64 ****
  	if(t == null) {return(-1);}
  	try {
! 	    RealAttribute a= (RealAttribute) t;	// try downcasting 
! 	    return((a.getValue() > threshold)?1:0);
  	}
  	catch (ClassCastException e) {
--- 60,74 ----
  	if(t == null) {return(-1);}
  	try {
! 	    
! 	    if (t instanceof RealAttribute) {
! 		RealAttribute a= (RealAttribute) t;	// try downcasting 
! 		return((a.getValue() > threshold)?1:0);
! 	    } else if (t instanceof IntegerAttribute) {
! 		IntegerAttribute a= (IntegerAttribute) t;	// try downcasting 
! 		return((a.getValue() > threshold)?1:0);
! 	    } else {
! 		throw new IncompAttException("InequalitySplitter Error:",index
! 					     ,t.getClass());
! 	    }
  	}
  	catch (ClassCastException e) {

[Jboost-cvs] jboost/src/jboost/tokenizer jboost_DataStream.java, 1.2, 1.3

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/tokenizer
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv26189/tokenizer

Modified Files:
	jboost_DataStream.java 
Log Message:
Cost sensitive normalboost, fixed compile bug in BooleanAttribute

Index: jboost_DataStream.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/tokenizer/jboost_DataStream.java,v
retrieving revision 1.2
retrieving revision 1.3
diff -C2 -d -r1.2 -r1.3
*** jboost_DataStream.java	23 Oct 2007 23:52:26 -0000	1.2
--- jboost_DataStream.java	24 Jan 2008 22:48:54 -0000	1.3
***************
*** 298,303 ****
              }
              // make sure the type is one of the three supported
!             if (!(type.equals("finite") || type.equals("text") || type.equals("number") 
!             		|| type.equals("string"))) {
                throw new SpecFileException("Type not valid for attribute " + line);
              }
--- 298,308 ----
              }
              // make sure the type is one of the three supported
!             if (!(type.equals("finite") || 
! 		  type.equals("text") || 
! 		  type.equals("number") ||  
! 		  type.equals("string") || 
! 		  type.equals("int") || 
! 		  type.equals("bool")
! 		  )) {
                throw new SpecFileException("Type not valid for attribute " + line);
              }
***************
*** 357,367 ****
              if (attribute != null) {
                if (name.equals(DataStream.LABELS_ATTR)) {
!                 ed.setLabel(attribute, lineCount);
                } else if (name.equals(DataStream.WEIGHT_ATTR)) {
!                   ed.setWeight(attribute, lineCount);/*
                } else if (name.equals(DataStream.ID_ATTR)) {
! 	      ed.setId(attribute, lineCount);*/
                } else {
!                 ed.addAttribute(attribute);
                }
  
--- 362,372 ----
              if (attribute != null) {
                if (name.equals(DataStream.LABELS_ATTR)) {
! 		  ed.setLabel(attribute, lineCount);
                } else if (name.equals(DataStream.WEIGHT_ATTR)) {
!                   ed.setWeight(attribute, lineCount);
                } else if (name.equals(DataStream.ID_ATTR)) {
! 		  //ed.setId(attribute, lineCount);
                } else {
! 		  ed.addAttribute(attribute);
                }
  

[Jboost-cvs] jboost/src/jboost/examples BooleanAttribute.java, 1.1, 1.2

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/examples
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv26189/examples

Modified Files:
	BooleanAttribute.java 
Log Message:
Cost sensitive normalboost, fixed compile bug in BooleanAttribute

Index: BooleanAttribute.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/examples/BooleanAttribute.java,v
retrieving revision 1.1
retrieving revision 1.2
diff -C2 -d -r1.1 -r1.2
*** BooleanAttribute.java	17 Dec 2007 00:36:31 -0000	1.1
--- BooleanAttribute.java	24 Jan 2008 22:48:54 -0000	1.2
***************
*** 7,11 ****
  
      public BooleanAttribute(boolean value) {this.value=value; setDefined();}
!     public BooleanAttribute() { setDefined(false);}
  
      public boolean getValue() {return value;}
--- 7,11 ----
  
      public BooleanAttribute(boolean value) {this.value=value; setDefined();}
!     public BooleanAttribute() { defined=false;}
  
      public boolean getValue() {return value;}

[Jboost-cvs] jboost/src/jboost/controller Controller.java, 1.6, 1.7 ControllerConfiguration.java, 1.3, 1.4

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/controller
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv26189/controller

Modified Files:
	Controller.java ControllerConfiguration.java 
Log Message:
Cost sensitive normalboost, fixed compile bug in BooleanAttribute

Index: ControllerConfiguration.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/controller/ControllerConfiguration.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -C2 -d -r1.3 -r1.4
*** ControllerConfiguration.java	23 Oct 2007 22:46:32 -0000	1.3
--- ControllerConfiguration.java	24 Jan 2008 22:48:53 -0000	1.4
***************
*** 54,67 ****
    public static final String YABA_C1="c1";
    public static final String YABA_C2="c2";
    public static final String YABA_THETA="theta";
!   public static final String YABA_POS_C="pos_r";
    public static final String YABA_POS_C1="pos_c1";
    public static final String YABA_POS_C2="pos_c2";
    public static final String YABA_POS_THETA="pos_theta";
!   public static final String YABA_NEG_C="neg_r";
    public static final String YABA_NEG_C1="neg_c1";
    public static final String YABA_NEG_C2="neg_c2";
    public static final String YABA_NEG_THETA="neg_theta";
    public static final String BROWN_BOOST_POTENTIAL="brownBoostPotential";
    
    public static final String DEFAULT_BOOSTER = "AdaBoost";
--- 54,71 ----
    public static final String YABA_C1="c1";
    public static final String YABA_C2="c2";
+   public static final String YABA_NC="nc";
    public static final String YABA_THETA="theta";
!   public static final String YABA_POS_C="pos_c";
    public static final String YABA_POS_C1="pos_c1";
    public static final String YABA_POS_C2="pos_c2";
    public static final String YABA_POS_THETA="pos_theta";
!   public static final String YABA_POS_NC="pos_nc";
!   public static final String YABA_NEG_C="neg_c";
    public static final String YABA_NEG_C1="neg_c1";
    public static final String YABA_NEG_C2="neg_c2";
    public static final String YABA_NEG_THETA="neg_theta";
+   public static final String YABA_NEG_NC="neg_nc";
    public static final String BROWN_BOOST_POTENTIAL="brownBoostPotential";
+   public static final String BROWN_COST_SENSITIVE="costSensitive";
    
    public static final String DEFAULT_BOOSTER = "AdaBoost";
***************
*** 94,107 ****
      addOption(YABA_C1, getString("c1", null));
      addOption(YABA_C2, getString("c2", null));
      addOption(YABA_THETA, getString("theta", null));
!     addOption(YABA_POS_C,"pos_r");
!     addOption(YABA_POS_C1,"pos_c1");
!     addOption(YABA_POS_C2,"pos_c2");
!     addOption(YABA_POS_THETA,"pos_theta");
!     addOption(YABA_NEG_C,"neg_r");
!     addOption(YABA_NEG_C1,"neg_c1");
!     addOption(YABA_NEG_C2,"neg_c2");
!     addOption(YABA_NEG_THETA,"neg_theta");
      addOption(BROWN_BOOST_POTENTIAL, getString("potential", null));
    }
      
--- 98,115 ----
      addOption(YABA_C1, getString("c1", null));
      addOption(YABA_C2, getString("c2", null));
+     addOption(YABA_NC, getString("nc", null));
      addOption(YABA_THETA, getString("theta", null));
!     addOption(YABA_POS_C,getString("pos_c",null));
!     addOption(YABA_POS_C1,getString("pos_c1",null));
!     addOption(YABA_POS_C2,getString("pos_c2",null));
!     addOption(YABA_POS_THETA,getString("pos_theta",null));
!     addOption(YABA_POS_NC, getString("pos_nc", null));
!     addOption(YABA_NEG_C,getString("neg_c",null));
!     addOption(YABA_NEG_C1,getString("neg_c1",null));
!     addOption(YABA_NEG_C2,getString("neg_c2",null));
!     addOption(YABA_NEG_THETA,getString("neg_theta",null));
!     addOption(YABA_NEG_NC, getString("neg_nc", null));
      addOption(BROWN_BOOST_POTENTIAL, getString("potential", null));
+     addOption(BROWN_COST_SENSITIVE, getString("costSensitive", null));
    }
      
***************
*** 288,291 ****
--- 296,311 ----
  
  
+   /**
+    * @return should we print potential and exit?
+    */
+   public boolean getCostSensitive() {
+ 	String str = getString(BROWN_COST_SENSITIVE);
+ 	if (str == null) {
+ 	    return false;
+ 	}
+ 	return true;
+   }
+ 
+ 
  
  

Index: Controller.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/controller/Controller.java,v
retrieving revision 1.6
retrieving revision 1.7
diff -C2 -d -r1.6 -r1.7
*** Controller.java	5 Dec 2007 06:39:57 -0000	1.6
--- Controller.java	24 Jan 2008 22:48:53 -0000	1.7
***************
*** 92,96 ****
  	    configuration.printUsage();
  	    e.printStackTrace();
- 	    e.getMessage();
  	} finally {
  	    Monitor.closeLog();
--- 92,95 ----
***************
*** 143,146 ****
--- 142,149 ----
  	    BrownBoost b = (BrownBoost) m_booster;
  	    System.out.println("\tPotential loss of m_booster: " + b.getInitialPotential());
+ 	    if (b.isCostSensitive()) {
+ 	    System.out.println("\tPotential loss of positive examples m_booster: " + b.getPositivePotential());
+ 	    System.out.println("\tPotential loss of negative examples m_booster: " + b.getNegativePotential());
+ 	    }
  	    System.exit(0);
  	}

[Jboost-cvs] jboost/src/jboost/atree InstrumentedAlternatingTree.java, 1.5, 1.6

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/atree
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv26189/atree

Modified Files:
	InstrumentedAlternatingTree.java 
Log Message:
Cost sensitive normalboost, fixed compile bug in BooleanAttribute

Index: InstrumentedAlternatingTree.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/atree/InstrumentedAlternatingTree.java,v
retrieving revision 1.5
retrieving revision 1.6
diff -C2 -d -r1.5 -r1.6
*** InstrumentedAlternatingTree.java	23 Oct 2007 23:52:26 -0000	1.5
--- InstrumentedAlternatingTree.java	24 Jan 2008 22:48:53 -0000	1.6
***************
*** 705,709 ****
      private int[] makeIndices(boolean[] exMask) {
  	int[] examples= null;
! 	int count= 0;
  	for (int j= 0; j < exMask.length; j++)
  	    if (exMask[j] == true)
--- 705,709 ----
      private int[] makeIndices(boolean[] exMask) {
  	int[] examples= null;
! 	int count= 0; 
  	for (int j= 0; j < exMask.length; j++)
  	    if (exMask[j] == true)

[Jboost-cvs] jboost/src/jboost/booster AbstractBooster.java, 1.6, 1.7 AdaBoost.java, 1.4, 1.5 Bag.java, 1.1.1.1, 1.2 BrownBoost.java, 1.9, 1.10 MixedBinaryPrediction.java, 1.4, 1.5 NormalizedPrediction.java, 1.1, 1.2 YabaBoost.java, 1.6, 1.7

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/booster
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv19099

Modified Files:
	AbstractBooster.java AdaBoost.java Bag.java BrownBoost.java 
	MixedBinaryPrediction.java NormalizedPrediction.java 
	YabaBoost.java 
Log Message:
Main changes: one sided prediction is optional (USE_ONE_SIDED), cost asymetric code in yababoost/normalboost, new parameters are optional (USE_BETA_RHO), several other changes as well

Index: Bag.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/Bag.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** Bag.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- Bag.java	24 Jan 2008 22:30:16 -0000	1.2
***************
*** 12,15 ****
--- 12,19 ----
  public abstract class Bag {
  
+ 
+     /** total weight for examples of each label */
+     protected double[] m_w;
+ 
    /**
     * Resets the bag to empty (or to its default setting as defined by
***************
*** 17,21 ****
     */
    public abstract void reset();
! 
    /**
     * If the bag has no weight, then it is considered to be weightless.
--- 21,32 ----
     */
    public abstract void reset();
!     
!     /** 
!      * Get the weights of the bag.
!      */
!     public double[] getWeights() {
! 	return m_w;
!     }
!     
    /**
     * If the bag has no weight, then it is considered to be weightless.

Index: NormalizedPrediction.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/NormalizedPrediction.java,v
retrieving revision 1.1
retrieving revision 1.2
diff -C2 -d -r1.1 -r1.2
*** NormalizedPrediction.java	2 Oct 2007 02:32:07 -0000	1.1
--- NormalizedPrediction.java	24 Jan 2008 22:30:16 -0000	1.2
***************
*** 22,29 ****
       */
      public Prediction add(Prediction p);
- 
      
      /*    public static boolean isNormPred(Prediction p); */
! 
  
  }
--- 22,28 ----
       */
      public Prediction add(Prediction p);
      
      /*    public static boolean isNormPred(Prediction p); */
!     public double getDeltaT();    
  
  }

Index: AbstractBooster.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/AbstractBooster.java,v
retrieving revision 1.6
retrieving revision 1.7
diff -C2 -d -r1.6 -r1.7
*** AbstractBooster.java	23 Oct 2007 22:45:40 -0000	1.6
--- AbstractBooster.java	24 Jan 2008 22:30:16 -0000	1.7
***************
*** 3,6 ****
--- 3,7 ----
  import java.io.Serializable;
  import jboost.controller.Configuration;
+ import jboost.controller.ControllerConfiguration;
  import jboost.monitor.Monitor;
  
***************
*** 66,90 ****
  	  
  
  	  if (result instanceof jboost.booster.YabaBoost) {
! 	      double c1=0, c2=0, theta=0;
! 	      double rpos=0, c1pos=0, c2pos=0, thetapos=0;
! 	      double rneg=0, c1neg=0, c2neg=0, thetaneg=0;
  	      try {
  		  c1 = Double.parseDouble(c.getString("c1", "Z1.0"));
  		  c2 = Double.parseDouble(c.getString("c2", "Z1.0"));
  		  theta = Double.parseDouble(c.getString("theta", "Z0.15"));
! 		  //c1 = Double.parseDouble(c.getString("pos_r", "Z1.0"));
! 		  //c1 = Double.parseDouble(c.getString("pos_c1", "Z1.0"));
! 		  //c2 = Double.parseDouble(c.getString("pos_c2", "Z1.0"));
! 		  //theta = Double.parseDouble(c.getString("pos_theta", "Z0.15"));
  	      } catch (NumberFormatException e) {
! 		  String s = "Need to supply r, c1, c2, and theta!";
! 		  System.err.println(s);
! 		  throw new InstantiationException(s);
  	      }
- 	      jboost.booster.YabaBoost yaba = (jboost.booster.YabaBoost) result;
- 	      yaba.setParams(c1,c2,theta);
- 	      yaba.setCostSensitiveParams(rpos, c1pos, c2pos, thetapos, 
- 					  rneg, c1neg, c2neg, thetaneg);
  	      result = yaba;
  	  }
--- 67,127 ----
  	  
  
+ 
  	  if (result instanceof jboost.booster.YabaBoost) {
! 	      jboost.booster.YabaBoost yaba = (jboost.booster.YabaBoost) result;
! 	      double c1=0, c2=0, theta=0, nc=0;
! 	      double rpos=0, c1pos=0, c2pos=0, thetapos=0, ncpos=0;
! 	      double rneg=0, c1neg=0, c2neg=0, thetaneg=0, ncneg=0;
! 	      ControllerConfiguration conf = (ControllerConfiguration)c;
  	      try {
  		  c1 = Double.parseDouble(c.getString("c1", "Z1.0"));
  		  c2 = Double.parseDouble(c.getString("c2", "Z1.0"));
  		  theta = Double.parseDouble(c.getString("theta", "Z0.15"));
! 		  nc = Double.parseDouble(c.getString("nc", "Z0.15"));
! 		  yaba.setParams(c1,c2,theta,nc);
! 		  if (conf.getCostSensitive()) {
! 		      /*
! 		      System.out.println("  runtime: " + rpos + "," + rneg + 
! 					 "  c1: " + c1pos + "," + c1neg + 
! 					 "  c2: " + c2pos + "," + c2neg + 
! 					 "  theta: " + thetapos + "," + thetaneg + 
! 					 "  nc: " + ncpos + "," + ncneg 
! 					 );
! 		      */
! 		      rpos = Double.parseDouble(c.getString("pos_c","Z"));
! 		      rneg = Double.parseDouble(c.getString("neg_c","Z"));
! 		      c1pos = Double.parseDouble(c.getString("pos_c1","Z"));
! 		      c1neg = Double.parseDouble(c.getString("neg_c1","Z"));
! 		      /*
! 		      System.out.println("  runtime: " + rpos + "," + rneg + 
! 					 "  c1: " + c1pos + "," + c1neg + 
! 					 "  c2: " + c2pos + "," + c2neg + 
! 					 "  theta: " + thetapos + "," + thetaneg + 
! 					 "  nc: " + ncpos + "," + ncneg 
! 					 );
! 		      */
! 		      c2pos = Double.parseDouble(c.getString("pos_c2","Z"));
! 		      c2neg = Double.parseDouble(c.getString("neg_c2","Z"));
! 		      thetapos = Double.parseDouble(c.getString("pos_theta","Z"));
! 		      thetaneg = Double.parseDouble(c.getString("neg_theta","Z"));
! 		      ncpos = Double.parseDouble(c.getString("pos_nc","Z"));
! 		      ncneg = Double.parseDouble(c.getString("neg_nc","Z"));
! 		      /*
! 		      System.out.println("  runtime: " + rpos + "," + rneg + 
! 					 "  c1: " + c1pos + "," + c1neg + 
! 					 "  c2: " + c2pos + "," + c2neg + 
! 					 "  theta: " + thetapos + "," + thetaneg + 
! 					 "  nc: " + ncpos + "," + ncneg 
! 					 );
! 		      */
! 		      yaba.setCostSensitiveParams(rpos, c1pos, c2pos, thetapos, ncpos,
! 						  rneg, c1neg, c2neg, thetaneg, ncneg);
! 		  }
  	      } catch (NumberFormatException e) {
! 		  System.err.println("Need to supply appropriate parameters!");
! 		  System.err.println("For YabaBoost normal, we need r, c1, c2, nc, and theta!");
! 		  System.err.println("For YabaBoost cost sensitive, we need neg_c, pos_c, neg_c1, pos_c1, neg_c2, pos_c2, neg_theta, pos_theta, neg_nc, pos_nc");
! 		  throw new InstantiationException("Need more params for yaba");
  	      }
  	      result = yaba;
  	  }

Index: MixedBinaryPrediction.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/MixedBinaryPrediction.java,v
retrieving revision 1.4
retrieving revision 1.5
diff -C2 -d -r1.4 -r1.5
*** MixedBinaryPrediction.java	13 Dec 2007 07:41:39 -0000	1.4
--- MixedBinaryPrediction.java	24 Jan 2008 22:30:16 -0000	1.5
***************
*** 18,24 ****
--- 18,31 ----
      public MixedBinaryPrediction(double p) 
  	throws NotNormalizedPredException {
+ 	(new Exception("asdf")).printStackTrace();
  	throw new NotNormalizedPredException("MixedBinaryPrediction: Need to normalize based on time!");
      }
  
+     public static double updateMargin(double currentMargin, double stepSize, double step, double dt, double nc) {
+ 	//System.out.println("cm: " + currentMargin + ", deltax: " + stepSize*step + ", dt: " + dt + ", nc: " + nc);
+ 	//System.out.println(Math.exp(-dt*nc)*currentMargin + stepSize*step);
+ 	return Math.exp(-dt*nc)*currentMargin + stepSize*step;
+     }
+ 
      public MixedBinaryPrediction(double p, double dt, double nc) 
  	throws NotNormalizedPredException {
***************
*** 30,33 ****
--- 37,49 ----
      
  
+     public MixedBinaryPrediction(double p, double dt) 
+ 	throws NotNormalizedPredException {
+ 	super();
+ 	deltaT = dt;
+ 	prediction=p;
+ 	normalizingConstant = 1;
+     }
+     
+ 
  
      public Object clone(){
***************
*** 48,52 ****
  	// The prediction p is for h_t
  	double alpha = ((MixedBinaryPrediction) p).prediction;
! 	prediction = Math.exp(-dt) * prediction + alpha;
  	/*
  	if (Math.abs(alpha) > 1 || Math.abs(prediction) > 1) {
--- 64,68 ----
  	// The prediction p is for h_t
  	double alpha = ((MixedBinaryPrediction) p).prediction;
! 	prediction = Math.exp(-normalizingConstant*deltaT) * prediction + alpha;
  	/*
  	if (Math.abs(alpha) > 1 || Math.abs(prediction) > 1) {
***************
*** 70,73 ****
--- 86,97 ----
  	return this;
      }
+ 
+     public double getDeltaT() {
+ 	return deltaT;
+     }
+ 
+     public double getNormConstant() {
+ 	return normalizingConstant;
+     }
      
      public Prediction add(double w, Prediction p) {
***************
*** 79,83 ****
      public boolean equals(Prediction other) {
        MixedBinaryPrediction bp= (MixedBinaryPrediction) other;
!       return (prediction == bp.prediction && dt == bp.dt && normalizingConstant == bp.normalizingConstant); 
      }
      
--- 103,107 ----
      public boolean equals(Prediction other) {
        MixedBinaryPrediction bp= (MixedBinaryPrediction) other;
!       return (prediction == bp.prediction && deltaT == bp.deltaT && normalizingConstant == bp.normalizingConstant); 
      }
      

Index: BrownBoost.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/BrownBoost.java,v
retrieving revision 1.9
retrieving revision 1.10
diff -C2 -d -r1.9 -r1.10
*** BrownBoost.java	13 Dec 2007 07:21:05 -0000	1.9
--- BrownBoost.java	24 Jan 2008 22:30:16 -0000	1.10
***************
*** 72,76 ****
       */
      protected int[] m_examples;
!   
      /**
       * Default constructor just calls AdaBoost to 
--- 72,97 ----
       */
      protected int[] m_examples;
! 
!     /**
!      * A parameter indicating whether we should use confidence rated
!      * predictions.
!      */ 
!     protected static final boolean USE_BETA_RHO = false;
! 
! 
!     /**
!      * A parameter indicating whether we should use confidence rated
!      * predictions.
!      */ 
!     protected static final boolean USE_CONFIDENCE = false;
! 
! 
!     /**
!      * A parameter indicating whether we should use confidence rated
!      * predictions.
!      */ 
!     protected static final boolean ONE_SIDED_PREDICTION = USE_CONFIDENCE;
! 
! 
      /**
       * Default constructor just calls AdaBoost to 
***************
*** 86,90 ****
  	m_examples = new int[m_numExamples];
  	for (int i=0; i<m_numExamples; i++) {
! 	    m_potentials[i] = calculatePotential(0,m_c);
  	    m_examples[i] = i;
  	}
--- 107,117 ----
  	m_examples = new int[m_numExamples];
  	for (int i=0; i<m_numExamples; i++) {
! 	    if (m_isCostSensitive) {
! 		m_potentials[i] = calculatePotential(0,m_c,m_labels[i]);
! 		m_weights[i] = calculateWeight(0,m_c,m_labels[i]);
! 	    } else {
! 		m_potentials[i] = calculatePotential(0,m_c);
! 		m_weights[i] = calculateWeight(0,m_c);
! 	    }
  	    m_examples[i] = i;
  	}
***************
*** 98,105 ****
  	//System.out.println("BrownBoost:\n \t m_c: " + m_c + "\n \t intial potential: " + m_initialPotential);	  
      }
! 
  
      public double getInitialPotential(){
! 	return m_initialPotential;
      }
  
--- 125,160 ----
  	//System.out.println("BrownBoost:\n \t m_c: " + m_c + "\n \t intial potential: " + m_initialPotential);	  
      }
!     /*
!     public double getRuntime() {
! 	if (USE_BETA_RHO) {
! 	    return 1.0;
! 	} else {
! 	    return m_c;
! 	}
!     }
!     */
  
      public double getInitialPotential(){
! 	return calculatePotential(0.0, m_c);
!     }
! 
!     public double getPositivePotential(){
! 	return calculatePotential(0.0, m_posc, POSITIVE_LABEL);
!     }
! 
!     public double getNegativePotential(){
! 	return calculatePotential(0.0, m_negc, NEGATIVE_LABEL);
!     }
! 
!     public double getTimeRemaining() {
! 	if (USE_BETA_RHO) {
! 	    return 1.0 - m_s;
! 	} else {
! 	    return m_c - m_s;
! 	}
!     }
!     
!     public double getTimePlayed() {
! 	return m_s;
      }
  
***************
*** 109,112 ****
--- 164,171 ----
      }
  
+     public boolean isCostSensitive(){
+ 	return m_isCostSensitive;
+     }
+ 
      /**
       * Checked results with Matlab, and correctness is sufficient.
***************
*** 277,285 ****
  
    
!     protected double solve_constraints(double hyp_err, int[] examples)
      {   
  	if( m_s < 0.001){
  	    m_s = -1;
! 	    return 0;
  	}
  	  
--- 336,344 ----
  
    
!     protected double[] solve_constraints(double hyp_err, int[] examples)
      {   
  	if( m_s < 0.001){
  	    m_s = -1;
! 	    return new double[] {0,0};
  	}
  	  
***************
*** 386,394 ****
  	    System.err.println("ERROR: Bad solution for t<0");
  	    m_s = m_oldS;
! 	    return(0.0);
  	} 
  	    
  	//System.out.println("\ns: " + m_s);
! 	return alpha;
      }
  
--- 445,453 ----
  	    System.err.println("ERROR: Bad solution for t<0");
  	    m_s = m_oldS;
! 	    return new double[] {0, 0};
  	} 
  	    
  	//System.out.println("\ns: " + m_s);
! 	return new double[] {alpha, t};
      }
  
***************
*** 452,455 ****
--- 511,518 ----
      }
  
+     public double calculateWeight(double margin, double time_remaining, short label) {
+   	return calculateWeight(margin, m_s);
+     }
+ 
      /**
       * BrownBoost uses (1-erf(-(margin+s)/c))/2  as the potential function
***************
*** 459,462 ****
--- 522,530 ----
      }
  
+     public double calculatePotential(double margin, double time_remaining, short label) {
+   	return calculatePotential(margin, m_s);
+     }
+ 
+ 
      /**
       * BrownBoost uses e^(-(margin+s)^2/c) as the weight calculation
***************
*** 464,468 ****
      public double calculateWeight(double margin, double time_remaining) {
  	double s = time_remaining;
!   	return Math.exp(-1 * Math.pow(margin+s,2)/m_c);
      }
  
--- 532,544 ----
      public double calculateWeight(double margin, double time_remaining) {
  	double s = time_remaining;
! 	double mu = -s;
!   	double weight = Math.exp(-1 * Math.pow(margin+s,2)/m_c);
! 	if (USE_CONFIDENCE) {
! 	    if (margin < mu)
!                 weight = 0;
!             else
!                 weight = 2*weight;
! 	}
! 	return weight;
      }
  
***************
*** 472,476 ****
      public double calculatePotential(double margin, double time_remaining) {
  	double s = time_remaining;
!   	return (1-erf((margin+s)/Math.sqrt(m_c)))/2;
      }
  
--- 548,556 ----
      public double calculatePotential(double margin, double time_remaining) {
  	double s = time_remaining;
! 	double pot = (1-erf((margin+s)/Math.sqrt(m_c)))/2;
!         if (USE_CONFIDENCE) {
!             pot = Math.min(2*pot, 1.0);
!         }
!   	return pot;
      }
  
***************
*** 504,573 ****
  
  
-     /**
-      * BrownBag is identical to BinaryBag, except for the method used to
-      * derive the value of prediction (alpha in the literature).  BrownBag
-      * uses the value of alpha determined by BrownBoost and its variants.
-      * See comments for AdaBoost.BinaryBag.
-      * @author Aaron Arvey
-      */
-     class BrownBag extends AdaBoost.BinaryBag{
- 
- 	protected BrownBag(int[] list) {
- 	    m_w= new double[2];
- 	    reset();
- 	    this.addExampleList(list);
- 	}
- 
- 	/** compute the binary prediction associated with this bag */
- 	public BinaryPrediction calcPrediction(double alpha) {
- 	    BinaryPrediction ret;
- 	    ret = new BinaryPrediction(m_w[1] > m_w[0] ? 1.0 : -1.0 );
- 	    ret.scale(alpha);
- 	    return ret;
- 	}
-       
- 	/** compute the binary prediction associated with this bag */
- 	public BinaryPrediction calcPrediction(double posAlpha, double negAlpha) {
- 	    BinaryPrediction ret;
- 	    if (m_w[1] > m_w[0]) {
- 		ret = new BinaryPrediction(1.0);
- 		ret.scale(posAlpha);
- 	    } else {
- 		ret = new BinaryPrediction(-1.0);
- 		ret.scale(negAlpha);
- 	    } 
- 	    return ret;
- 	}
-       
-       
- 	/** Place holder to ensure that this function is not used in BrownBoost. */
- 	public BinaryPrediction calcPrediction(){
- 	    //System.err.println("Need to have alpha for prediction in BrownBag.calcPrediction()!");
- 	    return new BinaryPrediction(0);
- 	}
-       
-       
- 	/** default constructor */
- 	protected BrownBag() {
- 	    super();
- 	}
-       
-       
- 	/** constructor that copies an existing bag */
- 	protected BrownBag(BrownBag bag) {
- 	    super(bag);
- 	}
-       
-       
- 	/** Output the weights in the bag */
- 	public String toString() {
- 	    String s= "BrownBag.\t w0=" + m_w[0] + "\t w1=" + m_w[1] + "\n";
- 	    return s;
- 	}
-       
- 	
-     } /* End BrownBag */
-     
-     
      
      protected double getHypErr(Bag[] bags, int[][] exampleIndex) {
--- 584,587 ----
***************
*** 583,595 ****
  	boolean[] examplesWithHyp = new boolean[m_margins.length];
  	
  	
  	// Get all examples that have a hypothesis associated with them
  	for (int i= 0; i < exampleIndex.length; i++) {
  	    int[] indexes= exampleIndex[i];
! 	    BinaryPrediction pred = ((BrownBag) bags[i]).calcPrediction(1.0);
  	    total += 1;
  	    for (int j= 0; j < indexes.length; j++) {
  		int example   = indexes[j];
  		examplesWithHyp[example] = true;
  		double step   = getStep(m_labels[example], m_hypPredictions[example]);
  
--- 597,611 ----
  	boolean[] examplesWithHyp = new boolean[m_margins.length];
  	
+ 	int numExamplesWithHyps = 0;
  	
  	// Get all examples that have a hypothesis associated with them
  	for (int i= 0; i < exampleIndex.length; i++) {
  	    int[] indexes= exampleIndex[i];
! 	    //BinaryPrediction pred = ((BrownBag) bags[i]).calcPrediction(1.0);
  	    total += 1;
  	    for (int j= 0; j < indexes.length; j++) {
  		int example   = indexes[j];
  		examplesWithHyp[example] = true;
+ 		numExamplesWithHyps += 1;
  		double step   = getStep(m_labels[example], m_hypPredictions[example]);
  
***************
*** 611,615 ****
  	for (int i=0; i < m_margins.length; i++) {
  	    if(!examplesWithHyp[i]){
! 		int example   = i;
  		m_hypPredictions[example] = 0;
  		double weight = m_weights[example];
--- 627,631 ----
  	for (int i=0; i < m_margins.length; i++) {
  	    if(!examplesWithHyp[i]){
! 		int example = i;
  		m_hypPredictions[example] = 0;
  		double weight = m_weights[example];
***************
*** 619,622 ****
--- 635,639 ----
  	    potential += calculatePotential(m_margins[i]);
  	}
+ 	System.out.println("Num Examples with predictions: " + numExamplesWithHyps);
  	
  	//updatePotential(exampleIndex);
***************
*** 669,680 ****
  	
  	
  	// Create a prediction array to accompany the exampleIndex array
  	m_hypPredictions = new double[m_margins.length];
  	for (int i=0; i < exampleIndex.length; i++){
  	    int[] index = exampleIndex[i];
  	    BrownBag b = (BrownBag)bags[i];
  	    for (int j=0; j < index.length; j++){
  		int example = index[j];
! 		m_hypPredictions[example] = b.calcPrediction(1.0).getClassScores()[0];
  	    }
  	}
--- 686,714 ----
  	
  	
+ 	double[] totalWeights = new double[bags.length];
+ 	int maxWeightIndex = 0;
+ 	double maxWeight = 0;
+ 	for (int i=0; i < bags.length; i++) {
+ 	    double[] weights = bags[i].getWeights();
+ 	    for (int j=0; j < weights.length; j++) {
+ 		totalWeights[i] += weights[j];
+ 		if (totalWeights[i] > maxWeight) {
+ 		    maxWeight = totalWeights[i];
+ 		    maxWeightIndex = i;
+ 		}
+ 	    }
+ 	}
+ 	
+ 
  	// Create a prediction array to accompany the exampleIndex array
  	m_hypPredictions = new double[m_margins.length];
  	for (int i=0; i < exampleIndex.length; i++){
+ 	    if (ONE_SIDED_PREDICTION && i!= maxWeightIndex)
+ 		continue;
  	    int[] index = exampleIndex[i];
  	    BrownBag b = (BrownBag)bags[i];
  	    for (int j=0; j < index.length; j++){
  		int example = index[j];
! 		m_hypPredictions[example] = b.calcPrediction(1.0,0.0).getClassScores()[0];
  	    }
  	}
***************
*** 685,704 ****
  	double gamma = getHypErr(bags, exampleIndex);
  
! 	if (m_isCostSensitive) {
  	    System.out.println("Solving positive example constraints");
! 	    double posAlpha = solve_constraints(gamma, m_posExamples);
  	    System.out.println("Solving negative example constraints");
! 	    double negAlpha = solve_constraints(gamma, m_negExamples);
  	    for (int i= 0; i < bags.length; i++) {
! 		p[i]= ((BrownBag) bags[i]).calcPrediction(posAlpha, negAlpha);
  		System.out.println("p[" + i + "] = " + p[i]);
  	    }
  	} else {
! 	    double alpha = solve_constraints(gamma, m_examples);
  	    for (int i= 0; i < bags.length; i++) {
! 		p[i]= ((BrownBag) bags[i]).calcPrediction(alpha);
! 		System.out.println("p[" + i + "] = " + p[i]);
  	    }
  	}
  	return p;
      }
--- 719,770 ----
  	double gamma = getHypErr(bags, exampleIndex);
  
! 	if (false) { //m_isCostSensitive) {
  	    System.out.println("Solving positive example constraints");
! 	    double[] sols = solve_constraints(gamma, m_posExamples);
! 	    double posAlpha = sols[0];
! 	    double posDeltaT = sols[1];
! 	    System.out.println("Positive alpha, dt: " + posAlpha + "," + posDeltaT);
  	    System.out.println("Solving negative example constraints");
! 	    sols = solve_constraints(gamma, m_negExamples);
! 	    double negAlpha = sols[0];
! 	    double negDeltaT = sols[1];
! 	    System.out.println("Negative alpha, dt: " + negAlpha + "," + negDeltaT);
  	    for (int i= 0; i < bags.length; i++) {
! 		p[i]= ((BrownBag) bags[i]).calcPrediction(posAlpha, posDeltaT, negAlpha, negDeltaT);
  		System.out.println("p[" + i + "] = " + p[i]);
  	    }
  	} else {
! 	    double[] sols = solve_constraints(gamma, m_examples);
! 	    double alpha = sols[0];
! 	    double dt = sols[1];
! 	    if (bags.length > 1) {
! 		BinaryBag [] bbags = new BinaryBag[2];
! 		bbags[0] = (BinaryBag)bags[0];
! 		bbags[1] = (BinaryBag)bags[1];
! 		for (int i= 0; i < bags.length; i++) {
! 		    if ( (bbags[0].getWeights()[0] > bbags[0].getWeights()[1] &&  bbags[1].getWeights()[0] > bbags[1].getWeights()[1])
! 			 ||
! 			 (bbags[0].getWeights()[0] < bbags[0].getWeights()[1] &&  bbags[1].getWeights()[0] < bbags[1].getWeights()[1])
! 			 || 
! 			 true
! 			 )
! 			System.out.print("Bag i: " + bags[i]);
! 		}
! 	    }
! 
  	    for (int i= 0; i < bags.length; i++) {
! 		if (i!=maxWeightIndex && ONE_SIDED_PREDICTION) {
! 		    p[i]= ((BrownBag) bags[i]).calcPrediction(0,dt);		    
! 		} else {
! 		    p[i]= ((BrownBag) bags[i]).calcPrediction(alpha,dt);
! 		}
! 		//System.out.print("Bag i: " + bags[i]);
! 		//System.out.println("p[" + i + "] = " + p[i]);
  	    }
  	}
+ 
+ 	for (int i=0; i<totalWeights.length; i++) 
+ 	    System.out.println("Totalweights[" + i + "]=" + totalWeights[i]);
+ 
  	return p;
      }
***************
*** 725,727 ****
--- 791,870 ----
      }
  
+ 
+ 
+ 
+     /**
+      * BrownBag is identical to BinaryBag, except for the method used to
+      * derive the value of prediction (alpha in the literature).  BrownBag
+      * uses the value of alpha determined by BrownBoost and its variants.
+      * See comments for AdaBoost.BinaryBag.
+      * @author Aaron Arvey
+      */
+     class BrownBag extends AdaBoost.BinaryBag{
+ 
+ 	protected BrownBag(int[] list) {
+ 	    m_w= new double[2];
+ 	    reset();
+ 	    this.addExampleList(list);
+ 	}
+ 
+ 	/** compute the binary prediction associated with this bag */
+ 	public BinaryPrediction calcPrediction(double alpha) {
+ 	    BinaryPrediction ret;
+ 	    ret = new BinaryPrediction(m_w[1] > m_w[0] ? 1.0 : -1.0 );
+ 	    ret.scale(alpha);
+ 	    return ret;
+ 	}
+       
+ 	/** compute the binary prediction associated with this bag */
+ 	public BinaryPrediction calcPrediction(double alpha, double dt) {
+ 	    //if (this instanceof BrownBag)
+ 		return calcPrediction(alpha);
+ 		//if (this instanceof BrownBag)
+ 		//return ((YabaBag)this).calcPrediction(alpha,dt);
+ 	}
+       
+ 	/** compute the binary prediction associated with this bag */
+ 	public BinaryPrediction calcPrediction(double posAlpha, double posDeltaT, double negAlpha, double negDeltaT) {
+ 	    BinaryPrediction ret;
+ 	    if (m_w[1] > m_w[0]) {
+ 		ret = new BinaryPrediction(1.0);
+ 		ret.scale(posAlpha);
+ 	    } else {
+ 		ret = new BinaryPrediction(-1.0);
+ 		ret.scale(negAlpha);
+ 	    } 
+ 	    return ret;
+ 	}
+       
+       
+ 	/** Place holder to ensure that this function is not used in BrownBoost. */
+ 	public BinaryPrediction calcPrediction(){
+ 	    //System.err.println("Need to have alpha for prediction in BrownBag.calcPrediction()!");
+ 	    return new BinaryPrediction(0);
+ 	}
+       
+       
+ 	/** default constructor */
+ 	protected BrownBag() {
+ 	    super();
+ 	}
+ 
+       
+ 	/** constructor that copies an existing bag */
+ 	protected BrownBag(BrownBag bag) {
+ 	    super(bag);
+ 	}
+       
+       
+ 	/** Output the weights in the bag */
+ 	public String toString() {
+ 	    String s= "BrownBag.\t w0=" + m_w[0] + "\t w1=" + m_w[1] + "\n";
+ 	    return s;
+ 	}
+       
+ 	
+     } /* End BrownBag */
+ 
+ 
  } /* End BrownBoost Class */

Index: YabaBoost.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/YabaBoost.java,v
retrieving revision 1.6
retrieving revision 1.7
diff -C2 -d -r1.6 -r1.7
*** YabaBoost.java	23 Oct 2007 22:45:40 -0000	1.6
--- YabaBoost.java	24 Jan 2008 22:30:16 -0000	1.7
***************
*** 1,5 ****
  package jboost.booster;
  
- 
  import java.io.BufferedWriter;
  import java.io.FileWriter;
--- 1,4 ----
***************
*** 72,75 ****
--- 71,81 ----
  
      /**
+      * The normalization constant for updating the score (might be alpha in Freund & Arvey 2008).
+      */
+     protected double m_nc = 1.0;
+     protected double m_posNc = 1.0;
+     protected double m_negNc = 1.0;
+ 
+     /**
       * A parameter indicating when we should stop the game.
       * Technically, this should be 0; however due to numerical instability
***************
*** 77,89 ****
       * an approximation of the 0/1 loss instead of the exact 0/1 loss.
       */	
!     protected static final double FINISH_GAME_NOW = 0.05; 
! 
! 
!     /**
!      * A parameter indicating whether we should use confidence rated
!      * predictions.
!      */	
!     protected static final boolean USE_CONFIDENCE = false; 
! 
  
  
--- 83,87 ----
       * an approximation of the 0/1 loss instead of the exact 0/1 loss.
       */	
!     protected static final double FINISH_GAME_NOW = 0.02; 
  
  
***************
*** 95,102 ****
       */	
      protected static final double THETA_UPDATE_STEP = 0.02; 
      
      /**
!      * Default constructor just calls AdaBoost to 
!      * get everything initialized
       */
      public YabaBoost() {
--- 93,101 ----
       */	
      protected static final double THETA_UPDATE_STEP = 0.02; 
+ 
      
      /**
!      * Default constructor just calls AdaBoost to get everything
!      * initialized
       */
      public YabaBoost() {
***************
*** 111,126 ****
          m_c = runtime;
          m_s = m_c;
!         m_initialPotential = calculatePotential(0,m_c);
      }
      
!     public void setParams(double c1, double c2, double theta) throws Exception {
  	double EPS = 0.01;
  	if  (c1 < EPS || c2 < EPS || theta < EPS || m_c < EPS) {
! 	    throw new Exception("Yaba params are bad!\nc1:"+c1+"\nc2:"
! 				+c2+"\ntheta:"+theta+"\n");
  	}
  	m_c1 = c1;
  	m_c2 = c2;
  	m_theta = theta;
  	m_origTheta = theta;
          m_initialPotential = calculatePotential(0,m_c);
--- 110,127 ----
          m_c = runtime;
          m_s = m_c;
!         m_initialPotential = calculatePotential(0,m_c,NO_LABEL);
      }
      
!     public void setParams(double c1, double c2, double theta, double nc) throws Exception {
  	double EPS = 0.01;
  	if  (c1 < EPS || c2 < EPS || theta < EPS || m_c < EPS) {
! 	    /*throw new Exception("Yaba params are bad!\nc1:"+c1+"\nc2:"
! 	      +c2+"\ntheta:"+theta+"\n");*/
! 	    System.err.println("Bad yaba params!");
  	}
  	m_c1 = c1;
  	m_c2 = c2;
  	m_theta = theta;
+ 	m_nc = nc;
  	m_origTheta = theta;
          m_initialPotential = calculatePotential(0,m_c);
***************
*** 128,164 ****
  
  
!     public void setCostSensitiveParams(double pc, double pc1, double pc2, double ptheta, 
! 				       double nc, double nc1, double nc2, double ntheta) {
  	m_posc = pc;
  	m_posc1 = pc1;
  	m_posc2 = pc2;
  	m_posTheta = ptheta;
  	m_posOrigTheta = ptheta;
  
! 	m_negc = pc;
! 	m_negc1 = pc1;
! 	m_negc2 = pc2;
! 	m_negTheta = ptheta;
! 	m_negOrigTheta = ptheta;
      }
      
      
-     public String surfingData() {
-         StringBuffer ret = new StringBuffer("");
-         ret.append(String.format("YabaBoost Params: %.4f %.4f %4f %4f %4f\n", m_c, m_s, m_c1, m_c2, m_theta));
-         for (int i=0; i<m_margins.length; i++){
-             ret.append(String.format("%.4f\t%.4f\t%.4f\n", m_margins[i], m_weights[i], m_potentials[i]));
-         }
-         return ret.toString();
-     }
- 
- 
  
      public void finalizeData() {
  	double EPS = 0.01;
! 	if  (m_c1 < EPS || m_c2 < EPS || m_theta < EPS || m_c < EPS) {
  	    System.err.println("Yaba params are bad!\nc:"+m_c+"\nc1:"
  			       +m_c1+"\nc2:"+m_c2+"\ntheta:"+m_theta+"\n");
! 	    System.exit(2);
  	}
          m_initialPotential = calculatePotential(0,m_c);
--- 129,169 ----
  
  
!     public void setCostSensitiveParams(double pc, double pc1, double pc2, double ptheta, double pnc,
! 				       double nc, double nc1, double nc2, double ntheta, double nnc) {
  	m_posc = pc;
  	m_posc1 = pc1;
  	m_posc2 = pc2;
+ 	m_posNc = pnc;
  	m_posTheta = ptheta;
  	m_posOrigTheta = ptheta;
+ 	m_posInitialPotential = calculatePotential(0.0,m_posc,POSITIVE_LABEL);
+ 	System.out.println("pc, pc1, pc2, pnc, ptheta,  pos pot: " + pc + "," + pc1 
+ 			   + "," + pc2 + "," + ptheta + "," + pnc + "," +  + m_posInitialPotential);
  
! 	m_negc = nc;
! 	m_negc1 = nc1;
! 	m_negc2 = nc2;
! 	m_negNc = nnc;
! 	m_negTheta = ntheta;
! 	m_negOrigTheta = ntheta;
! 	m_negInitialPotential = calculatePotential(0.0,m_negc,NEGATIVE_LABEL);
! 	
! 	m_isCostSensitive = true;
      }
      
      
  
      public void finalizeData() {
  	double EPS = 0.01;
! 	if  (!m_isCostSensitive && (m_c1 < EPS || m_c2 < EPS || m_theta < EPS || m_c < EPS || m_nc < EPS)) {
  	    System.err.println("Yaba params are bad!\nc:"+m_c+"\nc1:"
  			       +m_c1+"\nc2:"+m_c2+"\ntheta:"+m_theta+"\n");
! 	    //System.exit(2);
! 	} else if  (m_isCostSensitive && (m_posc1 < EPS || m_posc2 < EPS || m_posTheta < EPS || m_posc < EPS || m_posNc < EPS ||
! 					  m_negc1 < EPS || m_negc2 < EPS || m_negTheta < EPS || m_negc < EPS || m_negNc < EPS)) {
! 	    System.err.println("Yaba cost sensitive params are bad!\nc:"+m_c+"\nc1:"
! 			       +m_c1+"\nc2:"+m_c2+"\ntheta:"+m_theta+"\n");
! 	    
! 	    //System.exit(2);
  	}
          m_initialPotential = calculatePotential(0,m_c);
***************
*** 168,172 ****
  	    + "\t m_c2: " + m_c2 + "\n"
  	    + "\t m_theta: " + m_theta + "\n"
! 	    + "\t initial potential: " + m_initialPotential
  	    + "\n";     
          System.out.println(yabaout);
--- 173,191 ----
  	    + "\t m_c2: " + m_c2 + "\n"
  	    + "\t m_theta: " + m_theta + "\n"
! 	    + "\t m_nc: " + m_nc + "\n"
! 	    + "\t m_initialPotential: " + m_initialPotential + "\n"
! 	    + "\t m_posc: " + m_posc + "\n"
! 	    + "\t m_posc1: " + m_posc1 + "\n"
! 	    + "\t m_posc2: " + m_posc2 + "\n"
! 	    + "\t m_posTheta: " + m_posTheta + "\n"
! 	    + "\t m_posNc: " + m_posNc + "\n"
! 	    + "\t m_posInitialPotential: " + m_posInitialPotential  + "\n"
! 	    + "\t m_negc: " + m_negc + "\n"
! 	    + "\t m_negc1: " + m_negc1 + "\n"
! 	    + "\t m_negc2: " + m_negc2 + "\n"
! 	    + "\t m_negTheta: " + m_negTheta + "\n"
! 	    + "\t m_negNc: " + m_negNc + "\n"
! 	    + "\t m_negInitialPotential: " + m_negInitialPotential + "\n"
! 	    + "\t m_isCostSensitive: " + m_isCostSensitive  + "\n"
  	    + "\n";     
          System.out.println(yabaout);
***************
*** 205,218 ****
  	    step  = getStep(m_labels[example], m_hypPredictions[example]);
  	    
! 	    EPS = 0.0001;
  	    if (Math.abs(step) > EPS) {
! 		new_margin = (1-alpha)*margin + alpha*step;
  	    } else { 
  		new_margin = margin;
  	    }
  	    
! 	    new_weight = calculateWeight(new_margin, new_time_remaining);
! 	    new_pot = calculatePotential(new_margin, new_time_remaining);
! 	    orig_pot  = calculatePotential(orig_margin, orig_time_remaining);
  	    
  	    vars.B += new_weight*step;
--- 224,238 ----
  	    step  = getStep(m_labels[example], m_hypPredictions[example]);
  	    
! 	    EPS = 0.000001;
  	    if (Math.abs(step) > EPS) {
! 		new_margin = MixedBinaryPrediction.updateMargin(margin, alpha, step, t, m_nc);
  	    } else { 
  		new_margin = margin;
  	    }
  	    
! 	    new_weight = calculateWeight(new_margin, new_time_remaining, m_labels[example]);
! 	    new_pot = calculatePotential(new_margin, new_time_remaining, m_labels[example]);
! 	    orig_pot  = calculatePotential(orig_margin, orig_time_remaining,  m_labels[example]);		
! 	    //System.out.println("New weight is: " + new_weight);
  	    
  	    vars.B += new_weight*step;
***************
*** 278,282 ****
       * @return alpha - An appropriate value of alpha that satisfies constraints.
       */
!     protected double solve_constraints(double hyp_err, int[] examples) {
  	/*
  	 * If the game has a small amount of time remaining, quit now.
--- 298,303 ----
       * @return alpha - An appropriate value of alpha that satisfies constraints.
       */
!     protected double[] solve_constraints(double hyp_err, int[] examples) {
! 
  	/*
  	 * If the game has a small amount of time remaining, quit now.
***************
*** 286,290 ****
  	if( m_s < FINISH_GAME_NOW){
  	    m_s = -1;
! 	    return 0;
  	}
  	  
--- 307,311 ----
  	if( m_s < FINISH_GAME_NOW){
  	    m_s = -1;
! 	    return new double[] {0,0};
  	}
  	  
***************
*** 308,311 ****
--- 329,333 ----
  	double t=0.3;
  	double alpha=0.1;
+ 
  	  	  
  	/*
***************
*** 370,377 ****
  		// Increment alpha
  		alpha += alpha_step;
- 		//System.out.print("   alpha: " + alpha);
  		
  		// Deal with boundary cases, keep margin normalized
! 		if(alpha < 0 || alpha > 1) {
  		    alpha = Math.round(alpha);
  		    alpha_step = -alpha_step;
--- 392,398 ----
  		// Increment alpha
  		alpha += alpha_step;
  		
  		// Deal with boundary cases, keep margin normalized
! 		if(alpha < -0.1 || alpha > 1) {
  		    alpha = Math.round(alpha);
  		    alpha_step = -alpha_step;
***************
*** 464,470 ****
  
  	if (t < 0) {
- 	    dump_everything(vars);
  	    System.err.println("The change in time is negtive!");
  	    System.err.println("Outputting all data to outfile!");
  	    System.exit(2);
  	}
--- 485,491 ----
  
  	if (t < 0) {
  	    System.err.println("The change in time is negtive!");
  	    System.err.println("Outputting all data to outfile!");
+ 	    dump_everything(vars);
  	    System.exit(2);
  	}
***************
*** 480,484 ****
  
  	m_lastAlpha = alpha;
! 	return alpha;
      }
  
--- 501,505 ----
  
  	m_lastAlpha = alpha;
! 	return  new double[] {alpha, t};
      }
  
***************
*** 503,514 ****
  	    double[] value = new double[] { -p, p };
  	    int[] indexes = exampleIndex[i];
  	    for (int j= 0; j < indexes.length; j++) {
  		int example   = indexes[j];
- 		double alpha = Math.abs(p);
  
  		// Set margins to new margin
  		// value[m_labels[example]] is equal to alpha*step
  		double EPS = 0.0001;
! 		if (Math.abs(p) - m_lastAlpha  > EPS) {
  		    System.err.println("Prediction was messed up!");
  		    System.err.println("alpha was: " + m_lastAlpha);
--- 524,537 ----
  	    double[] value = new double[] { -p, p };
  	    int[] indexes = exampleIndex[i];
+ 	    double alpha = Math.abs(p);
+ 	    double dt = ((MixedBinaryPrediction)predictions[i]).getDeltaT();
+ 	    double step;
  	    for (int j= 0; j < indexes.length; j++) {
  		int example   = indexes[j];
  
  		// Set margins to new margin
  		// value[m_labels[example]] is equal to alpha*step
  		double EPS = 0.0001;
! 		if (Math.abs(Math.abs(p) - m_lastAlpha)  > EPS) {
  		    System.err.println("Prediction was messed up!");
  		    System.err.println("alpha was: " + m_lastAlpha);
***************
*** 516,531 ****
  		}
  		m_oldMargins[example] = m_margins[example];
! 		m_margins[example] = (1-alpha)*m_margins[example] 
! 		    + value[m_labels[example]];
! 
! 		if(Math.abs(m_margins[example]) > 1){
! 		    System.out.println("m_margins[" +example+ "]: " 
! 				       + m_margins[example] + " greater than 1!!!!");
! 		}
  	    }
  	}
  
! 
! 	if (m_hypPredictions.length != m_margins.length || m_hypPredictions.length != m_numExamples) {
  	    System.err.println("WARNING: m_hypPredictions is not the same length as the margins");
  	}
--- 539,549 ----
  		}
  		m_oldMargins[example] = m_margins[example];
! 		step = (value[m_labels[example]] >= 0) ? 1.0 : -1.0;
! 		m_margins[example] =  MixedBinaryPrediction.updateMargin(m_margins[example], Math.abs(value[m_labels[example]]), step, dt, m_nc);
  	    }
  	}
  
! 	
! 	if (m_hypPredictions == null || m_hypPredictions.length != m_margins.length || m_hypPredictions.length != m_numExamples) {
  	    System.err.println("WARNING: m_hypPredictions is not the same length as the margins");
  	}
***************
*** 556,559 ****
--- 574,578 ----
       */
      public double calculatePotential(double margin) {
+ 	//System.out.println("potential being called");
  	return calculatePotential(margin, m_s);
      }
***************
*** 564,581 ****
       * @return weight of any example with the given margin
       */
!     public double calculateWeight(double margin, double time_remaining) {
! 	double s  = m_c - time_remaining;
! 	double mu = m_theta - m_c1* ( Math.exp(-s) - Math.exp(-m_c) );
! 	double sd = Math.sqrt(m_c2*(Math.exp(-2*s) - Math.exp(-2*m_c))) + FINISH_GAME_NOW;
! 	double norm = 1 / Math.sqrt(Math.PI * sd * sd); 
! 	double ret = norm * Math.exp(- Math.pow((margin-mu)/sd,2));
  
! 	if (USE_CONFIDENCE) {
! 	    if (margin < mu) 
! 		ret = 0;
! 	    else 
! 		ret = 2*ret;
  	}
  
    	return ret;
      }
--- 583,646 ----
       * @return weight of any example with the given margin
       */
!     public double calculateWeight(double margin, double time_remaining){
! 	return calculateWeight(margin, time_remaining, (short)NO_LABEL);
!     }
  
!     /**
!      * @param margin
!      * @param time time remaining in game
!      * @param label the label of the example (for asymmetric weighting)
!      * @return weight of any example with the given margin
!      */
!     public double calculateWeight(double margin, double time_remaining, short label) {
! 	double ret;
! 	if (label==POSITIVE_LABEL && m_isCostSensitive) {
! 	    ret = calculateWeight(margin, time_remaining, m_c, m_posc1, m_posc2, m_posTheta, m_posNc);
! 	} else if (label==NEGATIVE_LABEL && m_isCostSensitive) {
! 	    ret = calculateWeight(margin, time_remaining, m_c, m_negc1, m_negc2, m_negTheta, m_negNc);
! 	} else {
! 	    ret = calculateWeight(margin, time_remaining, m_c, m_c1, m_c2, m_theta, m_nc);
! 	}
! 	return ret;
!     }
! 
! 
! 
!     /**
!      * @param margin
!      * @param time time remaining in game
!      * @param c total time of game
!      * @param c1 scale of mean
!      * @param c2 scale of variance
!      * @param theta mean of the final function
!      * @param nc normalization constant
!      * @return weight of any example with the given margin
!      */
!     public double calculateWeight(double margin, double time_remaining, 
! 				  double c, double c1, double c2,
! 				  double theta, double nc) {
! 	double s=0, mu=0, sd=0, norm=0, ret=0;
! 	s  = c - time_remaining;
! 	mu = theta - c1* ( Math.exp(-s) - Math.exp(-c) );
! 	sd = Math.sqrt(c2*(Math.exp(-2*s) - Math.exp(-2*c))) + FINISH_GAME_NOW;
! 
! 	/**
! 	 * To translate between notations (Freund & Arvey -> code
! 	 * here): rho is c1, theta is theta, alpha is nc.
! 	 */
! 	if (USE_BETA_RHO) {
! 	    double rho = c1; 
! 	    double alpha = nc;
! 	    double t = s;
! 	    mu = rho + Math.exp(-alpha*(1-t))*(theta - rho);
! 	    sd = Math.sqrt( (Math.exp(2*alpha*(1-t)) - 1) / (2*alpha) );
  	}
  
+ 	norm = 1 / Math.sqrt(Math.PI * sd * sd); 
+ 	ret = norm * Math.exp(- Math.pow((margin-mu)/sd,2));
+ 	if (USE_CONFIDENCE) {
+ 	    if (margin < mu) 	ret = 0;
+ 	    else                ret = 2*ret;
+ 	}
    	return ret;
      }
***************
*** 584,594 ****
      /**
       * @param margin
       * @return potential of example with given margin
       */
      public double calculatePotential(double margin, double time_remaining) {
! 	double s  = m_c - time_remaining;
! 	double mu = m_theta - m_c1 * (Math.exp(-s) - Math.exp(-m_c));
! 	double sd = Math.sqrt(m_c2 * (Math.exp(-2*s) - Math.exp(-2*m_c))) + FINISH_GAME_NOW;
! 	double pot = (1 - erf((margin-mu)/sd)) / 2;
  	if (USE_CONFIDENCE) {
  	    pot = Math.min(2*pot, 1.0);
--- 649,717 ----
      /**
       * @param margin
+      * @param time_remaining
       * @return potential of example with given margin
       */
      public double calculatePotential(double margin, double time_remaining) {
! 	return calculatePotential(margin, time_remaining, NO_LABEL);
!     }
! 
!     /**
!      * @param margin
!      * @param time_remaining
!      * @param label 
!      * @return potential of example with given margin
!      */
!     public double calculatePotential(double margin, double time_remaining, short label) {
! 	double ret = 0;
! 	if (label==POSITIVE_LABEL && m_isCostSensitive) {
! 	    ret = calculatePotential(margin, time_remaining, m_c, m_posc1, m_posc2, m_posTheta, m_posNc);
! 	} else if (label==NEGATIVE_LABEL && m_isCostSensitive) {
! 	    ret = calculatePotential(margin, time_remaining, m_c, m_negc1, m_negc2, m_negTheta, m_negNc);
! 	} else {
! 	    ret = calculatePotential(margin, time_remaining, m_c, m_c1, m_c2, m_theta, m_nc);
! 	}
! 	return ret;
!     }
! 
!     /**
!      * @param margin
!      * @return potential of example with given margin
!      */
!     double calculatePotential(double margin, double time_remaining, double c, double c1, double c2, double theta, double nc) {
! 	double s, mu, sd, pot;
! 	s  = c - time_remaining;
! 	mu = theta - c1 * (Math.exp(-s) - Math.exp(-c));
! 	sd = Math.sqrt(c2 * (Math.exp(-2*s) - Math.exp(-2*c))) + FINISH_GAME_NOW;
! 
! 	/**
! 	 * To translate between notations (Freund & Arvey -> code
! 	 * here): rho is c1, theta is theta, alpha is nc.
! 	 */
! 	if (USE_BETA_RHO) {
! 	    double rho = c1; 
! 	    double alpha = nc;
! 	    double t = s;
! 	    mu = rho + Math.exp(-alpha*(1-t))*(theta - rho);
! 	    sd = Math.sqrt( (Math.exp(2*alpha*(1-t)) - 1) / (2*alpha) );
! 	    /*
! 	    System.out.println("Using beta, rho potential");	    
! 	    System.out.println("Using beta, rho potentialb");
! 	    System.out.println("Using beta, rho potential");	    
! 	    System.out.println("Using beta, rho potentialb");
! 	    System.out.println("Using beta, rho potential");	    
! 	    System.out.println("Using beta, rho potentialb");
! 	    System.out.println("Using beta, rho potential");	    
! 	    System.out.println("Using beta, rho potentialb");
! 	    System.out.println("Using beta, rho potential");	    
! 	    System.out.println("Using beta, rho potentialb");
! 	    System.out.println("Using beta, rho potential");	    
! 	    System.out.println("Using beta, rho potentialb");
! 	    System.out.println("Using beta, rho potential");	    
! 	    System.out.println("Using beta, rho potentialb");
! 	    */
! 	}
! 
! 	pot = (1 - erf((margin-mu)/sd)) / 2;
! 
  	if (USE_CONFIDENCE) {
  	    pot = Math.min(2*pot, 1.0);
***************
*** 612,616 ****
  
      public String getParamString() {
!         String ret = String.format("YabaBoost s=%.4f c=%.4f c1=%.4f c2=%.4f theta=%.4f confidence=%b ", m_s, m_c, m_c1, m_c2, m_theta, USE_CONFIDENCE);
          return ret;
      }
--- 735,747 ----
  
      public String getParamString() {
!         String ret = String.format("YabaBoost s=%.4f c=%.4f c1=%.4f c2=%.4f theta=%.4f nc=%.4f " + 
! 				   "poss=%.4f negs=%.4f posc=%.4f negc=%.4f posc1=%.4f " + 
! 				   "negc1=%.4f posc2=%.4f negc2=%.4f posnc=%.4f negnc=%.4f " + 
! 				   "postheta=%.4f negtheta=%.4f confidence=%b " + 
! 				   "asymmetric=%b onesided=%b betarho=%b", 
! 				   m_s, m_c, m_c1, m_c2, m_theta, m_nc,
! 				   -5.0, -5.0, m_posc, m_negc, m_posc1, m_negc1, m_posc2, m_negc2,
! 				   m_posNc, m_negNc, m_posTheta, m_negTheta,
! 				   USE_CONFIDENCE, m_isCostSensitive, ONE_SIDED_PREDICTION, USE_BETA_RHO);
          return ret;
      }
***************
*** 635,640 ****
  	public MixedBinaryPrediction calcPrediction(double alpha) {
  	    MixedBinaryPrediction ret = new MixedBinaryPrediction(0);
  	    try {
! 		ret = new MixedBinaryPrediction(m_w[1] > m_w[0] ? 1.0 : -1.0 );
  	    } catch (NotNormalizedPredException e){
  		System.err.println(e.getStackTrace());
--- 766,777 ----
  	public MixedBinaryPrediction calcPrediction(double alpha) {
  	    MixedBinaryPrediction ret = new MixedBinaryPrediction(0);
+ 	    return ret;
+ 	}
+       
+ 	/** compute the binary prediction associated with this bag */
+ 	public MixedBinaryPrediction calcPrediction(double alpha, double dt) {
+ 	    MixedBinaryPrediction ret = new MixedBinaryPrediction(0,0);
  	    try {
! 		ret = new MixedBinaryPrediction(m_w[1] > m_w[0] ? 1.0 : -1.0, dt);
  	    } catch (NotNormalizedPredException e){
  		System.err.println(e.getStackTrace());
***************
*** 695,699 ****
  
      protected MixedBinaryPrediction getZeroPred() {
! 	return new MixedBinaryPrediction(0);
      }
  
--- 832,836 ----
  
      protected MixedBinaryPrediction getZeroPred() {
! 	return new MixedBinaryPrediction(0,0);
      }
  

Index: AdaBoost.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/AdaBoost.java,v
retrieving revision 1.4
retrieving revision 1.5
diff -C2 -d -r1.4 -r1.5
*** AdaBoost.java	23 Oct 2007 22:45:40 -0000	1.4
--- AdaBoost.java	24 Jan 2008 22:30:16 -0000	1.5
***************
*** 43,46 ****
--- 43,53 ----
    protected int m_numNegExamples;
  
+   /** Constant for positive label for cost sensitive stuff */
+   protected final short POSITIVE_LABEL = 0;
+   /** Constant for negative label for cost sensitive stuff */
+   protected final short NEGATIVE_LABEL = 1;
+   /** Constant for non cost sensitive stuff */
+   protected final short NO_LABEL = -99;
+ 
    /** sampling weights for the examples */
    protected double[] m_sampleWeights;
***************
*** 101,105 ****
          m_numExamples++;
          m_tmpList.add(new TmpData(index, (short) l, weight));
!         if (l==1) m_numPosExamples++;
        } else {
          failed= "AdaBoost.addExample received index " + index + ", when it expected index " +
--- 108,112 ----
          m_numExamples++;
          m_tmpList.add(new TmpData(index, (short) l, weight));
!         if (l==POSITIVE_LABEL) m_numPosExamples++;
        } else {
          failed= "AdaBoost.addExample received index " + index + ", when it expected index " +
***************
*** 160,167 ****
        m_weights[index]= m_oldWeights[index]= defaultWeight;
        m_labels[index]= a.getLabel();
!       if (a.getLabel()==1)  
          m_posExamples[m_posIndex++] = index; 
!       else
          m_negExamples[m_negIndex++] = index;  
        m_sampleWeights[index]= a.getWeight();
      }
--- 167,179 ----
        m_weights[index]= m_oldWeights[index]= defaultWeight;
        m_labels[index]= a.getLabel();
!       if (a.getLabel()==POSITIVE_LABEL)  
          m_posExamples[m_posIndex++] = index; 
!       else if (a.getLabel()==NEGATIVE_LABEL)
          m_negExamples[m_negIndex++] = index;  
+       else {
+         System.err.println("Label of example is unknown to adaboost");
+         System.exit(2);
+       }
+       
        m_sampleWeights[index]= a.getWeight();
      }
***************
*** 311,314 ****
--- 323,340 ----
     */
    public Prediction[] getPredictions(Bag[] bags, int[][] exampleIndex) {
+     // Code to see how often the splitting partition predicts the same way on both sides
+     /*
+     if (bags.length > 1) {
+       BinaryBag [] bbags = new BinaryBag[2];
+       bbags[0] = (BinaryBag)bags[0];
+       bbags[1] = (BinaryBag)bags[1];
+       for (int i= 0; i < bags.length; i++) {
+         if ( (bbags[0].getWeights()[0] > bbags[0].getWeights()[1] &&  bbags[1].getWeights()[0] > bbags[1].getWeights()[1])
+              ||
+              (bbags[0].getWeights()[0] < bbags[0].getWeights()[1] &&  bbags[1].getWeights()[0] < bbags[1].getWeights()[1])
+              )
+           System.out.print("Bag i: " + bags[i]);
+       }
+     }*/
      return getPredictions(bags);
    }
***************
*** 408,414 ****
    class BinaryBag extends Bag {
  
-     /** total weight for examples of each label */
-     protected double[] m_w;
- 
      /** default constructor */
      protected BinaryBag() {
--- 434,437 ----

[Jboost-cvs] jboost/scripts surfing.py,1.4,1.5

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/scripts
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv30878

Modified Files:
	surfing.py 
Log Message:
plot the symetric cost surfing graph

Index: surfing.py
===================================================================
RCS file: /cvsroot/jboost/jboost/scripts/surfing.py,v
retrieving revision 1.4
retrieving revision 1.5
diff -C2 -d -r1.4 -r1.5
*** surfing.py	23 Oct 2007 23:52:26 -0000	1.4
--- surfing.py	19 Jan 2008 04:13:49 -0000	1.5
***************
*** 39,43 ****
  def get_weight_line(params, start, end, max_bin):
  	weights = []
! 	s = c= c1 = c2 = theta = 0
  	confidence = False
  	if(params[0] == 'BrownBoost'):
--- 39,43 ----
  def get_weight_line(params, start, end, max_bin):
  	weights = []
! 	s = c = c1 = c2 = theta = 0
  	confidence = False
  	if(params[0] == 'BrownBoost'):
***************
*** 45,52 ****
  		s = float(params[2]) #time remaining
  	elif(params[0] == 'NormalBoost'):
  		c = float(params[1])
! 		s = c-float(params[2]) #time played
! 		start = -1.0
! 		end = 1.0
  		c1 = float(params[3])
  		c2 = float(params[4])
--- 45,53 ----
  		s = float(params[2]) #time remaining
  	elif(params[0] == 'NormalBoost'):
+ 		t = float(params[2]) # time left
  		c = float(params[1])
! 		if t < 0:
! 			t = 0
! 		s = c-t #time played
  		c1 = float(params[3])
  		c2 = float(params[4])
***************
*** 59,78 ****
  	x = start
  	for i in range(0,num_steps):
! 		mu = var = 0
! 		if params[0] == 'BrownBoost': 
! 			mu = -s
! 			var = c
! 		elif params[0] == 'NormalBoost':
! 			mu = theta - c1 * (math.exp(-s) - math.exp(-c))
! 			var = c2 * (math.exp(-2*s) - math.exp(-2*c)) + 0.05
! 					 
! 		norm = 1 / math.sqrt(var*math.pi)
! 		y = math.exp(-math.pow(x-mu,2)/(var)) * max_bin 
! 		p = (1 - erf((x-mu)/math.sqrt(var)))/2
! 		if (confidence):
! 			p = min(2*p, 1)
  		weights.append((x,y,p))
  		x += step
- 
  	return weights
  
--- 60,88 ----
  	x = start
  	for i in range(0,num_steps):
! 		if params[0] == 'BrownBoost' or params[0] == 'NormalBoost':
! 			mu = var = 0
! 			if s < 0:
! 				s = 0.0001
! 			if params[0] == 'BrownBoost': 
! 				mu = -s
! 				var = c
! 			elif params[0] == 'NormalBoost':
! 				mu = theta - c1 * (math.exp(-s) - math.exp(-c))
! 				var = c2 * (math.exp(-2*s) - math.exp(-2*c)) + 0.02
! 
! 			norm = 1 / math.sqrt(var*math.pi)
! 			y = math.exp(-math.pow(x-mu,2)/(var)) * max_bin 
! 			p = (1 - erf((x-mu)/math.sqrt(var)))/2
! 			if (confidence):
! 				p = min(2*p, 1)
! 		elif params[0] == 'AdaBoost':
! 			y = math.exp(-x)
! 			p = y
! 		elif params[0] == 'LogLossBoost':
! 			y = 1.0/(1+math.exp(x))
! 			p = math.log(1.0 + math.exp(-x))
! 			
  		weights.append((x,y,p))
  		x += step
  	return weights
  
***************
*** 159,166 ****
  	epsoutlines.append('set terminal post\n')
  	epsoutlines.append('set output \'surfing%05d.eps\'\n' % (iter))
! 	epsoutlines.append('set title "'+  params[0] +  ' ' + datafile + ' Surfing: Time Remaining ' + str(params[2]) + '" font "Times,20"\n')
  	epsoutlines.append('set key left top\n')
  	epsoutlines.append('set yzeroaxis lt -1\n')
! 	if params[0]=='NormalBoost':
  		epsoutlines.append('set xrange [-1:1]\n')
  		epsoutlines.append('set yrange [0:1]\n')
--- 169,179 ----
  	epsoutlines.append('set terminal post\n')
  	epsoutlines.append('set output \'surfing%05d.eps\'\n' % (iter))
! 	if params[0] == 'BrownBoost' or params[0] == 'NormalBoost':
! 		epsoutlines.append('set title "'+  params[0] +  ' ' + datafile + ' Surfing: Time Remaining ' + str(params[2]) + '" font "Times,20"\n')
! 	if params[0] == 'AdaBoost' or params[0] == 'LogLossBoost':
! 		epsoutlines.append('set title "'+  params[0] +  ' ' + datafile + ' Surfing: Iteration ' + str(iter) + '" font "Times,20"\n')
  	epsoutlines.append('set key left top\n')
  	epsoutlines.append('set yzeroaxis lt -1\n')
! 	if params[0] == 'NormalBoost':
  		epsoutlines.append('set xrange [-1:1]\n')
  		epsoutlines.append('set yrange [0:1]\n')
***************
*** 246,251 ****
  	    usage()
  	    sys.exit(2)
!     if(booster==None or (booster!='NormalBoost' and booster!='BrownBoost')):
! 	    print 'Only accepts `NormalBoost` and `BrownBoost` boost.'
  	    usage()
  	    sys.exit(2)
--- 259,264 ----
  	    usage()
  	    sys.exit(2)
!     if(booster==None or (booster!='NormalBoost' and booster!='BrownBoost' and booster!='AdaBoost' and booster!='LogLossBoost')):
! 	    print 'Only accepts the same boosters (capitilization and all) from JBoost.'
  	    usage()
  	    sys.exit(2)
***************
*** 263,267 ****
      for iter in range(0, num_iterations+1, framerate):
  	    boost_params_str = string.split(data[iter*(num_elements+1)], SEPARATOR)[2].split('boosting_params=')[1]
! 	    if (booster=='BrownBoost'):
  		    search_str = 'r='
  		    total_time = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
--- 276,280 ----
      for iter in range(0, num_iterations+1, framerate):
  	    boost_params_str = string.split(data[iter*(num_elements+1)], SEPARATOR)[2].split('boosting_params=')[1]
! 	    if booster=='BrownBoost':
  		    search_str = 'r='
  		    total_time = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
***************
*** 269,273 ****
  		    time_left = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
  		    params = ('BrownBoost', total_time, time_left)
! 	    if (booster=='NormalBoost'):
  		    search_str = 'c='
  		    total_time = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
--- 282,286 ----
  		    time_left = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
  		    params = ('BrownBoost', total_time, time_left)
! 	    elif booster=='NormalBoost':
  		    search_str = 'c='
  		    total_time = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
***************
*** 280,283 ****
--- 293,317 ----
  		    search_str = 'theta='
  		    theta = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    search_str = 'posc='
+ 		    posc = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    search_str = 'negc='
+ 		    negc = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    search_str = 'posc1='
+ 		    posc1 = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    search_str = 'negc1='
+ 		    negc1 = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    search_str = 'posc2='
+ 		    posc2 = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    search_str = 'negc2='
+ 		    negc2 = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    search_str = 'postheta='
+ 		    postheta = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    search_str = 'negtheta='
+ 		    negtheta = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    search_str = 'asymmetric='
+ 		    asymmetric = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
+ 		    use_asym = False
+ 		    if asymmetric == 'true' or asymmetric == 'True' or asymmetric == '1':
+ 			    use_asym = True
  		    search_str = 'confidence='
  		    confidence = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
***************
*** 286,289 ****
--- 320,325 ----
  			    use_conf = True
  		    params = ('NormalBoost', total_time, time_left, c1, c2, theta, use_conf)
+ 	    else:
+ 		    params = (booster,)
  	    lines = [x   for x in data[iter*(num_elements+1)+1:(iter+1)*(num_elements+1)]]
  	    write_gnuplot(lines, iter, datafile, params)

[Jboost-cvs] jboost/src/jboost/examples BooleanAttribute.java, NONE, 1.1 BooleanDescription.java, NONE, 1.1

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/examples
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv14785

Added Files:
	BooleanAttribute.java BooleanDescription.java 
Log Message:
Adding another type, boolean

--- NEW FILE: BooleanAttribute.java ---
package jboost.examples;

/** Represents a single real valued attribute */
public class BooleanAttribute extends Attribute {

    private boolean value;

    public BooleanAttribute(boolean value) {this.value=value; setDefined();}
    public BooleanAttribute() { setDefined(false);}

    public boolean getValue() {return value;}

    public String toString() {
	return this.isDefined() ? String.valueOf(value) : "undefined";
    }
}



--- NEW FILE: BooleanDescription.java ---
package jboost.examples;

import jboost.controller.Configuration;
import jboost.tokenizer.BadAttException;

/**
 * the description for number attributes.
 */
class BooleanDescription extends AttributeDescription {
  BooleanDescription(String name, Configuration c) throws ClassNotFoundException {
    attributeName = name;
    attributeClass = Class.forName("jboost.examples.BooleanAttribute");
    
    //  Need to make a SAFE version of configuration, which implements
    //  an error() function, which can return and error message.
    
    crucial=c.getBool("crucial",false);
    ignoreAttribute=c.getBool("ignoreAttribute",false);
    existence=c.getBool("existence",false);
    order=c.getBool("order",true);
  }
  
  /** checks format of string in datafile and converts to int
   *  
   *  If the attribute is missing it creates a new
   *  real attribute, that is not defined.
   */
  public Attribute str2Att(String string) throws BadAttException {
    if(string == null) return(new BooleanAttribute());
    string=string.trim();
    if(string.length()==0) return(new BooleanAttribute());
    boolean att = true; // initialized because try complains otherwise.
    try {
	if(string.equals("0") || string.toLowerCase().equals("false")) 
	    att = false;
    }
    catch (NumberFormatException e) {
      System.err.println(string + " is not a boolean.");
      throw new BadAttException(string+" is not a boolean",0,0);
    }
    return new BooleanAttribute (att);
  }
  
  public String toString() {
    String retval=new String(attributeName);
    retval+=" "+attributeClass.getName();
    retval+=" crucial: "+crucial;
    retval+=" ignoreAttribute: "+ignoreAttribute;
    retval+=" existence: "+existence;
    retval+=" order: "+order;
    return(retval);
  }
  
  public String toString(Attribute attr) {
    String retval=new String();
    if(attr==null) return("undefined");
    retval+=((BooleanAttribute)attr).toString();
    return(retval);
  }
}

[Jboost-cvs] jboost/src/jboost/booster MixedBinaryPrediction.java, 1.3, 1.4

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/booster
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv31054

Modified Files:
	MixedBinaryPrediction.java 
Log Message:
New normalization technique based on dt instead of exact convolution

Index: MixedBinaryPrediction.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/MixedBinaryPrediction.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -C2 -d -r1.3 -r1.4
*** MixedBinaryPrediction.java	2 Oct 2007 02:32:07 -0000	1.3
--- MixedBinaryPrediction.java	13 Dec 2007 07:41:39 -0000	1.4
***************
*** 5,9 ****
  
  /**
!  * A binary normalized predictor. See NoramlizedPrediction
   *
   * @author Aaron Arvey
--- 5,9 ----
  
  /**
!  * A binary normalized predictor. See NormalizedPrediction
   *
   * @author Aaron Arvey
***************
*** 11,34 ****
  class MixedBinaryPrediction extends BinaryPrediction implements NormalizedPrediction{
  
!     public MixedBinaryPrediction() {super();}
  
  
!     /*
!     public static boolean isNormPred(Prediction p) {
! 	return (p instanceof NormalizedPrediction);
      }
-     */
  
!     public MixedBinaryPrediction(double p) throws NotNormalizedPredException{
  	super();
! 	if (Math.abs(p) > 1) {
! 	    throw new  NotNormalizedPredException("Prediction is unnormalized "
! 					  + "prediction!  p is: " + p);
! 	}
  	prediction=p;
      }
      
      public Object clone(){
! 	Object a = new MixedBinaryPrediction(prediction);
  	return a;
      }
--- 11,36 ----
  class MixedBinaryPrediction extends BinaryPrediction implements NormalizedPrediction{
  
!     protected double deltaT;
!     protected double normalizingConstant;
  
+     public MixedBinaryPrediction() {super();}
  
!     public MixedBinaryPrediction(double p) 
! 	throws NotNormalizedPredException {
! 	throw new NotNormalizedPredException("MixedBinaryPrediction: Need to normalize based on time!");
      }
  
!     public MixedBinaryPrediction(double p, double dt, double nc) 
! 	throws NotNormalizedPredException {
  	super();
! 	deltaT = dt;
  	prediction=p;
+ 	normalizingConstant = nc;
      }
      
+ 
+ 
      public Object clone(){
! 	Object a = new MixedBinaryPrediction(prediction, deltaT);
  	return a;
      }
***************
*** 46,54 ****
  	// The prediction p is for h_t
  	double alpha = ((MixedBinaryPrediction) p).prediction;
! 	prediction = (1-Math.abs(alpha)) * prediction + alpha;
  	if (Math.abs(alpha) > 1 || Math.abs(prediction) > 1) {
  	    throw new  NotNormalizedPredException("Prediction may result in unnormalized "
  					  + "prediction!  p is: " + p);
  	}
  	return this;
      }
--- 48,58 ----
  	// The prediction p is for h_t
  	double alpha = ((MixedBinaryPrediction) p).prediction;
! 	prediction = Math.exp(-dt) * prediction + alpha;
! 	/*
  	if (Math.abs(alpha) > 1 || Math.abs(prediction) > 1) {
  	    throw new  NotNormalizedPredException("Prediction may result in unnormalized "
  					  + "prediction!  p is: " + p);
  	}
+ 	*/
  	return this;
      }
***************
*** 75,79 ****
      public boolean equals(Prediction other) {
        MixedBinaryPrediction bp= (MixedBinaryPrediction) other;
!       return (prediction == bp.prediction); 
      }
      
--- 79,83 ----
      public boolean equals(Prediction other) {
        MixedBinaryPrediction bp= (MixedBinaryPrediction) other;
!       return (prediction == bp.prediction && dt == bp.dt && normalizingConstant == bp.normalizingConstant); 
      }
      
***************
*** 83,87 ****
  
      public String cPreamble() {
! 	System.out.println("Prediction::cPreamble not supported.");
  	System.exit(2);
        return
--- 87,91 ----
  
      public String cPreamble() {
! 	System.out.println("Prediction::cPreamble not supported for 'mixed' or normalized predictions.");
  	System.exit(2);
        return
***************
*** 94,98 ****
  
      public String javaPreamble() {
! 	System.out.println("Prediction::javaPreamble not supported.");
  	System.exit(2);
        return ""
--- 98,102 ----
  
      public String javaPreamble() {
! 	System.out.println("Prediction::javaPreamble not supported for 'mixed' or normalized predictions.");
  	System.exit(2);
        return ""

[Jboost-cvs] jboost/src/jboost/booster BrownBoost.java,1.8,1.9

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/booster
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv22919

Modified Files:
	BrownBoost.java 
Log Message:
Added pos and neg values for m_s

Index: BrownBoost.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/BrownBoost.java,v
retrieving revision 1.8
retrieving revision 1.9
diff -C2 -d -r1.8 -r1.9
*** BrownBoost.java	23 Oct 2007 22:45:40 -0000	1.8
--- BrownBoost.java	13 Dec 2007 07:21:05 -0000	1.9
***************
*** 28,33 ****
--- 28,41 ----
      protected double m_s;
  
+     /** In case we ever want to make BrownBoost cost sensitive.  Also useful for Yaba. */
+     protected double m_poss;
+     protected double m_negs;
+ 
      /** The potential loss of each example at the begining of the game. */
      protected double m_initialPotential;
+ 
+     /** The potential loss of each example at the begining of the game. */
+     protected double m_posInitialPotential;
+     protected double m_negInitialPotential;
    
      /** The last value of alpha */
***************
*** 468,471 ****
--- 476,507 ----
  
    
+ 
+     /** output AdaBoost contents as a human-readable string */
+     public String toString() {
+     String s=
+       "BrownBoost. No of examples = "
+         + m_numExamples
+         + ", m_epsilon = "
+         + m_epsilon;
+     s += "\nindex\tmargin\tweight\told weight\tlabel\n";
+     NumberFormat f= new DecimalFormat("0.00");
+     for (int i= 0; i < m_numExamples; i++) {
+       s += "  "
+         + i
+         + " \t "
+ 	  + f.format(m_margins[i])
+         + " \t "
+ 	  + f.format(m_weights[i])
+         + " \t "
+ 	  + f.format(m_oldWeights[i])
+         + " \t"
+ 	  + f.format(m_sampleWeights[i]) + "\t\t"
+         + m_labels[i]
+ 	  + "\n";
+     }
+     return s;
+     }
+ 
+ 
      /**
       * BrownBag is identical to BinaryBag, except for the method used to
***************
*** 608,615 ****
       */
      public Prediction[] getPredictions(Bag[] bags, int[][] exampleIndex) {
! 	boolean bagsAreWeightless = true;
  	for (int i=0; i < bags.length; i++) {
  	    if (!bags[i].isWeightless()) {
! 		bagsAreWeightless = false;
  	    }
  	}
--- 644,652 ----
       */
      public Prediction[] getPredictions(Bag[] bags, int[][] exampleIndex) {
! 
! 	boolean allBagsAreWeightless = true;
  	for (int i=0; i < bags.length; i++) {
  	    if (!bags[i].isWeightless()) {
! 		allBagsAreWeightless = false;
  	    }
  	}
***************
*** 624,628 ****
  	 * any more iterations.
  	 */
! 	if (bagsAreWeightless || m_s < 0) {
  	    for (int i=0; i < bags.length; i++) {
  		p[i] = getZeroPred();
--- 661,665 ----
  	 * any more iterations.
  	 */
! 	if (allBagsAreWeightless || m_s < 0) {
  	    for (int i=0; i < bags.length; i++) {
  		p[i] = getZeroPred();

[Jboost-cvs] jboost/scripts resample.py,1.1.1.1,1.2

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/scripts
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv18895

Modified Files:
	resample.py 
Log Message:
This script is pretty hacky

Index: resample.py
===================================================================
RCS file: /cvsroot/jboost/jboost/scripts/resample.py,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** resample.py	16 May 2007 04:06:02 -0000	1.1.1.1
--- resample.py	13 Dec 2007 01:31:14 -0000	1.2
***************
*** 11,15 ****
  def usage():
  	print 'Usage: nfold.py --k=N  --label=STRING  --train=TRAINFILE'
! 	print '  --k=N             times to resample'
  	print '  --train=TRAINFILE the training file to oversample'
  	print '  --label=STRING    the label to resample (note that this needs to be a unique identifier,'
--- 11,16 ----
  def usage():
  	print 'Usage: nfold.py --k=N  --label=STRING  --train=TRAINFILE'
! 	print '  --k=N             integer, times to resample integer'
! 	print '  --p=R             number in [0,1] prob to keep nonlabel examples'
  	print '  --train=TRAINFILE the training file to oversample'
  	print '  --label=STRING    the label to resample (note that this needs to be a unique identifier,'
***************
*** 20,47 ****
  # a further slight speed-up on my box
  # is to map a bound-method:
! def sort_dict(d):
  	keys = d.keys()
  	vals = d.keys()
! 	keys.sort(lambda x,y : y-x)
  	return [(key,d[key]) for key in keys]
  
! def generateFile(trainfile, oversample, labelstr):
! 	# XXX: put in description
! 	
! 	# load data and shuffle it
  	f= file(trainfile, 'r')
  	data= f.readlines()
  	f.close()
  
  	add_lines = {}
! 	
  	i = 0
  	for line in data:
  		end = len(line)
! 		if(line[end-4:end-1].rstrip() == ' 1;'):
  			add_lines[i] = line
  		i = i + 1
  
- 
  	add_lines = sort_dict(add_lines)
  	for (key, val) in add_lines:
--- 21,56 ----
  # a further slight speed-up on my box
  # is to map a bound-method:
! def sort_dict(d, reverse=False):
  	keys = d.keys()
  	vals = d.keys()
! 	if (reverse):
! 		keys.sort(lambda x,y : y-x)
! 	else:
! 		keys.sort(lambda x,y : x-y)		
  	return [(key,d[key]) for key in keys]
  
! def generateFile(trainfile, oversample, undersample, labelstr):
  	f= file(trainfile, 'r')
  	data= f.readlines()
  	f.close()
  
+ 
  	add_lines = {}
! 	num_positive_examples = 0
  	i = 0
  	for line in data:
  		end = len(line)
! 		if line[end-4:end-1].rstrip() == ' 1;':
  			add_lines[i] = line
+ 			num_positive_examples += 1
+ 		elif line[end-4:end-1].rstrip() == '-1;':
+ 			# do nothing
+ 			x = 0
+ 		else:
+ 			print 'You clearly think this script was well written... you are mistaken...'
+ 			usage()
+ 			sys.exit(2)
  		i = i + 1
  
  	add_lines = sort_dict(add_lines)
  	for (key, val) in add_lines:
***************
*** 49,56 ****
  			data.insert(key,val)
  
  			
  	trainsuffix= '.train'
  	oversample = oversample + 1
! 	tfilename = trainfile + '.' + str(oversample) + trainsuffix
  	tfile= file(tfilename, 'w')
  	tfile.writelines(data)
--- 58,100 ----
  			data.insert(key,val)
  
+ 
+ 	del_lines = {}
+ 	num_negative_examples = 0
+ 	i = 0
+ 	for line in data:
+ 		end = len(line)
+ 		if line[end-4:end-1].rstrip() == ' 1;':
+ 			# do nothing
+ 			x = 0
+ 		elif line[end-4:end-1].rstrip() == '-1;':
+ 			del_lines[i] = 'd'
+ 			num_negative_examples += 1
+ 		else:
+ 			print 'You clearly think this script was well written... you are mistaken...'
+ 			usage()
+ 			sys.exit(2)
+ 		i = i + 1
+ 
+ 	del_lines = sort_dict(del_lines, True)
+ 	random.seed(107)
+ 	num_lines_deleted = 0
+ 	for (key, val) in del_lines:
+ 		r = random.random()
+ 		if r > undersample:
+ 			num_lines_deleted += 1;
+ 			data.pop(key)
+ 
+ 	print 'Original number of examples:'
+ 	print '\tNegative examples:',  num_negative_examples
+ 	print '\tPositive examples:',  num_positive_examples
+ 	print 'After sampling:'
+ 	print '\tNegative examples:',  num_negative_examples - num_lines_deleted
+ 	print '\tPositive examples:',  num_positive_examples * (oversample + 1)
+ 	print 'Expected num lines deleted:',  num_negative_examples  * (1 - undersample)
+ 	print 'Actual num lines deleted:', num_lines_deleted
  			
  	trainsuffix= '.train'
  	oversample = oversample + 1
! 	tfilename = trainfile + '.' + str(oversample) + '.' + str(undersample) + trainsuffix
  	tfile= file(tfilename, 'w')
  	tfile.writelines(data)
***************
*** 60,70 ****
      
  def main():
- # Usage:
- 
- 
- 
      try:
          opts, args = getopt.getopt(sys.argv[1:],'' ,
!                                    ['k=','train=','label='])
      except getopt.GetoptError:
          print 'resample.py: Illegal argument\n'
--- 104,110 ----
      
  def main():
      try:
          opts, args = getopt.getopt(sys.argv[1:],'' ,
!                                    ['k=','train=','label=','p='])
      except getopt.GetoptError:
          print 'resample.py: Illegal argument\n'
***************
*** 73,82 ****
  
      # parse options
!     trainfile = k = label = None
      for opt,arg in opts:
  	    if (opt == '--train'):
  		    trainfile = arg
  	    elif (opt == '--k'):
! 		    k = float(arg)
  	    elif (opt == '--label'):
  		    label = arg
--- 113,124 ----
  
      # parse options
!     trainfile = k = p = label = None
      for opt,arg in opts:
  	    if (opt == '--train'):
  		    trainfile = arg
  	    elif (opt == '--k'):
! 		    k = int(arg)
! 	    elif (opt == '--p'):
! 		    p = float(arg)
  	    elif (opt == '--label'):
  		    label = arg
***************
*** 86,98 ****
  
  
!     if (trainfile==None or k==None):
  	    usage()
  	    sys.exit(2)
  
!     p = k - int(k)
      k = int(k) - 1
      print 'Oversample by', k
      print 'Prob sample', p
!     generateFile(trainfile, k, label)
  
  
--- 128,158 ----
  
  
!     if trainfile==None:
! 	    print 'Must specify file for resampling'
  	    usage()
  	    sys.exit(2)
  
!     if k==None and p==None:
! 	    print 'Must specify over/under sample quanitity'
! 	    usage()
! 	    sys.exit(2)
! 
!     if not k==None and k < 2:
! 	    print 'k must be larger than 2 for anything to happen'
! 	    sys.exit(2)
! 
!     if not p==None and p > 1:
! 	    print 'p  must be less than 1 for anything to happen'
! 	    sys.exit(2)
! 
!     if p==None:
! 	    p = 1
!     if k==None:
! 	    k = 1
! 
      k = int(k) - 1
      print 'Oversample by', k
      print 'Prob sample', p
!     generateFile(trainfile, k, p, label)
  
  

[Jboost-cvs] jboost/scripts atree2dot2ps.pl,1.5,1.6

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/scripts
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv18494

Modified Files:
	atree2dot2ps.pl 
Log Message:
MultiPredictions now handled

Index: atree2dot2ps.pl
===================================================================
RCS file: /cvsroot/jboost/jboost/scripts/atree2dot2ps.pl,v
retrieving revision 1.5
retrieving revision 1.6
diff -C2 -d -r1.5 -r1.6
*** atree2dot2ps.pl	16 Oct 2007 02:02:27 -0000	1.5
--- atree2dot2ps.pl	13 Dec 2007 01:30:27 -0000	1.6
***************
*** 14,19 ****
      print "$0 usage: \n";
      print "\t -i (--info) file.info \t File containing runtime information (required) \n";
      print "\t -t (--tree) file.tree \t File containing the ADTree in text format (required) \n";
!     print "\t -d (--dir)  directiory\t Directory to use (optional, defaults to '.') \n";
      print "\t -l (--labels)         \t Flip the labels (-1 becomes +1) (optional)\n";
      print "\t --truncate            \t Truncate threshold values to increase readability\n";
--- 14,20 ----
      print "$0 usage: \n";
      print "\t -i (--info) file.info \t File containing runtime information (required) \n";
+     print "\t -s (--spec) file.spec \t Spec file (optional, will default to spec file in info file) \n";
      print "\t -t (--tree) file.tree \t File containing the ADTree in text format (required) \n";
!     print "\t -d (--dir)  directory \t Directory to use (optional, defaults to '.') \n";
      print "\t -l (--labels)         \t Flip the labels (-1 becomes +1) (optional)\n";
      print "\t --truncate            \t Truncate threshold values to increase readability\n";
***************
*** 29,32 ****
--- 30,34 ----
  $dirname = ".";
  $infofilename = "";
+ $specfilename = "";
  $filename = "";
  $threshold = 0;
***************
*** 37,40 ****
--- 39,43 ----
  if ( @ARGV > 0 ) {
      GetOptions ('i|info=s' => \$infofilename,
+ 		's|spec=s' => \$specfilename,
  		't|tree=s' => \$filename,
  		'd|dir=s' => \$dirname,
***************
*** 71,74 ****
--- 74,97 ----
  $graphtitle .= " | ";
  
+ 
+ if ($specfilename eq "") {
+     while ($line = <INFO>) {
+ 	if ($line =~ /specFilename = (.*).*/) {
+ 	    $specfilename = $1;
+ 	}
+     }
+ }
+ 
+ print "Spec File: '$specfilename'\n";
+ open(SPEC, "$dirname/$specfilename") || die "could not open spec file: $specFilename\n";
+ 
+ while ($line = <SPEC>) {
+     if ($line =~ /labels.*\((.*)\).*/) {
+ 	@labels = split(/,/, $1);
+     }
+ }
+ print "Labels: @labels\n";
+ $numLabels = @labels;
+ 
  $filename= $dirname . "/" . $filename;
  
***************
*** 77,92 ****
  open(OUT,">$filename.$threshold.dot");
  
! print "running atree2dot2ps.pl ".join(" ",@ARGV).". generating $filename.$threshold.{dot,ps,pdf}\n";
  
  print OUT "digraph G \{\n";
  print OUT "size=\"10,7.5\";\n";
! print OUT "orientation=\"landscape\";\n";
  print OUT "ratio=auto;\n";
  print OUT "center=TRUE;\n";
  
- 
- 
  $last_iter = 0;
- 
  while($line = <FILE>) {
      if($line =~ /^(\d*)\t\[([^\]]*)\] Splitter = (.*)$/) {
--- 100,114 ----
  open(OUT,">$filename.$threshold.dot");
  
! print "Running atree2dot2ps.pl ".join(" ",@ARGV).".\n";
! print "Generating $filename.$threshold.{dot,ps,pdf}\n";
  
  print OUT "digraph G \{\n";
  print OUT "size=\"10,7.5\";\n";
! #print OUT "orientation=\"landscape\";\n";
! print OUT "rotate=\"90\";\n";
  print OUT "ratio=auto;\n";
  print OUT "center=TRUE;\n";
  
  $last_iter = 0;
  while($line = <FILE>) {
      if($line =~ /^(\d*)\t\[([^\]]*)\] Splitter = (.*)$/) {
***************
*** 99,124 ****
  	}
  	if ($splitType =~ /InequalitySplitter/ && $truncatesplits) {
! 	    $label =~ /([a-zA-Z0-9\-\s]*)\s*(<|>)\s*(-*[0-9]*(\.)?[0-9]?[0-9]?[0-9]?).*/;
  	    $var = $1;
              $ineq = $2; 
              $val = $3;
! 	    print "$type, $index, $var, $ineq, $val, $splitType\n";
  	    $label = "$var $ineq $val"
  	} else {
! 	    print "$type, $index, $label, $splitType\n";
  	}
!     }
!     elsif($line =~ /^(\d*)\t\[([^\]]*)\].*p\(1\)= (.*)/) {
  	$type = "predictor";
  	$iteration = $1;
  	$index = $2;
  	$label = $3;
! 	$label =~ s/(\d\.\d{3}).*/$1/;
! 	if($fliplabels) {
! 	    $label = -$label;
  	}
! 	print "$type, $index, $label\n";
!     }
!     else {
  	chop $line;
  	next;
--- 121,165 ----
  	}
  	if ($splitType =~ /InequalitySplitter/ && $truncatesplits) {
! 	    $label =~ /(.*)(<|>)\s*(-*[0-9]*(\.)?[0-9]?[0-9]?[0-9]?).*/;
  	    $var = $1;
              $ineq = $2; 
              $val = $3;
! 	    #print "$type, $index, $var, $ineq, $val, $splitType\n";
  	    $label = "$var $ineq $val"
  	} else {
! 	    #print "line: $line\n";
! 	    #print "$type, $index, $label, $splitType\n";
  	}
!     } elsif($line =~ /^(\d*)\t\[([^\]]*)\].*prediction.*= (.*)/) {
  	$type = "predictor";
  	$iteration = $1;
  	$index = $2;
  	$label = $3;
! 	#print "label: $label\n";
! 	$predType = $3;
! 	#print "predType: $predType\n";
! 	if ($predType =~ /MultiPrediction/) {
! 	    $label = "";
! 	    for ($i=0; $i<$numLabels; $i++) {
! 		$line = <FILE>;
! 		if ($line =~ /^.*prediction.*= (.*)/) {
! 		    $pred = $1;
! 		}
! 		if ($fliplabels) {
! 		    $pred = -$pred;
! 		}
! 		$label .= @labels[$i] . ": " . $pred . "\\n";
! 	    }
! 	    #print "label: $label \n";
! 	} else {
! 	    $label =~ s/(\d\.\d{3}).*/$1/;
! 	    #print "label: $label\n";
! 	    if($fliplabels) {
! 		$label = -$label;
! 	    }
  	}
! 	#print "$type, $predType, $index, $label\n";
!     } else {
! 	#print "Line ignored: $line";
  	chop $line;
  	next;
***************
*** 164,168 ****
  #system("dot -Tps2 $filename.$threshold.dot -o $filename.$threshold.eps");
  system("dot -Tps $filename.$threshold.dot -o $filename.$threshold.ps");
! system("ps2pdf $filename.$threshold.ps");
! system("pstoimg -antialias -aaliastext -density 400 $filename.$threshold.ps");
  
--- 205,209 ----
  #system("dot -Tps2 $filename.$threshold.dot -o $filename.$threshold.eps");
  system("dot -Tps $filename.$threshold.dot -o $filename.$threshold.ps");
! #system("ps2pdf $filename.$threshold.ps");
! #system("pstoimg -antialias -aaliastext -density 400 $filename.$threshold.ps");
  

[Jboost-cvs] jboost/scripts error.py,1.4,1.5

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/scripts
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv11316

Modified Files:
	error.py 
Log Message:
Can now show separate error for positive and negative examples

Index: error.py
===================================================================
RCS file: /cvsroot/jboost/jboost/scripts/error.py,v
retrieving revision 1.4
retrieving revision 1.5
diff -C2 -d -r1.4 -r1.5
*** error.py	23 Oct 2007 22:50:42 -0000	1.4
--- error.py	12 Dec 2007 23:50:29 -0000	1.5
***************
*** 12,36 ****
  	print '\t--bound             show the bound on training error'
  	print '\t--separate          separate the positive and negative examples'
! 	print '\t--scores=score_file the score file (only needed if --separate used)'
! 	print '\t--margins=marg_file the margin file (only needed if --separate used)'
  
! def do_separate(score_filename, margin_filename):
! 	print 'Parsing scores file'
! 	f = open(score_filename,'r')
! 	lines = f.readlines()
! 	f.close()
! 	
! 	read_iter = False
! 	data = []
! 	for line in lines:
! 		line = line.strip()
! 		if (line[:8] =='End time'):
! 			break
! 		if (read_iter):
! 			data.append(get_score(line))
! 			
  
!     
  
  
  
--- 12,88 ----
  	print '\t--bound             show the bound on training error'
  	print '\t--separate          separate the positive and negative examples'
! 	print '\t--boost-info=file  The boosting.info file for a data set'
  
! SEPARATOR = ':'
  
! def get_margin(line):
! 	m = line.split(SEPARATOR)[1]
! 	m.replace(']','')
! 	m.replace(';','')
! 	m.replace(SEPARATOR,'')
! 	m.replace(' ','')
! 	m.replace('\t','')
! 	return float(m)
! 
! def get_score(line):
! 	m = line.split(SEPARATOR)[2]
! 	m.replace(']','')
! 	m.replace(';','')
! 	m.replace(SEPARATOR,'')
! 	m.replace(' ','')
! 	m.replace('\t','')
! 	return float(m)
  
+ def getErrorsSingleIter(data):
+ 	tp = 0
+ 	fp = 0
+ 	tn = 0
+ 	fn = 0
+ 
+ 	scores = map(get_score, data)
+ 	margins = map(get_margin, data)
+ 	for score, margin in zip(scores, margins):		
+ 		if score < 0 and margin < 0:
+ 			fn += 1
+ 		if score < 0 and margin > 0:
+ 			tn += 1
+ 		if score > 0 and margin < 0:
+ 			fp += 1
+ 		if score > 0 and margin > 0:
+ 			tp += 1
+ 	return (tp,fp,tn,fn)					
+ 
+ def getErrors(boostfilename):
+     f= open(boostfilename,'r')
+     data= f.readlines()
+     f.close()
+     score_elements = int((string.split(data[0],SEPARATOR))[1].split('=')[1])
+     num_iterations = int((string.split(data[-score_elements-1],SEPARATOR))[0].split('=')[1])
+     print num_iterations
+ 
+     errors = []
+     for iter in range(num_iterations+1):
+ 	    tp,fp,tn,fn = getErrorsSingleIter([x for x in data[iter*(score_elements+1)+1:(iter+1)*(score_elements+1)]])
+ 	    print iter,tp,fp,tn,fn
+ 	    ret = {}
+ 	    if tp + fn > 0:
+ 		    ret['recall'] = ret['sensitivity'] = float(tp) / (tp + fn)
+ 	    else:
+ 		    ret['recall'] = ret['sensitivity'] = 0
+ 	    if tp + fp > 0:
+ 		    ret['precision'] = float(tp) / (tp + fp)
+ 	    else:
+ 		    ret['precision'] = 0
+ 	    if fp + tn > 0:		    
+ 		    ret['fpr'] = float(fp) / (fp + tn)
+ 	    else:
+ 		    ret['fpr'] = 0
+ 	    ret['specificity'] = 1 - ret['fpr']
+ 	    ret['neg_err'] = ret['fpr']
+ 	    ret['pos_err'] = 1 - ret['sensitivity']
+ 	    ret['err'] = float(fp+fn) / (fp+tp+fn+tn)
+ 	    ret['iter'] = iter
+ 	    errors.append(ret)
+     return errors
  
  
***************
*** 39,53 ****
  
      try:
! 	    opts, args= getopt.getopt(sys.argv[1:], '', ['info=','logaxis','bound','separate','scores=','margins='])
      except getopt.GetoptError:
  	    usage()
  	    sys.exit(2)
  	    
!     info_filename = logaxis = showbound = show_separate = margin_filename = score_filename = None
      for opt,arg in opts:
  	    if (opt == '--info'):
  		    info_filename = arg
! 	    if (opt == '--margins'):
! 		    margin_filename = arg
  	    if (opt == '--info'):
  		    score_filename = arg
--- 91,105 ----
  
      try:
! 	    opts, args= getopt.getopt(sys.argv[1:], '', ['info=','logaxis','bound','separate','boost-info='])
      except getopt.GetoptError:
  	    usage()
  	    sys.exit(2)
  	    
!     info_filename = logaxis = showbound = show_separate = boost_filename = None
      for opt,arg in opts:
  	    if (opt == '--info'):
  		    info_filename = arg
! 	    if (opt == '--boost-info'):
! 		    boost_filename = arg
  	    if (opt == '--info'):
  		    score_filename = arg
***************
*** 60,69 ****
  	
  
!     if (show_separate and (margin_filename==None or score_filename==None)):
  	    print 'ERROR: Need to specify score and margin file if showing positive and negative elements separately'
  	    usage()
  	    sys.exit(2)	    
  	    
!     if(info_filename == None):
  	    print 'ERROR: Need to specify info file'
  	    usage()
--- 112,121 ----
  	
  
!     if show_separate and boost_filename==None:
  	    print 'ERROR: Need to specify score and margin file if showing positive and negative elements separately'
  	    usage()
  	    sys.exit(2)	    
  	    
!     if info_filename == None:
  	    print 'ERROR: Need to specify info file'
  	    usage()
***************
*** 100,115 ****
      outlines = []
      for line in data:
! 	    out = ''
! 	    out += line['iter'] + ' '
! 	    out += line['train'] + ' '
! 	    out += line['test'] + ' '
  	    if (showbound):
  		    out += line['bound'] + ' '
  	    outlines.append(out+'\n')
- 	    
      f = open('error.dat', 'w')
      f.writelines(outlines)
      f.close()
  
      outlines = []
  
--- 152,173 ----
      outlines = []
      for line in data:
! 	    out = line['iter'] + ' ' + line['train'] + ' ' + line['test'] + ' '
  	    if (showbound):
  		    out += line['bound'] + ' '
  	    outlines.append(out+'\n')
      f = open('error.dat', 'w')
      f.writelines(outlines)
      f.close()
  
+     if show_separate:
+ 	    lines = getErrors(boost_filename)
+ 	    outlines = []
+ 	    for line in lines:
+ 		    out = str(line['iter']) + ' ' + str(line['neg_err']) + ' ' + str(line['pos_err']) + ' ' + str(line['err'])
+ 		    outlines.append(out+'\n')
+ 	    f = open('error_sep.dat', 'w')
+ 	    f.writelines(outlines)
+ 	    f.close()
+ 
      outlines = []
  
***************
*** 119,129 ****
      outlines.append('set ylabel "Error"\n')
      outlines.append('set title "' + info_filename + ' Error"\n')
      if (logaxis):
  	    outlines.append('set logscale x 10\n')
      out = ''
!     out += 'plot "error.dat" using 1:2 title \'train\' with lines'
!     out += ', "error.dat" using 1:3 title \'test\' with lines'
!     if showbound:
! 	    out += ', "error.dat" using 1:4 title \'bound\' with lines'
      out += '\n'
      outlines.append(out)
--- 177,197 ----
      outlines.append('set ylabel "Error"\n')
      outlines.append('set title "' + info_filename + ' Error"\n')
+     if show_separate:
+ 	    outlines.append('set title "' + info_filename + ' Error (POSITIVE EXAMPLE ERROR MAY BE NEGATIVE EXAMPLE ERROR)"\n')
      if (logaxis):
  	    outlines.append('set logscale x 10\n')
+     outlines.append('set style line 10 lt 1 lw 1 pt 5 ps 0.65\n')
+     outlines.append('set style line 11 lt 3 lw 1 pt 1 ps 0.65\n')
+     outlines.append('set style line 12 lt 5 lw 1 pt 1 ps 0.65\n')
      out = ''
!     if show_separate:
! 	    out += 'plot "error_sep.dat" using 1:2 title \'Negative Example Error\' with lines linestyle 10'
! 	    out += ', "error_sep.dat" using 1:3 title \'Positive Example Error\' with lines linestyle 11'
! 	    out += ', "error_sep.dat" using 1:4 title \'Error\' with lines linestyle 12'
!     else:
! 	    out += 'plot "error.dat" using 1:2 title \'train\' with lines linestyle 10'
! 	    out += ', "error.dat" using 1:3 title \'test\' with lines linestyle 11'
! 	    if showbound:
! 		    out += ', "error.dat" using 1:4 title \'bound\' with lines'
      out += '\n'
      outlines.append(out)
***************
*** 136,148 ****
      os.system('gnuplot ' + pngfilename)
  
- #####################################
-     #    Now do scores file for separate option
- #####################################
-     
-     if show_separate:
- 	    do_separate()
- 
- 
- 
  
  if __name__ == "__main__":
--- 204,207 ----

[Jboost-cvs] jboost/src/jboost/controller Configuration.java, 1.7, 1.8 Controller.java, 1.5, 1.6

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/controller
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv26595

Modified Files:
	Configuration.java Controller.java 
Log Message:
Logging changed to be correct

Index: Controller.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/controller/Controller.java,v
retrieving revision 1.5
retrieving revision 1.6
diff -C2 -d -r1.5 -r1.6
*** Controller.java	2 Oct 2007 20:36:43 -0000	1.5
--- Controller.java	5 Dec 2007 06:39:57 -0000	1.6
***************
*** 171,174 ****
--- 171,175 ----
  	} catch (Exception e) {
  	    // XXX: what to do?
+ 	    e.printStackTrace();
  	    throw new InstantiationException(e.getMessage());
  	}
***************
*** 206,212 ****
  	    m_learningTree= new InstrumentedAlternatingTree(m_splitterBuilderVector, m_booster, 
  							    m_trainSetIndices,m_config);
! 	    System.out.println("Finished creating root (iteration -1)");
  	}
      
  	// main loop
  	start= System.currentTimeMillis();
--- 207,220 ----
  	    m_learningTree= new InstrumentedAlternatingTree(m_splitterBuilderVector, m_booster, 
  							    m_trainSetIndices,m_config);
! 	    System.out.println("Finished creating root (iteration 0)");
! 	    if (Monitor.logLevel > 1) {
! 		m_monitor.logIteration(
! 				       0, // iteration
! 				       m_learningTree.getCombinedPredictor(),
! 				       m_learningTree.getLastBasePredictor());
! 	    }
  	}
      
+ 
  	// main loop
  	start= System.currentTimeMillis();
***************
*** 346,356 ****
      
  	Executor pe=ExecutorSinglet.getExecutor();
! 	for (int iter= 0; iter < iterNo	&& !m_learningTree.boosterIsFinished(); iter++) {
! 	    if (Monitor.logLevel > 1) {
! 		m_monitor.logIteration(
! 				       iter,
! 				       m_learningTree.getCombinedPredictor(),
! 				       m_learningTree.getLastBasePredictor());
! 	    }
  	    start= System.currentTimeMillis();
  	    Vector candidates= m_learningTree.getCandidates();
--- 354,358 ----
      
  	Executor pe=ExecutorSinglet.getExecutor();
! 	for (int iter= 1; iter <= iterNo && !m_learningTree.boosterIsFinished(); iter++) {
  	    start= System.currentTimeMillis();
  	    Vector candidates= m_learningTree.getCandidates();
***************
*** 422,429 ****
  			    + iter);
  	    }
- 	    System.out.println("Finished learning iteration " + iter);
  	    if(m_booster instanceof BrownBoost){
  		iterNo++;
  	    }
  	}
  
--- 424,437 ----
  			    + iter);
  	    }
  	    if(m_booster instanceof BrownBoost){
  		iterNo++;
  	    }
+ 	    System.out.println("Finished learning iteration " + iter);
+ 	    if (Monitor.logLevel > 1) {
+ 		m_monitor.logIteration(
+ 				       iter,
+ 				       m_learningTree.getCombinedPredictor(),
+ 				       m_learningTree.getLastBasePredictor());
+ 	    }
  	}
  

Index: Configuration.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/controller/Configuration.java,v
retrieving revision 1.7
retrieving revision 1.8
diff -C2 -d -r1.7 -r1.8
*** Configuration.java	23 Oct 2007 22:46:32 -0000	1.7
--- Configuration.java	5 Dec 2007 06:39:57 -0000	1.8
***************
*** 95,99 ****
  	+ "\t-BoosTexter	   Only make a zero prediction at the root node.\n"
  	+ "\t-booster_smooth sf   Smoothing factor for prediction computation (default: 0.5)\n"
! 	+ "\t                     Described Shapire & Singer 1999 (smoothing the predictions), \n"
  	+ "\t                     $epsilon = sf / total_num_examples$  \n"
  	+ "\t-booster_paranoid Use safe version of booster (default: false)\n"
--- 95,99 ----
  	+ "\t-BoosTexter	   Only make a zero prediction at the root node.\n"
  	+ "\t-booster_smooth sf   Smoothing factor for prediction computation (default: 0.5)\n"
! 	+ "\t                     Described Schapire & Singer 1999 (smoothing the predictions), \n"
  	+ "\t                     $epsilon = sf / total_num_examples$  \n"
  	+ "\t-booster_paranoid Use safe version of booster (default: false)\n"

[Jboost-cvs] jboost CHANGES, 1.4, 1.5 build.xml, 1.7, 1.8 jboost, 1.4, 1.5

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv31948

Modified Files:
	CHANGES build.xml jboost 
Log Message:
bunch of misc changes

Index: jboost
===================================================================
RCS file: /cvsroot/jboost/jboost/jboost,v
retrieving revision 1.4
retrieving revision 1.5
diff -C2 -d -r1.4 -r1.5
*** jboost	4 Jun 2007 22:15:12 -0000	1.4
--- jboost	23 Oct 2007 23:52:26 -0000	1.5
***************
*** 22,29 ****
  fi
  
! echo -n "JBoost jar file is:"
  echo "${CLASSPATH}" | sed 's/:/\n/g' | grep 'jboost\.jar'
  
! exec java -Xmx1000M jboost.controller.Controller  $@
  
  if [ $? -ne 0 ]; then
--- 22,29 ----
  fi
  
! echo -n "JBoost jar file is: "
  echo "${CLASSPATH}" | sed 's/:/\n/g' | grep 'jboost\.jar'
  
! exec java -Xmx500M jboost.controller.Controller  $@
  
  if [ $? -ne 0 ]; then

Index: CHANGES
===================================================================
RCS file: /cvsroot/jboost/jboost/CHANGES,v
retrieving revision 1.4
retrieving revision 1.5
diff -C2 -d -r1.4 -r1.5
*** CHANGES	14 Jun 2007 16:33:31 -0000	1.4
--- CHANGES	23 Oct 2007 23:52:25 -0000	1.5
***************
*** 2,5 ****
--- 2,15 ----
  --------------------
  
+ + Release 1.4 + 
+ =================
+ 
+  - NormalBoost is implemented
+     - BrownBoost-like update
+     - Asymetric cost
+     - Iteration order evalution of the ADTree
+  - More bugs fixed
+  - Scripts updated and made more general
+ 
  + Release 1.3.1 + 
  =================

Index: build.xml
===================================================================
RCS file: /cvsroot/jboost/jboost/build.xml,v
retrieving revision 1.7
retrieving revision 1.8
diff -C2 -d -r1.7 -r1.8
*** build.xml	4 Jun 2007 22:15:10 -0000	1.7
--- build.xml	23 Oct 2007 23:52:26 -0000	1.8
***************
*** 15,19 ****
    <property name="dir.scripts" value="scripts"/>
    <property name="dir.javadoc" value="javadoc"/>
!   <property name="jboost.version" value="1.3.1"/>
    <property name="test.class.name" value="jboost.AllTests"/>
   
--- 15,19 ----
    <property name="dir.scripts" value="scripts"/>
    <property name="dir.javadoc" value="javadoc"/>
!   <property name="jboost.version" value="1.4"/>
    <property name="test.class.name" value="jboost.AllTests"/>
   
***************
*** 76,80 ****
    <target name="compile" depends="prepare"
            description="Compile all source code.">
!     <javac srcdir="${dir.src}" classpath="${dir.lib}/junit.jar:${dir.lib}/concurrent.jar" destdir="${dir.build}" debug="on" />
    </target>
  
--- 76,85 ----
    <target name="compile" depends="prepare"
            description="Compile all source code.">
!     <javac srcdir="${dir.src}" 
!            classpath="${dir.lib}/junit.jar:${dir.lib}/concurrent.jar" 
!            destdir="${dir.build}" 
!            debug="on" 
!            executable="/usr/java/jdk1.3/bin/javac"
!     />
    </target>
  
***************
*** 187,195 ****
          </exec>
  
! 
    	<tar destfile="jboost-${jboost.version}.tar">
              <tarfileset dir="jboost-${jboost.version}" prefix="jboost-${jboost.version}"/>
          </tar>
    	<gzip zipfile="jboost-${jboost.version}.tgz" src="jboost-${jboost.version}.tar"/>
  
  
--- 192,208 ----
          </exec>
  
! <!--
    	<tar destfile="jboost-${jboost.version}.tar">
              <tarfileset dir="jboost-${jboost.version}" prefix="jboost-${jboost.version}"/>
          </tar>
    	<gzip zipfile="jboost-${jboost.version}.tgz" src="jboost-${jboost.version}.tar"/>
+ -->
+ 
+         <exec executable="tar" >
+              <arg value="cpzf" />
+              <arg value="jboost-${jboost.version}.tgz" />
+              <arg value="jboost-${jboost.version}" />
+         </exec>
+ 
  
  

[Jboost-cvs] jboost/src/jboost/tokenizer DataStream.java, 1.1.1.1, 1.2 jboost_DataStream.java, 1.1.1.1, 1.2

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/tokenizer
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv31948/src/jboost/tokenizer

Modified Files:
	DataStream.java jboost_DataStream.java 
Log Message:
bunch of misc changes

Index: DataStream.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/tokenizer/DataStream.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** DataStream.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- DataStream.java	23 Oct 2007 23:52:26 -0000	1.2
***************
*** 15,18 ****
--- 15,19 ----
    
      public static String WEIGHT_ATTR= "weight";
+     public static String ID_ATTR = "id";
      public static String LABELS_ATTR= "labels";
    

Index: jboost_DataStream.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/tokenizer/jboost_DataStream.java,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** jboost_DataStream.java	16 May 2007 04:06:02 -0000	1.1.1.1
--- jboost_DataStream.java	23 Oct 2007 23:52:26 -0000	1.2
***************
*** 359,363 ****
                  ed.setLabel(attribute, lineCount);
                } else if (name.equals(DataStream.WEIGHT_ATTR)) {
!                   ed.setWeight(attribute, lineCount);
                } else {
                  ed.addAttribute(attribute);
--- 359,365 ----
                  ed.setLabel(attribute, lineCount);
                } else if (name.equals(DataStream.WEIGHT_ATTR)) {
!                   ed.setWeight(attribute, lineCount);/*
!               } else if (name.equals(DataStream.ID_ATTR)) {
! 	      ed.setId(attribute, lineCount);*/
                } else {
                  ed.addAttribute(attribute);

[Jboost-cvs] jboost/scripts surfing.py,1.3,1.4

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/scripts
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv31948/scripts

Modified Files:
	surfing.py 
Log Message:
bunch of misc changes

Index: surfing.py
===================================================================
RCS file: /cvsroot/jboost/jboost/scripts/surfing.py,v
retrieving revision 1.3
retrieving revision 1.4
diff -C2 -d -r1.3 -r1.4
*** surfing.py	23 Oct 2007 22:50:42 -0000	1.3
--- surfing.py	23 Oct 2007 23:52:26 -0000	1.4
***************
*** 13,17 ****
  def usage():
  	print 'Usage: margin.py    '
- 	print '\t--surfing=filename      surfing file as output by jboost (-a -2 switch)'
  	print '\t--boost-info=filename   file as output by jboost (-a -2 switch)'
  	print '\t--data=filename         train/test filename'
--- 13,16 ----
***************
*** 128,131 ****
--- 127,131 ----
  def write_gnuplot(lines, iter, datafile, params):
  
+ 	print 'Writing gnuplot file'
  	line_compare = lambda x,y : cmp(float(string.split(x,SEPARATOR)[1]), float(string.split(y,SEPARATOR)[1]))
  	lines.sort(line_compare);
***************
*** 138,142 ****
  	marg_max = max(margins);
  	marg_min = min(margins);
! 	lines = get_margin_hist(margins, 1);
  	max_bin = max([l[1] for l in lines])
  	lines = [str(l[0]) + ' ' + str(l[1]) + '\n' for l in lines] 
--- 138,142 ----
  	marg_max = max(margins);
  	marg_min = min(margins);
! 	lines = get_margin_hist(margins, True);
  	max_bin = max([l[1] for l in lines])
  	lines = [str(l[0]) + ' ' + str(l[1]) + '\n' for l in lines] 
***************
*** 216,220 ****
  
      try:
!         opts, args= getopt.getopt(sys.argv[1:], '', ['surfing=', 'boost-info=','data=','spec=','labels=', 'iteration=', 'framerate=', 'booster=', 'sample'])
      except getopt.GetoptError, inst:
          print 'Received an illegal argument:', inst
--- 216,220 ----
  
      try:
!         opts, args= getopt.getopt(sys.argv[1:], '', ['boost-info=','data=','spec=', 'framerate=', 'booster='])
      except getopt.GetoptError, inst:
          print 'Received an illegal argument:', inst
***************
*** 257,260 ****
--- 257,261 ----
      num_elements= int((string.split(data[0],SEPARATOR)[1].split('=')[1]))
      num_iterations = int(string.split(string.split(data[-num_elements-1], SEPARATOR)[0], '=')[1])
+     print 'Read ' + str(num_iterations) + ' iterations, each with ' + str(num_elements) + ' from ' + boostfile
  
      #os.system('rm surfing*.eps surfing*.png')

[Jboost-cvs] jboost/src/jboost/atree InstrumentedAlternatingTree.java, 1.4, 1.5

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/atree
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv31948/src/jboost/atree

Modified Files:
	InstrumentedAlternatingTree.java 
Log Message:
bunch of misc changes

Index: InstrumentedAlternatingTree.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/atree/InstrumentedAlternatingTree.java,v
retrieving revision 1.4
retrieving revision 1.5
diff -C2 -d -r1.4 -r1.5
*** InstrumentedAlternatingTree.java	13 Oct 2007 04:32:27 -0000	1.4
--- InstrumentedAlternatingTree.java	23 Oct 2007 23:52:26 -0000	1.5
***************
*** 159,162 ****
--- 159,165 ----
  	Prediction[] tmpPred= null;
  	initialWeights[0]= m_booster.newBag(m_examples);
+ 	int[][] tmpEx= new int[1][];
+ 	tmpEx[0]= m_examples;
+ 
  	//  To make it behave like boost texter.
  	if (m_emulateBoosTexter) {
***************
*** 166,173 ****
  	    }
  	    initialWeights[0].reset();
  	}
      
- 	int[][] tmpEx= new int[1][];
- 	tmpEx[0]= m_examples;
  	tmpPred= m_booster.getPredictions(initialWeights, tmpEx);
  	m_booster.update(tmpPred, tmpEx);
--- 169,180 ----
  	    }
  	    initialWeights[0].reset();
+ 	    tmpPred= m_booster.getPredictions(initialWeights, tmpEx);
+ 	    m_booster.update(tmpPred, tmpEx);
+ 	    PredictorNode predictorNode= new PredictorNode(tmpPred[0], "R", 
+ 						       0, null, null, 0);
+ 	    m_predictors.add(predictorNode);
+ 	    return;
  	}
      
  	tmpPred= m_booster.getPredictions(initialWeights, tmpEx);
  	m_booster.update(tmpPred, tmpEx);

[Jboost-cvs] jboost/demo stem.spec,1.1.1.1,1.2

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/demo
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv31948/demo

Modified Files:
	stem.spec 
Log Message:
bunch of misc changes

Index: stem.spec
===================================================================
RCS file: /cvsroot/jboost/jboost/demo/stem.spec,v
retrieving revision 1.1.1.1
retrieving revision 1.2
diff -C2 -d -r1.1.1.1 -r1.2
*** stem.spec	16 May 2007 04:06:02 -0000	1.1.1.1
--- stem.spec	23 Oct 2007 23:52:26 -0000	1.2
***************
*** 2,5 ****
--- 2,6 ----
  attributeTerminator=,
  maxBadExa=0
+ 
  age              number
  income           number

[Jboost-cvs] jboost/scripts error.py, 1.3, 1.4 margin.py, 1.7, 1.8 surfing.py, 1.2, 1.3

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/scripts
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv6911

Modified Files:
	error.py margin.py surfing.py 
Log Message:
New output format

Index: margin.py
===================================================================
RCS file: /cvsroot/jboost/jboost/scripts/margin.py,v
retrieving revision 1.7
retrieving revision 1.8
diff -C2 -d -r1.7 -r1.8
*** margin.py	13 Jun 2007 16:09:14 -0000	1.7
--- margin.py	23 Oct 2007 22:50:42 -0000	1.8
***************
*** 8,20 ****
  import re
  
  def usage():
  	print 'Usage: margin.py    '
! 	print '\t--margins=filename  margins file as output by jboost (-a -2 switch)'
! 	print '\t--scores=filename   scores file as output by jboost (-a -2 switch)'
! 	print '\t--data=filename     train/test filename'
! 	print '\t--spec=filename     spec filename'
! 	print '\t--iteration=NUMBER  the iteration number to inspect'
! 	print '\t--sample            are we interested in a sample of false pos/neg'
! 	print '\t--labels=filename   labels file (if unspecified then created from data file)'
  
  def sortByColumns(list2d, column_indices, in_place=False):
--- 8,21 ----
  import re
  
+ 
+ SEPARATOR = ':'
+ 
  def usage():
  	print 'Usage: margin.py    '
! 	print '\t--boost-info=filename  margins file as output by jboost (-a -2 switch)'
! 	print '\t--data=filename        train/test filename'
! 	print '\t--spec=filename        spec filename'
! 	print '\t--iteration=i,j,k,...  the iterations to inspect, no spaces between commas'
! 	print '\t--sample               are we interested in a sample of false pos/neg'
  
  def sortByColumns(list2d, column_indices, in_place=False):
***************
*** 45,52 ****
  	    '\n'
  	    )
!     f.write('Sensitivity: ' + str(float(len(truepos))/(len(truepos)+len(falseneg))) + '\n');
!     f.write('False Pos Rate: ' + str(float(len(falsepos))/(len(trueneg)+len(falsepos))) + '\n');
  
  
      list= [[margins[w],examples[w]] for w in falseneg.keys()]
      sortByColumns(list, [0], True)
--- 46,58 ----
  	    '\n'
  	    )
!     f.write('Sensitivity: ' +  str(1-float(len(falseneg))/(len(truepos)+len(falseneg))) + '\n')
!     f.write('False Pos Rate: ' + str(float(len(falsepos))/(len(trueneg)+len(falsepos))) + '\n')
  
+     f.write('True pos: ' + str(len(truepos)) + '\n')
+     f.write('False pos: ' + str(len(falsepos)) + '\n')
+     f.write('True neg: ' + str(len(trueneg)) + '\n')
+     f.write('False neg: ' + str(len(falseneg)) + '\n')
  
+     
      list= [[margins[w],examples[w]] for w in falseneg.keys()]
      sortByColumns(list, [0], True)
***************
*** 77,86 ****
  
  
! def write_gnuplot(scores_list, iters, datafile):
  
  	margin_list = []
  	for i in range(len(iters)):
! 		scores = scores_list[i]
! 		margins = scores_list[i] #get_margins(labels, scores);
  		margins.sort()
  		margin_list.append(margins)
--- 83,93 ----
  
  
! def write_gnuplot(m_list, iters, datafile):
  
  	margin_list = []
  	for i in range(len(iters)):
! 		scores = m_list[i]
! 		m = m_list[i]
! 		margins = m[:]
  		margins.sort()
  		margin_list.append(margins)
***************
*** 131,144 ****
  
  	
  
! 	f = open('margin.png.gnuplot', 'w')
  	f.writelines(pngoutlines)
  	f.close()
  
! 	f = open('margin.eps.gnuplot', 'w')
  	f.writelines(epsoutlines)
  	f.close()
  
! 
  
  
--- 138,155 ----
  
  	
+ 	pngfilename = 'margin.png.gnuplot'
+ 	epsfilename = 'margin.eps.gnuplot'
  
! 	f = open(pngfilename, 'w')
  	f.writelines(pngoutlines)
  	f.close()
  
! 	f = open(epsfilename, 'w')
  	f.writelines(epsoutlines)
  	f.close()
  
! 	os.system('gnuplot ' + pngfilename)
! 	os.system('gnuplot ' + epsfilename)
! 	os.system('epstopdf margin.eps')
  
  
***************
*** 164,180 ****
  	
  
! def get_margins(labels, scores):
! 	def computeMargin(score, label):
! 		return score*int(label)
! 
! 	margins= map(computeMargin, scores, labels)
! 	return margins
! 
! 
! 
! def process_data(examples, labels, margins, scores, datafile, sample):
  
  	print 'Examples ' + str(len(examples))
- 	print 'Scores   ' + str(len(scores))
  	print 'Margins  ' + str(len(margins))
  	print 'Generating margins'
--- 175,181 ----
  	
  
! def process_data(examples, labels, margins, datafile, sample):
  
  	print 'Examples ' + str(len(examples))
  	print 'Margins  ' + str(len(margins))
  	print 'Generating margins'
***************
*** 182,187 ****
  	print 'Matching margins with data'
  
- 	#pos_label, neg_label = getLabels(specfile)
- 
  	falsepos = dict([(w,[margins[w],examples[w],labels[w]])
  		    for w in range(len(margins))
--- 183,186 ----
***************
*** 200,203 ****
--- 199,205 ----
  
  
+ #        for i in range(len(margins)):
+ #		print '%d\t %s\t %.2f\t %.2f' % (i, labels[i].strip(), margins[i])
+ 
  	write_files(examples, datafile, sample, margins, falsepos, truepos, falseneg, trueneg)
  	
***************
*** 213,217 ****
  
      try:
!         opts, args= getopt.getopt(sys.argv[1:], '', ['margins=', 'scores=','data=','spec=','labels=', 'iteration=', 'sample'])
      except getopt.GetoptError, inst:
          print 'Received an illegal argument:', inst
--- 215,219 ----
  
      try:
!         opts, args= getopt.getopt(sys.argv[1:], '', ['boost-info=', 'data=', 'spec=', 'iteration=', 'sample'])
      except getopt.GetoptError, inst:
          print 'Received an illegal argument:', inst
***************
*** 219,228 ****
          sys.exit(2)
  
!     marginsfile = scoresfile = datafile = specfile = labelsfile = sample = iteration = None
      for opt,arg in opts:
! 	    if (opt == '--margins'):
! 		    marginsfile = arg
! 	    if (opt == '--scores'):
! 		    scoresfile = arg
  	    if (opt == '--spec'):
  		    specfile = arg
--- 221,228 ----
          sys.exit(2)
  
!     boostfile = datafile = specfile = labelsfile = sample = iteration = None
      for opt,arg in opts:
! 	    if (opt == '--boost-info'):
! 		    boostfile = arg
  	    if (opt == '--spec'):
  		    specfile = arg
***************
*** 237,258 ****
  	
  
!     if(marginsfile == None or scoresfile == None or datafile == None or specfile == None):
  	    print 'Need score, data, and spec file.'
  	    usage()
  	    sys.exit(2)
      
-     if(labelsfile == None):
- 	    labelsfile = datafile + '.labels'
- 	#if os.path.exists(labelsfile):
- 	#    print 'Using label file:', labelsfile
- 	#    # do nothing
- 	#else:
- 	    command = 'cat ' + datafile + ' | sed \'s/.*\(.1\);/\\1/g\' > ' + labelsfile
- 	    print command
- 	    os.system(command)
- 	    command = 'sed -i \'s/ 1/+1/g\' ' + labelsfile
- 	    print command
- 	    os.system(command)
- 
      print 'Reading data'
      f= open(datafile,'r')
--- 237,245 ----
  	
  
!     if(boostfile == None or datafile == None or specfile == None):
  	    print 'Need score, data, and spec file.'
  	    usage()
  	    sys.exit(2)
      
      print 'Reading data'
      f= open(datafile,'r')
***************
*** 260,301 ****
      f.close()
      
!     print 'Reading score values'
!     f= open(scoresfile,'r')
!     data= f.readlines()
!     f.close()
!     score_elements= int((string.split(data[0],'='))[2])
!     
!     print 'Reading margin values'
!     f= open(marginsfile,'r')
      data= f.readlines()
      f.close()
!     margin_elements= int((string.split(data[0],'='))[2])
      
!     print 'Reading labels'
!     f= open(labelsfile,'r')
!     labels= f.readlines()[-score_elements:]
!     f.close()
  
!     scores = []
!     margin_list = []
  
      iters = [1]
      iters[0] = len(data) / (margin_elements+1)
      if (iteration != None):
! 	    iters = map(int, iteration.split(','))
  	    for iter in iters:
! 		    margins = [float(x) for x in data[iter*(margin_elements+1)+1:(iter+1)*(margin_elements+1)]]
! 		    scores = [float(x) for x in data[iter*(score_elements+1)+1:(iter+1)*(score_elements+1)]]
  		    margin_list.append(margins)
      else:
! 	    margins = [float(x) for x in data[-margin_elements:]]
! 	    scores = [float(x) for x in data[-score_elements:]]
  	    margin_list.append(margins)
  
!     write_gnuplot(margin_list, iters, datafile)
! 
!     process_data(examples, labels, margin_list[len(margin_list)-1], scores, datafile, sample)
! 
  
      
  
--- 247,294 ----
      f.close()
      
!     print 'Reading boosting info'
!     f= open(boostfile,'r')
      data= f.readlines()
      f.close()
!     margin_elements= int((string.split(data[0],SEPARATOR))[1].split('=')[1])
      
!     def get_margin(line):
! 	    m = line.split(SEPARATOR)[1]
! 	    m.replace(']','')
! 	    m.replace(';','')
! 	    m.replace(SEPARATOR,'')
! 	    m.replace(' ','')
! 	    m.replace('\t','')
! 	    return float(m)
  
!     def get_label(line):
! 	    m = line.split(SEPARATOR)[-2]
! 	    m.replace(']','')
! 	    m.replace(';','')
! 	    m.replace(SEPARATOR,'')
! 	    m.replace(' ','')
! 	    m.replace('\t','')
! 	    return m
  
+     # if iterations are not specified on command line, we do the last iteration
      iters = [1]
      iters[0] = len(data) / (margin_elements+1)
+ 
+     margin_list = []
      if (iteration != None):
! 	    iters = map(int, [x.strip() for x in iteration.split(',')])
  	    for iter in iters:
! 		    lines = [x for x in data[iter*(margin_elements+1)+1:(iter+1)*(margin_elements+1)]]
! 		    margins = map(get_margin, lines)
  		    margin_list.append(margins)
      else:
! 	    lines = [x for x in data[-margin_elements:]]
! 	    margins = map(get_margin, lines)
  	    margin_list.append(margins)
  
!     labels = map(get_label, [x for x in data[-margin_elements:]])
!     process_data(examples, labels, margin_list[len(margin_list)-1], datafile, sample)
  
+     write_gnuplot(margin_list, iters, datafile)
      
  

Index: surfing.py
===================================================================
RCS file: /cvsroot/jboost/jboost/scripts/surfing.py,v
retrieving revision 1.2
retrieving revision 1.3
diff -C2 -d -r1.2 -r1.3
*** surfing.py	12 Jul 2007 16:07:26 -0000	1.2
--- surfing.py	23 Oct 2007 22:50:42 -0000	1.3
***************
*** 9,20 ****
  import math
  
  def usage():
  	print 'Usage: margin.py    '
! 	print '\t--surfing=filename  surfing file as output by jboost (-a -2 switch)'
! 	print '\t--scores=filename   scores file as output by jboost (-a -2 switch)'
! 	print '\t--data=filename     train/test filename'
! 	print '\t--spec=filename     spec filename'
! 	print '\t--booster=type      type={normal, brown}'
! 	print '\t--framerate=num     an integer specifying framerate'
  
  
--- 9,22 ----
  import math
  
+ SEPARATOR=':'
+ 
  def usage():
  	print 'Usage: margin.py    '
! 	print '\t--surfing=filename      surfing file as output by jboost (-a -2 switch)'
! 	print '\t--boost-info=filename   file as output by jboost (-a -2 switch)'
! 	print '\t--data=filename         train/test filename'
! 	print '\t--spec=filename         spec filename'
! 	print '\t--booster=type          type={AdaBoost, LogLossBoost, NormalBoost, BrownBoost}'
! 	print '\t--framerate=num         an integer specifying framerate'
  
  
***************
*** 39,46 ****
  	weights = []
  	s = c= c1 = c2 = theta = 0
! 	if(params[0] == 'brown'):
  		c = float(params[1])
  		s = float(params[2]) #time remaining
! 	elif(params[0] == 'normal'):
  		c = float(params[1])
  		s = c-float(params[2]) #time played
--- 41,49 ----
  	weights = []
  	s = c= c1 = c2 = theta = 0
! 	confidence = False
! 	if(params[0] == 'BrownBoost'):
  		c = float(params[1])
  		s = float(params[2]) #time remaining
! 	elif(params[0] == 'NormalBoost'):
  		c = float(params[1])
  		s = c-float(params[2]) #time played
***************
*** 50,53 ****
--- 53,57 ----
  		c2 = float(params[4])
  		theta = float(params[5])
+ 		confidence = params[6]
  		
  		
***************
*** 57,64 ****
  	for i in range(0,num_steps):
  		mu = var = 0
! 		if params[0] == 'brown': 
  			mu = -s
  			var = c
! 		elif params[0] == 'normal':
  			mu = theta - c1 * (math.exp(-s) - math.exp(-c))
  			var = c2 * (math.exp(-2*s) - math.exp(-2*c)) + 0.05
--- 61,68 ----
  	for i in range(0,num_steps):
  		mu = var = 0
! 		if params[0] == 'BrownBoost': 
  			mu = -s
  			var = c
! 		elif params[0] == 'NormalBoost':
  			mu = theta - c1 * (math.exp(-s) - math.exp(-c))
  			var = c2 * (math.exp(-2*s) - math.exp(-2*c)) + 0.05
***************
*** 67,71 ****
  		y = math.exp(-math.pow(x-mu,2)/(var)) * max_bin 
  		p = (1 - erf((x-mu)/math.sqrt(var)))/2
! 		p = min(2*p, 1)
  		weights.append((x,y,p))
  		x += step
--- 71,76 ----
  		y = math.exp(-math.pow(x-mu,2)/(var)) * max_bin 
  		p = (1 - erf((x-mu)/math.sqrt(var)))/2
! 		if (confidence):
! 			p = min(2*p, 1)
  		weights.append((x,y,p))
  		x += step
***************
*** 115,132 ****
  def paste_gnuplot(num_iterations, framerate): 
  	print 'Starting animated gif creation'
! 	os.system('convert -rotate  90 -delay 5 -loop 0 surfing*.eps surfing.gif')
  	print 'Finished gif creation'
  	print 'See surfing.gif for animation'
  
! line_compare = lambda x,y : cmp(float(string.split(x,'\t')[0]), float(string.split(y,'\t')[0]))
  
  def write_gnuplot(lines, iter, datafile, params):
  
  	lines.sort(line_compare);
! 
! 
! 	margins = [ float(string.split(x,'\t')[0])    for x in lines]
! 	weights = [ float(string.split(x,'\t')[1])    for x in lines]
! 	potentials = [ float(string.split(x,'\t')[2])    for x in lines]
  
  
--- 120,136 ----
  def paste_gnuplot(num_iterations, framerate): 
  	print 'Starting animated gif creation'
! 	os.system('convert -rotate  90 -delay 5 -loop 2 surfing*.eps surfing.gif')
  	print 'Finished gif creation'
  	print 'See surfing.gif for animation'
  
! 
  
  def write_gnuplot(lines, iter, datafile, params):
  
+ 	line_compare = lambda x,y : cmp(float(string.split(x,SEPARATOR)[1]), float(string.split(y,SEPARATOR)[1]))
  	lines.sort(line_compare);
! 	margins = [ float(string.split(x,SEPARATOR)[1])    for x in lines]
! 	weights = [ float(string.split(x,SEPARATOR)[2])    for x in lines]
! 	potentials = [ float(string.split(x,SEPARATOR)[3])    for x in lines]
  
  
***************
*** 155,162 ****
  	epsoutlines.append('set terminal post\n')
  	epsoutlines.append('set output \'surfing%05d.eps\'\n' % (iter))
! 	epsoutlines.append('set title "'+  params[0] +  'boost ' + datafile + ' Surfing: Time Remaining ' + str(params[2]) + '" font "Times,20"\n')
  	epsoutlines.append('set key left top\n')
  	epsoutlines.append('set yzeroaxis lt -1\n')
! 	if params[0]=='normal':
  		epsoutlines.append('set xrange [-1:1]\n')
  		epsoutlines.append('set yrange [0:1]\n')
--- 159,166 ----
  	epsoutlines.append('set terminal post\n')
  	epsoutlines.append('set output \'surfing%05d.eps\'\n' % (iter))
! 	epsoutlines.append('set title "'+  params[0] +  ' ' + datafile + ' Surfing: Time Remaining ' + str(params[2]) + '" font "Times,20"\n')
  	epsoutlines.append('set key left top\n')
  	epsoutlines.append('set yzeroaxis lt -1\n')
! 	if params[0]=='NormalBoost':
  		epsoutlines.append('set xrange [-1:1]\n')
  		epsoutlines.append('set yrange [0:1]\n')
***************
*** 173,177 ****
  				'"surfing_weight.dat" using 1:2 title "Potential" with lines \n')
  #				'delta(t) with lines \n')
! #	if params[0]=='normal':
  #		epsoutlines.append('set parametric\n')
  #		epsoutlines.append('set trange [0:1]\n')
--- 177,181 ----
  				'"surfing_weight.dat" using 1:2 title "Potential" with lines \n')
  #				'delta(t) with lines \n')
! #	if params[0]=='NormalBoost':
  #		epsoutlines.append('set parametric\n')
  #		epsoutlines.append('set trange [0:1]\n')
***************
*** 212,216 ****
  
      try:
!         opts, args= getopt.getopt(sys.argv[1:], '', ['surfing=', 'scores=','data=','spec=','labels=', 'iteration=', 'framerate=', 'booster=', 'sample'])
      except getopt.GetoptError, inst:
          print 'Received an illegal argument:', inst
--- 216,220 ----
  
      try:
!         opts, args= getopt.getopt(sys.argv[1:], '', ['surfing=', 'boost-info=','data=','spec=','labels=', 'iteration=', 'framerate=', 'booster=', 'sample'])
      except getopt.GetoptError, inst:
          print 'Received an illegal argument:', inst
***************
*** 218,231 ****
          sys.exit(2)
  
!     surffile = scoresfile = datafile = framerate = specfile = labelsfile = sample = iteration = booster = None
      for opt,arg in opts:
- 	    if (opt == '--surfing'):
- 		    surffile = arg
  	    if (opt == '--booster'):
  		    booster = arg
  	    if (opt == '--framerate'):
  		    framerate = int(arg)
! 	    if (opt == '--scores'):
! 		    scoresfile = arg
  	    if (opt == '--spec'):
  		    specfile = arg
--- 222,233 ----
          sys.exit(2)
  
!     boostfile = datafile = framerate = specfile = labelsfile = sample = iteration = booster = None
      for opt,arg in opts:
  	    if (opt == '--booster'):
  		    booster = arg
  	    if (opt == '--framerate'):
  		    framerate = int(arg)
! 	    if (opt == '--boost-info'):
! 		    boostfile = arg
  	    if (opt == '--spec'):
  		    specfile = arg
***************
*** 236,241 ****
  	
  
!     if(surffile == None or scoresfile == None or datafile == None or specfile == None):
! 	    print 'Need score, data, and spec file.'
  	    usage()
  	    sys.exit(2)
--- 238,243 ----
  	
  
!     if(boostfile == None or datafile == None or specfile == None):
! 	    print 'Need boosting.info, data, and spec file.'
  	    usage()
  	    sys.exit(2)
***************
*** 244,277 ****
  	    usage()
  	    sys.exit(2)
!     if(booster==None or (booster!='normal' and booster!='brown')):
! 	    print 'Only accepts `normal` and `brown` boost.'
  	    usage()
  	    sys.exit(2)
  	    
!     print 'Reading surfing file'
!     f= open(surffile,'r')
      data= f.readlines()
      f.close()
!     num_elements= int((string.split(data[0],'='))[2])
!     num_iterations = int(string.split(string.split(data[-num_elements-2], ',')[0], '=')[1])
  
!     os.system('rm surfing*.eps surfing*.png')
  
      for iter in range(0, num_iterations+1, framerate):
! 	    if (booster=='brown'):
! 		    total_time = string.split(data[iter*(num_elements+2)+1], ' ')[2]
! 		    time_left  = string.split(data[iter*(num_elements+2)+1], ' ')[3]
! 		    params = ('brown', total_time, time_left)
! 	    if (booster=='normal'):
! 		    total_time = string.split(data[iter*(num_elements+2)+1], ' ')[2]
! 		    time_left  = string.split(data[iter*(num_elements+2)+1], ' ')[3]
! 		    c1 = 1.818
! 		    c2 = 1
! 		    theta = 0.1
! 		    #c1  = string.split(data[iter*(num_elements+2)+1], ' ')[4]
! 		    #c2  = string.split(data[iter*(num_elements+2)+1], ' ')[5]
! 		    #theta  = string.split(data[iter*(num_elements+2)+1], ' ')[6]
! 		    params = ('normal', total_time, time_left, c1, c2, theta)
! 	    lines = [x   for x in data[iter*(num_elements+2)+2:(iter+1)*(num_elements+2)]]
  	    write_gnuplot(lines, iter, datafile, params)
  
--- 246,289 ----
  	    usage()
  	    sys.exit(2)
!     if(booster==None or (booster!='NormalBoost' and booster!='BrownBoost')):
! 	    print 'Only accepts `NormalBoost` and `BrownBoost` boost.'
  	    usage()
  	    sys.exit(2)
  	    
!     print 'Reading boosting.info file'
!     f= open(boostfile,'r')
      data= f.readlines()
      f.close()
!     num_elements= int((string.split(data[0],SEPARATOR)[1].split('=')[1]))
!     num_iterations = int(string.split(string.split(data[-num_elements-1], SEPARATOR)[0], '=')[1])
  
!     #os.system('rm surfing*.eps surfing*.png')
  
      for iter in range(0, num_iterations+1, framerate):
! 	    boost_params_str = string.split(data[iter*(num_elements+1)], SEPARATOR)[2].split('boosting_params=')[1]
! 	    if (booster=='BrownBoost'):
! 		    search_str = 'r='
! 		    total_time = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
! 		    search_str = 's='
! 		    time_left = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
! 		    params = ('BrownBoost', total_time, time_left)
! 	    if (booster=='NormalBoost'):
! 		    search_str = 'c='
! 		    total_time = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
! 		    search_str = 's='
! 		    time_left = boost_params_str[boost_params_str.index(search_str)+2 :].split(' ')[0]
! 		    search_str = 'c1='
! 		    c1 = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
! 		    search_str = 'c2='
! 		    c2 = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
! 		    search_str = 'theta='
! 		    theta = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
! 		    search_str = 'confidence='
! 		    confidence = boost_params_str[boost_params_str.index(search_str)+len(search_str) :].split(' ')[0]
! 		    use_conf = False
! 		    if confidence == 'true' or confidence == 'True' or confidence == '1':
! 			    use_conf = True
! 		    params = ('NormalBoost', total_time, time_left, c1, c2, theta, use_conf)
! 	    lines = [x   for x in data[iter*(num_elements+1)+1:(iter+1)*(num_elements+1)]]
  	    write_gnuplot(lines, iter, datafile, params)
  

Index: error.py
===================================================================
RCS file: /cvsroot/jboost/jboost/scripts/error.py,v
retrieving revision 1.3
retrieving revision 1.4
diff -C2 -d -r1.3 -r1.4
*** error.py	19 May 2007 01:57:56 -0000	1.3
--- error.py	23 Oct 2007 22:50:42 -0000	1.4
***************
*** 11,14 ****
--- 11,37 ----
  	print '\t--logaxis           should the axis be log-scaled (default: false)'
  	print '\t--bound             show the bound on training error'
+ 	print '\t--separate          separate the positive and negative examples'
+ 	print '\t--scores=score_file the score file (only needed if --separate used)'
+ 	print '\t--margins=marg_file the margin file (only needed if --separate used)'
+ 
+ def do_separate(score_filename, margin_filename):
+ 	print 'Parsing scores file'
+ 	f = open(score_filename,'r')
+ 	lines = f.readlines()
+ 	f.close()
+ 	
+ 	read_iter = False
+ 	data = []
+ 	for line in lines:
+ 		line = line.strip()
+ 		if (line[:8] =='End time'):
+ 			break
+ 		if (read_iter):
+ 			data.append(get_score(line))
+ 			
+ 
+     
+ 
+ 
  
  def main():
***************
*** 16,29 ****
  
      try:
! 	    opts, args= getopt.getopt(sys.argv[1:], '', ['info=','logaxis','bound'])
      except getopt.GetoptError:
  	    usage()
  	    sys.exit(2)
  	    
!     infofilename = logaxis = showbound = None
      for opt,arg in opts:
  	    if (opt == '--info'):
! 		    infofilename = arg
! 	    elif (opt == '--logaxis'):
  		    logaxis = True
  	    elif (opt == '--bound'):
--- 39,58 ----
  
      try:
! 	    opts, args= getopt.getopt(sys.argv[1:], '', ['info=','logaxis','bound','separate','scores=','margins='])
      except getopt.GetoptError:
  	    usage()
  	    sys.exit(2)
  	    
!     info_filename = logaxis = showbound = show_separate = margin_filename = score_filename = None
      for opt,arg in opts:
  	    if (opt == '--info'):
! 		    info_filename = arg
! 	    if (opt == '--margins'):
! 		    margin_filename = arg
! 	    if (opt == '--info'):
! 		    score_filename = arg
! 	    if (opt == '--separate'):
! 		    show_separate = True
! 	    if (opt == '--logaxis'):
  		    logaxis = True
  	    elif (opt == '--bound'):
***************
*** 31,44 ****
  	
  
!     if(infofilename == None):
! 	usage()
! 	sys.exit(2)
  
!     print 'Reading info file'
!     f = open(infofilename,'r')
      lines = f.readlines()
      f.close()
  
!     def get_data(line):
  	    list = line.split('\t')
  	    ret = {}
--- 60,80 ----
  	
  
!     if (show_separate and (margin_filename==None or score_filename==None)):
! 	    print 'ERROR: Need to specify score and margin file if showing positive and negative elements separately'
! 	    usage()
! 	    sys.exit(2)	    
! 	    
!     if(info_filename == None):
! 	    print 'ERROR: Need to specify info file'
! 	    usage()
! 	    sys.exit(2)
  
!     print 'Parsing info file'
!     f = open(info_filename,'r')
      lines = f.readlines()
      f.close()
  
! 
!     def get_info_data(line):
  	    list = line.split('\t')
  	    ret = {}
***************
*** 49,52 ****
--- 85,89 ----
  	    return ret
  
+ 
      read_iter = False
      data = []
***************
*** 57,61 ****
  		    break
  	    if (read_iter):
! 		    data.append(get_data(line))
  	    if (line=='iter \tbound \ttrain \ttest'):
  		    read_iter = True
--- 94,98 ----
  		    break
  	    if (read_iter):
! 		    data.append(get_info_data(line))
  	    if (line=='iter \tbound \ttrain \ttest'):
  		    read_iter = True
***************
*** 81,85 ****
      outlines.append('set xlabel "Iteration"\n')
      outlines.append('set ylabel "Error"\n')
!     outlines.append('set title "' + infofilename + ' Error"\n')
      if (logaxis):
  	    outlines.append('set logscale x 10\n')
--- 118,122 ----
      outlines.append('set xlabel "Iteration"\n')
      outlines.append('set ylabel "Error"\n')
!     outlines.append('set title "' + info_filename + ' Error"\n')
      if (logaxis):
  	    outlines.append('set logscale x 10\n')
***************
*** 92,100 ****
      outlines.append(out)
  
! 
!     f = open('error.gnuplot', 'w')
      f.writelines(outlines)
      f.close()
  
  if __name__ == "__main__":
      main()
--- 129,149 ----
      outlines.append(out)
  
!     pngfilename = 'error.gnuplot'
!     f = open(pngfilename, 'w')
      f.writelines(outlines)
      f.close()
  
+     os.system('gnuplot ' + pngfilename)
+ 
+ #####################################
+     #    Now do scores file for separate option
+ #####################################
+     
+     if show_separate:
+ 	    do_separate()
+ 
+ 
+ 
+ 
  if __name__ == "__main__":
      main()

[Jboost-cvs] jboost/src/jboost/controller Configuration.java, 1.6, 1.7 ControllerConfiguration.java, 1.2, 1.3

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/controller
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv5321/controller

Modified Files:
	Configuration.java ControllerConfiguration.java 
Log Message:
Cost sensitive params

Index: ControllerConfiguration.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/controller/ControllerConfiguration.java,v
retrieving revision 1.2
retrieving revision 1.3
diff -C2 -d -r1.2 -r1.3
*** ControllerConfiguration.java	27 May 2007 11:20:23 -0000	1.2
--- ControllerConfiguration.java	23 Oct 2007 22:46:32 -0000	1.3
***************
*** 55,58 ****
--- 55,66 ----
    public static final String YABA_C2="c2";
    public static final String YABA_THETA="theta";
+   public static final String YABA_POS_C="pos_r";
+   public static final String YABA_POS_C1="pos_c1";
+   public static final String YABA_POS_C2="pos_c2";
+   public static final String YABA_POS_THETA="pos_theta";
+   public static final String YABA_NEG_C="neg_r";
+   public static final String YABA_NEG_C1="neg_c1";
+   public static final String YABA_NEG_C2="neg_c2";
+   public static final String YABA_NEG_THETA="neg_theta";
    public static final String BROWN_BOOST_POTENTIAL="brownBoostPotential";
    
***************
*** 87,90 ****
--- 95,106 ----
      addOption(YABA_C2, getString("c2", null));
      addOption(YABA_THETA, getString("theta", null));
+     addOption(YABA_POS_C,"pos_r");
+     addOption(YABA_POS_C1,"pos_c1");
+     addOption(YABA_POS_C2,"pos_c2");
+     addOption(YABA_POS_THETA,"pos_theta");
+     addOption(YABA_NEG_C,"neg_r");
+     addOption(YABA_NEG_C1,"neg_c1");
+     addOption(YABA_NEG_C2,"neg_c2");
+     addOption(YABA_NEG_THETA,"neg_theta");
      addOption(BROWN_BOOST_POTENTIAL, getString("potential", null));
    }

Index: Configuration.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/controller/Configuration.java,v
retrieving revision 1.6
retrieving revision 1.7
diff -C2 -d -r1.6 -r1.7
*** Configuration.java	2 Oct 2007 20:36:43 -0000	1.6
--- Configuration.java	23 Oct 2007 22:46:32 -0000	1.7
***************
*** 47,51 ****
   * <p>
   *
!  * @author Nigel Duffy (rewritten by Aaron Arvey)
   */
  public class Configuration {
--- 47,51 ----
   * <p>
   *
!  * @author Nigel Duffy (parts rewritten by Aaron Arvey)
   */
  public class Configuration {

[Jboost-cvs] jboost/src/jboost/booster AbstractBooster.java, 1.5, 1.6 AdaBoost.java, 1.3, 1.4 BrownBoost.java, 1.7, 1.8 YabaBoost.java, 1.5, 1.6

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/booster
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv4913/booster

Modified Files:
	AbstractBooster.java AdaBoost.java BrownBoost.java 
	YabaBoost.java 
Log Message:
Cost sensitive boosting, new data output format

Index: AbstractBooster.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/AbstractBooster.java,v
retrieving revision 1.5
retrieving revision 1.6
diff -C2 -d -r1.5 -r1.6
*** AbstractBooster.java	18 Sep 2007 03:25:58 -0000	1.5
--- AbstractBooster.java	23 Oct 2007 22:45:40 -0000	1.6
***************
*** 67,75 ****
  
  	  if (result instanceof jboost.booster.YabaBoost) {
! 	      double c1 = Double.parseDouble(c.getString("c1", "1.0"));
! 	      double c2 = Double.parseDouble(c.getString("c2", "1.0"));
! 	      double theta = Double.parseDouble(c.getString("theta", "0.15"));
  	      jboost.booster.YabaBoost yaba = (jboost.booster.YabaBoost) result;
  	      yaba.setParams(c1,c2,theta);
  	      result = yaba;
  	  }
--- 67,90 ----
  
  	  if (result instanceof jboost.booster.YabaBoost) {
! 	      double c1=0, c2=0, theta=0;
! 	      double rpos=0, c1pos=0, c2pos=0, thetapos=0;
! 	      double rneg=0, c1neg=0, c2neg=0, thetaneg=0;
! 	      try {
! 		  c1 = Double.parseDouble(c.getString("c1", "Z1.0"));
! 		  c2 = Double.parseDouble(c.getString("c2", "Z1.0"));
! 		  theta = Double.parseDouble(c.getString("theta", "Z0.15"));
! 		  //c1 = Double.parseDouble(c.getString("pos_r", "Z1.0"));
! 		  //c1 = Double.parseDouble(c.getString("pos_c1", "Z1.0"));
! 		  //c2 = Double.parseDouble(c.getString("pos_c2", "Z1.0"));
! 		  //theta = Double.parseDouble(c.getString("pos_theta", "Z0.15"));
! 	      } catch (NumberFormatException e) {
! 		  String s = "Need to supply r, c1, c2, and theta!";
! 		  System.err.println(s);
! 		  throw new InstantiationException(s);
! 	      }
  	      jboost.booster.YabaBoost yaba = (jboost.booster.YabaBoost) result;
  	      yaba.setParams(c1,c2,theta);
+ 	      yaba.setCostSensitiveParams(rpos, c1pos, c2pos, thetapos, 
+ 					  rneg, c1neg, c2neg, thetaneg);
  	      result = yaba;
  	  }

Index: YabaBoost.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/YabaBoost.java,v
retrieving revision 1.5
retrieving revision 1.6
diff -C2 -d -r1.5 -r1.6
*** YabaBoost.java	2 Oct 2007 02:32:07 -0000	1.5
--- YabaBoost.java	23 Oct 2007 22:45:40 -0000	1.6
***************
*** 34,44 ****
    
      /**
-      * This is true if and only if we are cost sensitive boosting.  If
-      * this is set, then m_{pos,neg}c{1,2} and m_{pos,neg}Theta must
-      * be set.
-      */
-     protected boolean m_isCostSensitive;
- 
-     /**
       * Parameter for yaba that allows for decrease of potential
       * by translation of potential curve.  
--- 34,37 ----
***************
*** 135,140 ****
  
  
!     public void setCostSensativeParams(double pc1, double pc2, double ptheta, 
! 				       double nc1, double nc2, double ntheta) {
  	m_posc1 = pc1;
  	m_posc2 = pc2;
--- 128,134 ----
  
  
!     public void setCostSensitiveParams(double pc, double pc1, double pc2, double ptheta, 
! 				       double nc, double nc1, double nc2, double ntheta) {
! 	m_posc = pc;
  	m_posc1 = pc1;
  	m_posc2 = pc2;
***************
*** 142,145 ****
--- 136,140 ----
  	m_posOrigTheta = ptheta;
  
+ 	m_negc = pc;
  	m_negc1 = pc1;
  	m_negc2 = pc2;
***************
*** 183,187 ****
       * @see jboost.booster.BrownBoost#calc_constraints(int[][], double, double)
       */
!     protected ErfVars calc_constraints(double alpha, double t)
      {
  	ErfVars vars = new ErfVars();		
--- 178,182 ----
       * @see jboost.booster.BrownBoost#calc_constraints(int[][], double, double)
       */
!     protected ErfVars calc_constraints(double alpha, double t, int[] examples)
      {
  	ErfVars vars = new ErfVars();		
***************
*** 202,207 ****
  	double margin, orig_margin, step, new_margin, new_weight, new_pot, orig_pot;
  	int example;
! 	for (int i= 0; i < m_hypPredictions.length; i++) {
! 	    example = i;
  	    margin =  m_margins[example];
  	    orig_margin = margin;
--- 197,202 ----
  	double margin, orig_margin, step, new_margin, new_weight, new_pot, orig_pot;
  	int example;
! 	for (int i= 0; i < examples.length; i++) {
! 	    example = examples[i];
  	    margin =  m_margins[example];
  	    orig_margin = margin;
***************
*** 229,236 ****
  	    //System.out.println("aj: " + aj + ", dj: " + dj + ", dj^2/sd^2: " + (dj*dj/(sd*sd)) + ", bj: " + bj);
  	    //System.out.println("N(mu,sigma):" + mu + "," + sd + ", B:" + vars.B + ", E:" + vars.E);
! 	}		
  	vars.B /= totalWeight;
! 	vars.E /= m_hypPredictions.length;
! 	vars.Potential /= m_hypPredictions.length;
  	return vars;
      }
--- 224,231 ----
  	    //System.out.println("aj: " + aj + ", dj: " + dj + ", dj^2/sd^2: " + (dj*dj/(sd*sd)) + ", bj: " + bj);
  	    //System.out.println("N(mu,sigma):" + mu + "," + sd + ", B:" + vars.B + ", E:" + vars.E);
! 	}
  	vars.B /= totalWeight;
! 	vars.E /= examples.length;
! 	vars.Potential /= examples.length;
  	return vars;
      }
***************
*** 283,288 ****
       * @return alpha - An appropriate value of alpha that satisfies constraints.
       */
!     protected double solve_constraints(double hyp_err)
!     {
  	/*
  	 * If the game has a small amount of time remaining, quit now.
--- 278,282 ----
       * @return alpha - An appropriate value of alpha that satisfies constraints.
       */
!     protected double solve_constraints(double hyp_err, int[] examples) {
  	/*
  	 * If the game has a small amount of time remaining, quit now.
***************
*** 313,317 ****
  	double t_step = 0.1;
  	double t=0.3;
! 	double alpha=0;
  	  	  
  	/*
--- 307,311 ----
  	double t_step = 0.1;
  	double t=0.3;
! 	double alpha=0.1;
  	  	  
  	/*
***************
*** 386,390 ****
  		
  		// calculate constraints for values of alpha and t
! 		vars = calc_constraints (alpha, t);
  
  		// reverse alpha search direction
--- 380,384 ----
  		
  		// calculate constraints for values of alpha and t
! 		vars = calc_constraints (alpha, t, examples);
  
  		// reverse alpha search direction
***************
*** 533,537 ****
  
  
! 	if (m_hypPredictions.length != m_margins.length) {
  	    System.err.println("WARNING: m_hypPredictions is not the same length as the margins");
  	}
--- 527,531 ----
  
  
! 	if (m_hypPredictions.length != m_margins.length || m_hypPredictions.length != m_numExamples) {
  	    System.err.println("WARNING: m_hypPredictions is not the same length as the margins");
  	}
***************
*** 618,622 ****
  
      public String getParamString() {
!         String ret = String.format("s=%.4f,c=%.4f,c1=%.4f,c2=%.4f,theta=%.4f,confidence=%b", m_s, m_c, m_c1, m_c2, m_theta, USE_CONFIDENCE);
          return ret;
      }
--- 612,616 ----
  
      public String getParamString() {
!         String ret = String.format("YabaBoost s=%.4f c=%.4f c1=%.4f c2=%.4f theta=%.4f confidence=%b ", m_s, m_c, m_c1, m_c2, m_theta, USE_CONFIDENCE);
          return ret;
      }

Index: AdaBoost.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/AdaBoost.java,v
retrieving revision 1.3
retrieving revision 1.4
diff -C2 -d -r1.3 -r1.4
*** AdaBoost.java	13 Oct 2007 04:32:28 -0000	1.3
--- AdaBoost.java	23 Oct 2007 22:45:40 -0000	1.4
***************
*** 34,37 ****
--- 34,46 ----
    protected double[] m_potentials;
  
+   /** */
+   protected int[] m_posExamples;
+   /** */
+   protected int[] m_negExamples;
+   /** */
+   protected int m_numPosExamples;
+   /** */
+   protected int m_numNegExamples;
+ 
    /** sampling weights for the examples */
    protected double[] m_sampleWeights;
***************
*** 92,95 ****
--- 101,105 ----
          m_numExamples++;
          m_tmpList.add(new TmpData(index, (short) l, weight));
+         if (l==1) m_numPosExamples++;
        } else {
          failed= "AdaBoost.addExample received index " + index + ", when it expected index " +
***************
*** 139,143 ****
--- 149,157 ----
      m_sampleWeights= new double[m_numExamples];
      m_epsilon= m_smooth / m_numExamples;
+     m_posExamples = new int[m_numPosExamples];
+     m_numNegExamples = m_numExamples - m_numPosExamples;
+     m_negExamples = new int[m_numNegExamples];
  
+     int m_posIndex=0, m_negIndex=0;
      for (int i= 0; i < m_tmpList.size(); i++) {
        TmpData a= (TmpData) m_tmpList.get(i);
***************
*** 146,149 ****
--- 160,167 ----
        m_weights[index]= m_oldWeights[index]= defaultWeight;
        m_labels[index]= a.getLabel();
+       if (a.getLabel()==1)  
+         m_posExamples[m_posIndex++] = index; 
+       else
+         m_negExamples[m_negIndex++] = index;  
        m_sampleWeights[index]= a.getWeight();
      }

Index: BrownBoost.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/booster/BrownBoost.java,v
retrieving revision 1.7
retrieving revision 1.8
diff -C2 -d -r1.7 -r1.8
*** BrownBoost.java	2 Oct 2007 02:32:07 -0000	1.7
--- BrownBoost.java	23 Oct 2007 22:45:40 -0000	1.8
***************
*** 20,23 ****
--- 20,28 ----
      /** The total running time of the BrownBoost game. */
      protected double m_c;
+ 
+     /** In case we ever want to make BrownBoost cost sensitive.  Also useful for Yaba. */
+     protected double m_posc;
+     protected double m_negc;
+ 
      /** The time remaining in the BrownBoost game. */
      protected double m_s;
***************
*** 28,31 ****
--- 33,45 ----
      /** The last value of alpha */
      protected double m_lastAlpha;
+ 
+ 
+     /**
+      * This is true if and only if we are cost sensitive boosting.  If
+      * this is set, then m_{pos,neg}c{1,2} and m_{pos,neg}Theta must  
+      * be set in YabaBoost and m_c{pos,neg} must be set in BrownBoost.
+      */
+     protected boolean m_isCostSensitive;
+ 
    
      /** 
***************
*** 46,49 ****
--- 60,68 ----
      protected double m_totalPotential;
    
+     /** 
+      * Records the potentials.  Similar to m_margins and m_weights.
+      */
+     protected int[] m_examples;
+   
      /**
       * Default constructor just calls AdaBoost to 
***************
*** 57,62 ****
--- 76,83 ----
      public void finalizeData() {
  	super.finalizeData();
+ 	m_examples = new int[m_numExamples];
  	for (int i=0; i<m_numExamples; i++) {
  	    m_potentials[i] = calculatePotential(0,m_c);
+ 	    m_examples[i] = i;
  	}
      }
***************
*** 248,252 ****
  
    
!     protected double solve_constraints(double hyp_err)
      {   
  	if( m_s < 0.001){
--- 269,273 ----
  
    
!     protected double solve_constraints(double hyp_err, int[] examples)
      {   
  	if( m_s < 0.001){
***************
*** 265,320 ****
  	double new_t = 0.0;
  
- 	/*  
- 	int k = 0;
- 	while(k < 200){
- 	    k += 1;
- 			
- 	    vars = calc_constraints(alpha, t);
- 
- 	    double det = 2*(vars.V*vars.W  -  vars.U*vars.B);
- 	    double EPS = 0.0001;
- 
- 	    // If correlation and difference in potential are small,
- 	    // then we're done!
- 	    if (vars.B < EPS && vars.E < EPS){
- 		System.out.println("Solved constrains by minimizing B and E.");
- 		break;
- 	    }
- 	    
- 	    if (Math.abs(det) < EPS){
- 		System.out.println("DETERMINANT IS TOO SMALL -- SUGGESTS JACOBIAN IS ALMOST SINGULAR!!!");
- 	    }
- 			
- 	    new_alpha =  alpha + (c*vars.W*vars.B + Math.sqrt(Math.PI*c)*vars.U*vars.E) / det;
- 	    new_t     =  t     + (c*vars.B*vars.B + Math.sqrt(Math.PI*c)*vars.V*vars.E) / det;
- 				    		
- 	    // if newton raphson succeeded, exit loop
- 	    EPS = 0.001;
- 	    if (vars.E < EPS && vars.W < EPS){
- 		System.out.println("Solved constrains by minimizing E and W.");
- 		break;
- 	    }
- 		          
- 	    EPS = 0.0001;
- 	    if (Math.abs(alpha-new_alpha) < EPS){
- 		System.out.println("Solved constraints by similar old and new alpha.");
- 		break;
- 	    }
- 		            
- 	    alpha = new_alpha;
- 	    t = new_t;
- 	}
- 		    
- 	if (k>=100){
- 	    System.err.println("WE DIDN'T ACTUAL FINISH ANYTHING!!!  WE JUST TIMED OUT!!!");	    	
- 	}
- 
- 	double nr_alpha = alpha;
- 	double nr_t = t;
- 	System.out.println("Newton-Raphson gives alpha=" + nr_alpha + ", t=" + nr_t);
- 	*/
- 
- 
- 
  	// try binary search
  	// find the maximal value for t for which there exists a value
--- 286,289 ----
***************
*** 416,434 ****
      }
  
-     /*
-     public String surfingData() {
- 	StringBuffer ret = new StringBuffer("");
- 	ret.append(String.format("BrownBoost Params: %.4f %.4f\n", m_c, m_s));
- 	for (int i=0; i<m_margins.length; i++){
- 	    ret.append(String.format("%.4f\t%.4f\t%.4f\n", m_margins[i], m_weights[i], m_potentials[i]));
- 	}
- 	return ret.toString();
-     }
-     */
- 
- 
- 
      public String getParamString() {
! 	String ret = String.format("BrownBoost Params: %.4f %.4f", m_c, m_s);
  	return ret;
      }
--- 385,390 ----
      }
  
      public String getParamString() {
! 	String ret = String.format("BrownBoost r=%.4f s=%.4f ", m_c, m_s);
  	return ret;
      }
***************
*** 529,537 ****
  	/** compute the binary prediction associated with this bag */
  	public BinaryPrediction calcPrediction(double alpha) {
! 	    BinaryPrediction ret = new BinaryPrediction(m_w[1] > m_w[0] ? 1.0 : -1.0 );
  	    ret.scale(alpha);
  	    return ret;
  	}
        
        
  	/** Place holder to ensure that this function is not used in BrownBoost. */
--- 485,507 ----
  	/** compute the binary prediction associated with this bag */
  	public BinaryPrediction calcPrediction(double alpha) {
! 	    BinaryPrediction ret;
! 	    ret = new BinaryPrediction(m_w[1] > m_w[0] ? 1.0 : -1.0 );
  	    ret.scale(alpha);
  	    return ret;
  	}
        
+ 	/** compute the binary prediction associated with this bag */
+ 	public BinaryPrediction calcPrediction(double posAlpha, double negAlpha) {
+ 	    BinaryPrediction ret;
+ 	    if (m_w[1] > m_w[0]) {
+ 		ret = new BinaryPrediction(1.0);
+ 		ret.scale(posAlpha);
+ 	    } else {
+ 		ret = new BinaryPrediction(-1.0);
+ 		ret.scale(negAlpha);
+ 	    } 
+ 	    return ret;
+ 	}
+       
        
  	/** Place holder to ensure that this function is not used in BrownBoost. */
***************
*** 677,684 ****
  	// initial guess for alpha.
  	double gamma = getHypErr(bags, exampleIndex);
! 	double alpha = solve_constraints(gamma);
! 	
! 	for (int i= 0; i < bags.length; i++) {
! 	    p[i]= ((BrownBag) bags[i]).calcPrediction(alpha);
  	}
  	return p;
--- 647,666 ----
  	// initial guess for alpha.
  	double gamma = getHypErr(bags, exampleIndex);
! 
! 	if (m_isCostSensitive) {
! 	    System.out.println("Solving positive example constraints");
! 	    double posAlpha = solve_constraints(gamma, m_posExamples);
! 	    System.out.println("Solving negative example constraints");
! 	    double negAlpha = solve_constraints(gamma, m_negExamples);
! 	    for (int i= 0; i < bags.length; i++) {
! 		p[i]= ((BrownBag) bags[i]).calcPrediction(posAlpha, negAlpha);
! 		System.out.println("p[" + i + "] = " + p[i]);
! 	    }
! 	} else {
! 	    double alpha = solve_constraints(gamma, m_examples);
! 	    for (int i= 0; i < bags.length; i++) {
! 		p[i]= ((BrownBag) bags[i]).calcPrediction(alpha);
! 		System.out.println("p[" + i + "] = " + p[i]);
! 	    }
  	}
  	return p;

[Jboost-cvs] jboost/src/jboost/monitor Monitor.java,1.7,1.8

[Aaron A. <aa...@us...>]

Update of /cvsroot/jboost/jboost/src/jboost/monitor
In directory sc8-pr-cvs6.sourceforge.net:/tmp/cvs-serv4913/monitor

Modified Files:
	Monitor.java 
Log Message:
Cost sensitive boosting, new data output format

Index: Monitor.java
===================================================================
RCS file: /cvsroot/jboost/jboost/src/jboost/monitor/Monitor.java,v
retrieving revision 1.7
retrieving revision 1.8
diff -C2 -d -r1.7 -r1.8
*** Monitor.java	18 Sep 2007 03:25:59 -0000	1.7
--- Monitor.java	23 Oct 2007 22:45:40 -0000	1.8
***************
*** 27,30 ****
--- 27,34 ----
      private String outputStem;
  
+     private static final String FIELD_SEPARATOR = ": ";
+     private static final char SEPARATOR = ',';
+ 
+ 
      /** a high-level log of the program's progress. */
      private String infoFilename;
***************
*** 180,186 ****
      private void logBoostingTrainTest(PrintWriter boostingStream, ExampleSet tSet, int iter, Predictor combined, Predictor base) {
  	// Output the training data
! 	boostingStream.println("iteration=" + iter + 
! 			       ", elements=" + tSet.size() +
! 			       ", boosting_params=" + m_booster.getParamString()
  			       );
  	
--- 184,190 ----
      private void logBoostingTrainTest(PrintWriter boostingStream, ExampleSet tSet, int iter, Predictor combined, Predictor base) {
  	// Output the training data
! 	boostingStream.println("iteration=" + iter + FIELD_SEPARATOR + 
! 			       "elements=" + tSet.size() + FIELD_SEPARATOR + 
! 			       "boosting_params=" + m_booster.getParamString() + FIELD_SEPARATOR 
  			       );
  	
***************
*** 196,200 ****
  	}
  
! 	NumberFormat f= new DecimalFormat("0.0000");
  	double[] tmp = null;
  	Boolean[] labeltmp = null;
--- 200,204 ----
  	}
  
! 	NumberFormat f= new DecimalFormat("0.00000");
  	double[] tmp = null;
  	Boolean[] labeltmp = null;
***************
*** 202,250 ****
  	for (int i=0; i < tMargin.size(); i++) {
  	    // output the example number
! 	    boostingStream.print("[" +i + "]; ");
  
  	    // output the margins
- 	    boostingStream.print("[");
  	    tmp = ((double[]) tMargin.get(i));
  	    for (j= 0; j < tmp.length; j++){
  		boostingStream.print(f.format(tmp[j]));
  		if (j != tmp.length -1)
! 		    boostingStream.print(",");
  	    }
! 	    boostingStream.print("]; ");
  	    
  
  	    // output the scores
- 	    boostingStream.print("[");
  	    tmp = ((double[]) tScores.get(i));
  	    for (j= 0; j < tmp.length; j++){
  		boostingStream.print(f.format(tmp[j]));
  		if (j != tmp.length -1)
! 		    boostingStream.print(",");
  	    }
! 	    boostingStream.print("]; ");
! 	    
  
  
  	    if (boostingStream.equals(trainBoostingStream)) {
- 		
  		// output the weights
- 		boostingStream.print("[");
  		for (j= 0; j < tWeights[i].length; j++){
  		    boostingStream.print(f.format(tWeights[i][j]));
  		    if (j != tmp.length -1)
! 			boostingStream.print(",");
  		}
! 		boostingStream.print("]; ");
  		
  		
  		// output the potentials
- 		boostingStream.print("[");
  		for (j= 0; j < tPotentials[i].length; j++){
  		    boostingStream.print(f.format(tPotentials[i][j]));
  		    if (j != tmp.length -1)
! 			boostingStream.print(",");
  		}
! 		boostingStream.print("]; ");
  		
  		
--- 206,249 ----
  	for (int i=0; i < tMargin.size(); i++) {
  	    // output the example number
! 	    boostingStream.print("" + i + FIELD_SEPARATOR);
  
  	    // output the margins
  	    tmp = ((double[]) tMargin.get(i));
  	    for (j= 0; j < tmp.length; j++){
  		boostingStream.print(f.format(tmp[j]));
  		if (j != tmp.length -1)
! 		    boostingStream.print(SEPARATOR);
  	    }
! 	    boostingStream.print(FIELD_SEPARATOR);
  	    
  
  	    // output the scores
  	    tmp = ((double[]) tScores.get(i));
  	    for (j= 0; j < tmp.length; j++){
  		boostingStream.print(f.format(tmp[j]));
  		if (j != tmp.length -1)
! 		    boostingStream.print(SEPARATOR);
  	    }
! 	    boostingStream.print(FIELD_SEPARATOR);
  
  
  	    if (boostingStream.equals(trainBoostingStream)) {
  		// output the weights
  		for (j= 0; j < tWeights[i].length; j++){
  		    boostingStream.print(f.format(tWeights[i][j]));
  		    if (j != tmp.length -1)
! 			boostingStream.print(SEPARATOR);
  		}
! 		boostingStream.print(FIELD_SEPARATOR);
! 		
  		
  		
  		// output the potentials
  		for (j= 0; j < tPotentials[i].length; j++){
  		    boostingStream.print(f.format(tPotentials[i][j]));
  		    if (j != tmp.length -1)
! 			boostingStream.print(SEPARATOR);
  		}
! 		boostingStream.print(FIELD_SEPARATOR);
  		
  		
***************
*** 253,265 ****
  
  	    // output the labels
- 	    boostingStream.print("[");
  	    labeltmp = ((Boolean[]) tLabelIndices.get(i));
  	    for (j= 0; j < labeltmp.length; j++){
  		boostingStream.print(labeltmp[j].booleanValue() ? "+1" : "-1");
  		if (j != tmp.length -1)
! 		    boostingStream.print(",");
  	    }
! 	    boostingStream.print("]; ");
! 
  	    boostingStream.println("");
  	}
--- 252,262 ----
  
  	    // output the labels
  	    labeltmp = ((Boolean[]) tLabelIndices.get(i));
  	    for (j= 0; j < labeltmp.length; j++){
  		boostingStream.print(labeltmp[j].booleanValue() ? "+1" : "-1");
  		if (j != tmp.length -1)
! 		    boostingStream.print(SEPARATOR);
  	    }
! 	    boostingStream.print(FIELD_SEPARATOR);
  	    boostingStream.println("");
  	}