In a recent commit I added per label bias features but forgot to exclude them

from the non-negative feature learning range.  This change fixes that.

dlib/svm/sequence_segmenter.h

@@ -55,8 +55,8 @@ namespace dlib
                 else
                 {
                     // We make everything non-negative except for the label transition
-                    // features.
-                    return num_features() - NL*NL;
+                    // and bias features.
+                    return num_features() - NL*NL - NL;
                 }
             }
 

dlib/svm/sequence_segmenter_abstract.h

@@ -190,11 +190,11 @@ namespace dlib
         // the previous label. 
         const static bool use_high_order_features = true;
 
-        // You use a tool like the structural_sequence_segmentation_trainer to learn the
-        // weight vector needed by a sequence_segmenter.  You can tell the trainer to force
-        // all the elements of the weight vector corresponding to ZI() to be non-negative.
-        // This is all the elements of w except for the elements corresponding to the label
-        // transition indicator features.  To do this, just set allow_negative_weights to false.  
+        // You use a tool like the structural_sequence_segmentation_trainer to learn the weight
+        // vector needed by a sequence_segmenter.  You can tell the trainer to force all the
+        // elements of the weight vector corresponding to ZI() to be non-negative.  This is all
+        // the elements of w except for the elements corresponding to the label transition and
+        // bias indicator features.  To do this, just set allow_negative_weights to false.  
         const static bool allow_negative_weights = true;
 
 

dlib/test/sequence_segmenter.cpp

@@ -243,9 +243,9 @@ namespace
 
         long N;
         if (use_BIO_model)
-            N = 3*3;
+            N = 3*3+3;
         else
-            N = 5*5;
+            N = 5*5+5;
         const double min_normal_weight = min(colm(labeler2.get_weights(), 0, labeler2.get_weights().size()-N));
         const double min_trans_weight = min(labeler2.get_weights());
         dlog << LINFO << "min_normal_weight: " << min_normal_weight;