Made the order of constructor arguments in the various overloads

for the assignment_function and sequence_labeler consistent.

dlib/svm/assignment_function.h

@@ -54,8 +54,8 @@ namespace dlib
         }
 
         assignment_function(
-            const feature_extractor& fe_,
-            const matrix<double,0,1>& weights_
+            const matrix<double,0,1>& weights_,
+            const feature_extractor& fe_
         ) :
             fe(fe_),
             weights(weights_),
@@ -63,7 +63,7 @@ namespace dlib
         {
             // make sure requires clause is not broken
             DLIB_ASSERT(fe_.num_features() == static_cast<unsigned long>(weights_.size()),
-                "\t assignment_function::assignment_function(fe_,weights_)"
+                "\t assignment_function::assignment_function(weights_,fe_)"
                 << "\n\t These sizes should match"
                 << "\n\t fe_.num_features(): " << fe_.num_features() 
                 << "\n\t weights_.size():    " << weights_.size() 
@@ -72,8 +72,8 @@ namespace dlib
         }
 
         assignment_function(
-            const feature_extractor& fe_,
             const matrix<double,0,1>& weights_,
+            const feature_extractor& fe_,
             bool force_assignment_
         ) :
             fe(fe_),
@@ -82,7 +82,7 @@ namespace dlib
         {
             // make sure requires clause is not broken
             DLIB_ASSERT(fe_.num_features() == static_cast<unsigned long>(weights_.size()),
-                "\t assignment_function::assignment_function(fe_,weights_,force_assignment_)"
+                "\t assignment_function::assignment_function(weights_,fe_,force_assignment_)"
                 << "\n\t These sizes should match"
                 << "\n\t fe_.num_features(): " << fe_.num_features() 
                 << "\n\t weights_.size():    " << weights_.size() 
@@ -209,7 +209,7 @@ namespace dlib
         deserialize(weights, in);
         deserialize(force_assignment, in);
 
-        item = assignment_function<feature_extractor>(fe, weights, force_assignment);
+        item = assignment_function<feature_extractor>(weights, fe, force_assignment);
     }
 
 // ----------------------------------------------------------------------------------------

dlib/svm/assignment_function_abstract.h

@@ -161,8 +161,8 @@ namespace dlib
         !*/
 
         assignment_function(
-            const feature_extractor& fe,
-            const matrix<double,0,1>& weights
+            const matrix<double,0,1>& weights,
+            const feature_extractor& fe
         );
         /*!
             requires
@@ -174,8 +174,8 @@ namespace dlib
         !*/
 
         assignment_function(
-            const feature_extractor& fe,
             const matrix<double,0,1>& weights,
+            const feature_extractor& fe,
             bool force_assignment
         );
         /*!

dlib/svm/sequence_labeler.h

@@ -202,15 +202,15 @@ namespace dlib
         }
 
         sequence_labeler(
-            const feature_extractor& fe_,
-            const matrix<double,0,1>& weights_
+            const matrix<double,0,1>& weights_,
+            const feature_extractor& fe_
         ) :
             fe(fe_),
             weights(weights_)
         {
             // make sure requires clause is not broken
             DLIB_ASSERT(fe_.num_features() == static_cast<unsigned long>(weights_.size()),
-                "\t sequence_labeler::sequence_labeler(fe_,weights_)"
+                "\t sequence_labeler::sequence_labeler(weights_,fe_)"
                 << "\n\t These sizes should match"
                 << "\n\t fe_.num_features(): " << fe_.num_features() 
                 << "\n\t weights_.size():    " << weights_.size() 
@@ -294,7 +294,7 @@ namespace dlib
         deserialize(fe, in);
         deserialize(weights, in);
 
-        item = sequence_labeler<feature_extractor>(fe, weights);
+        item = sequence_labeler<feature_extractor>(weights, fe);
     }
 
 // ----------------------------------------------------------------------------------------

dlib/svm/sequence_labeler_abstract.h

@@ -221,8 +221,8 @@ namespace dlib
         !*/
 
         sequence_labeler(
-            const feature_extractor& fe,
-            const matrix<double,0,1>& weights
+            const matrix<double,0,1>& weights,
+            const feature_extractor& fe
         ); 
         /*!
             requires

dlib/svm/structural_assignment_trainer.h

@@ -185,7 +185,7 @@ namespace dlib
 
             solver(prob, weights);
 
-            return assignment_function<feature_extractor>(fe,weights,force_assignment);
+            return assignment_function<feature_extractor>(weights,fe,force_assignment);
 
         }
 

dlib/svm/structural_sequence_labeling_trainer.h

@@ -182,7 +182,7 @@ namespace dlib
             prob.set_max_cache_size(max_cache_size);
             solver(prob, weights);
 
-            return sequence_labeler<feature_extractor>(fe,weights);
+            return sequence_labeler<feature_extractor>(weights,fe);
         }
 
     private:

dlib/svm/structural_svm_assignment_problem_abstract.h

@@ -57,7 +57,7 @@ namespace dlib
                   given labels.  In particular, it attempts to learn to predict labels[i] 
                   based on samples[i].  Or in other words, this object can be used to learn 
                   a parameter vector, w, such that an assignment_function declared as:
-                    assignment_function<feature_extractor> assigner(fe,w,force_assignment)
+                    assignment_function<feature_extractor> assigner(w,fe,force_assignment)
                   results in an assigner object which attempts to compute the following mapping:
                     labels[i] == labeler(samples[i])
                 - This object will use num_threads threads during the optimization 

dlib/svm/structural_svm_sequence_labeling_problem_abstract.h

@@ -57,7 +57,7 @@ namespace dlib
                   given labels.  In particular, it attempts to learn to predict labels[i] 
                   based on samples[i].  Or in other words, this object can be used to learn 
                   a parameter vector, w, such that a sequence_labeler declared as:
-                    sequence_labeler<feature_extractor> labeler(fe,w)
+                    sequence_labeler<feature_extractor> labeler(w,fe)
                   results in a labeler object which attempts to compute the following mapping:
                     labels[i] == labeler(samples[i])
                 - This object will use num_threads threads during the optimization