DTBOblique: multi-class bugfix

classifier/fpclassifier/randomforest/DTBOblique.cpp

@@ -93,17 +93,14 @@ bool DTBOblique::entropyLeftRight (
     return true;
 }
 
-void DTBOblique::adaptDataAndLabelForMultiClass (
+bool DTBOblique::adaptDataAndLabelForMultiClass (
         const int posClass,
         const int negClass,
         NICE::Matrix & matX,
-        NICE::Vector & vecY,
-        bool & posHasExamples,
-        bool & negHasExamples )
+        NICE::Vector & vecY )
 {
-    int posCount = 0;
-    int negCount = 0;
-    int outCount = 0;
+    bool posHasExamples = false;
+    bool negHasExamples = false;
 
     // One-vs-one: Transforming into {-1,0,+1} problem
     if ( useOneVsOne )
@@ -113,19 +110,16 @@ void DTBOblique::adaptDataAndLabelForMultiClass (
             {
                 vecY[i] = 1.0;
                 posHasExamples = true;
-                posCount++;
             }
             else if ( vecY[i] == negClass )
             {
                 vecY[i] = -1.0;
                 negHasExamples = true;
-                negCount++;
             }
             else
             {
                 vecY[i] = 0.0;
                 matX.setRow( i, NICE::Vector( matX.cols(), 0.0 ) );
-                outCount++;
             }
         }
     // One-vs-all: Transforming into {-1,+1} problem
@@ -136,16 +130,18 @@ void DTBOblique::adaptDataAndLabelForMultiClass (
             {
                 vecY[i] = 1.0;
                 posHasExamples = true;
-                posCount++;
             }
             else
             {
                 vecY[i] = -1.0;
                 negHasExamples = true;
-                negCount++;
             }
         }
 
+    if ( posHasExamples && negHasExamples )
+        return true;
+    else
+        return false;
 }
 
 /** refresh data matrix X and label vector y */
@@ -154,7 +150,8 @@ void DTBOblique::getDataAndLabel(
         const Examples &examples,
         const std::vector<int> &examples_selection,
         NICE::Matrix & matX,
-        NICE::Vector & vecY )
+        NICE::Vector & vecY,
+        NICE::Vector & weights )
 {
     ConvolutionFeature *f = (ConvolutionFeature*)fp.begin()->second;
     int amountParams = f->getParameterLength();
@@ -162,6 +159,7 @@ void DTBOblique::getDataAndLabel(
 
     NICE::Matrix X(amountExamples, amountParams, 0.0 );
     NICE::Vector y(amountExamples, 0.0);
+    NICE::Vector w(amountExamples, 1.0);
 
     int matIndex = 0;
     for ( vector<int>::const_iterator si = examples_selection.begin();
@@ -173,17 +171,19 @@ void DTBOblique::getDataAndLabel(
 
         NICE::Vector pixelRepr = f->getFeatureVector( &ce );
 
-        double label = p.first;// * ce.weight;
+        double label = p.first;
         pixelRepr *= ce.weight;
 
-        y.set( matIndex, label );
-        X.setRow(matIndex,pixelRepr);
+        w.set    ( matIndex, ce.weight );
+        y.set    ( matIndex, label );
+        X.setRow ( matIndex, pixelRepr );
 
         matIndex++;
     }
 
     matX = X;
     vecY = y;
+    weights = w;
 }
 
 void DTBOblique::regularizeDataMatrix(
@@ -375,8 +375,8 @@ DecisionNode *DTBOblique::buildRecursive(
 
     // Creating data matrix X and label vector y
     NICE::Matrix X;
-    NICE::Vector y, beta;
-    getDataAndLabel( fp, examples, examples_selection, X, y );
+    NICE::Vector y, beta, weights;
+    getDataAndLabel( fp, examples, examples_selection, X, y, weights );
 
     // Transforming into multi-class problem
     for ( int posClass = 0; posClass <= maxClassNo; posClass++ )
@@ -389,14 +389,13 @@ DecisionNode *DTBOblique::buildRecursive(
             NICE::Vector yCur = y;
             NICE::Matrix XCur = X;
 
-            bool posHasExamples = false;
-            bool negHasExamples = false;
+            bool hasExamples = adaptDataAndLabelForMultiClass(
+                posClass, negClass, XCur, yCur );
 
-            adaptDataAndLabelForMultiClass(
-                posClass, negClass, XCur, yCur, posHasExamples, negHasExamples );
+            yCur *= weights;
 
             // are there examples for positive and negative class?
-            if ( !posHasExamples || !negHasExamples ) continue;
+            if ( !hasExamples ) continue;
 
             // one-vs-all setting: only one iteration for inner loop
             if ( !useOneVsOne && gotInnerIteration ) continue;
@@ -465,7 +464,7 @@ DecisionNode *DTBOblique::buildRecursive(
     }
 
 #ifdef DEBUGTREE
-    node->f->store( std::cerr );
+//    node->f->store( std::cerr );
     std::cerr << std::endl;
     std::cerr << "DTBOblique: Information Gain: " << bestSplitInfo.informationGain
               << ", Left Entropy: " <<  bestSplitInfo.entropyLeft << ", Right Entropy: "
@@ -482,10 +481,10 @@ DecisionNode *DTBOblique::buildRecursive(
             distribution_left_sparse[k] = l;
         if ( r != 0 )
             distribution_right_sparse[k] = r;
-#ifdef DEBUGTREE
-        std::cerr << "DTBOblique: Split of Class " << k << " ("
-                  << l << " <-> " << r << ") " << std::endl;
-#endif
+//#ifdef DEBUGTREE
+//        std::cerr << "DTBOblique: Split of Class " << k << " ("
+//                  << l << " <-> " << r << ") " << std::endl;
+//#endif
     }
 
     //TODO

classifier/fpclassifier/randomforest/DTBOblique.h

@@ -81,16 +81,14 @@ class DTBOblique : public DecisionTreeBuilder
      * @param negClass negative class number
      * @param matX adapted data matrix
      * @param vecY adapted label vector
-     * @param posHasExamples whether positive class has examples or not
-     * @param negHasExamples whether negative class has examples or not
+     * @param weights example weights
+     * @return whether positive and negative classes have examples or not
      */
-    void adaptDataAndLabelForMultiClass (
+    bool adaptDataAndLabelForMultiClass (
             const int posClass,
             const int negClass,
             NICE::Matrix & matX,
-            NICE::Vector & vecY,
-            bool & posHasExamples,
-            bool & negHasExamples );
+            NICE::Vector & vecY );
 
     /**
     * @brief get data matrix X and label vector y
@@ -105,7 +103,8 @@ class DTBOblique : public DecisionTreeBuilder
             const Examples &examples,
             const std::vector<int> & examples_selection,
             NICE::Matrix &matX,
-            NICE::Vector &vecY );
+            NICE::Vector &vecY,
+            NICE::Vector &weights );
 
     /**
      * @brief return a regularization matrix of size (dimParams)x(dimParams)