All I did in this change was rename calls to array_to_matrix(),

vector_to_matrix(), pointer_to_column_vector(), and pointer_to_matrix() to
mat() (in addition to adding a few more mat() overloads for certain things).

dlib/svm/structural_svm_sequence_labeling_problem.h

@@ -181,7 +181,7 @@ namespace dlib
             matrix<unsigned long,0,1> candidate_labeling; 
             for (unsigned long i = 0; i < sample.size(); ++i)
             {
-                candidate_labeling = rowm(vector_to_matrix(label), range(i, std::max((int)i-order,0)));
+                candidate_labeling = rowm(mat(label), range(i, std::max((int)i-order,0)));
 
                 fe_helpers::get_feature_vector(psi,fe,sample,candidate_labeling, i);
             }

dlib/svm/svm.h

@@ -48,7 +48,7 @@ namespace dlib
         const U& x_labels
     )
     {
-        return is_learning_problem_impl(vector_to_matrix(x), vector_to_matrix(x_labels));
+        return is_learning_problem_impl(mat(x), mat(x_labels));
     }
 
 // ----------------------------------------------------------------------------------------
@@ -93,7 +93,7 @@ namespace dlib
         const U& x_labels
     )
     {
-        return is_binary_classification_problem_impl(vector_to_matrix(x), vector_to_matrix(x_labels));
+        return is_binary_classification_problem_impl(mat(x), mat(x_labels));
     }
 
 // ----------------------------------------------------------------------------------------
@@ -171,8 +171,8 @@ namespace dlib
     )
     {
         return test_binary_decision_function_impl(dec_funct,
-                                 vector_to_matrix(x_test),
-                                 vector_to_matrix(y_test));
+                                 mat(x_test),
+                                 mat(y_test));
     }
 
 // ----------------------------------------------------------------------------------------
@@ -256,7 +256,7 @@ namespace dlib
         {
             for (unsigned long i = 0; i < samples.size(); ++i)
             {
-                const unsigned long N = sum(vector_to_matrix(labels[i]) != -1);
+                const unsigned long N = sum(mat(labels[i]) != -1);
                 if (std::min(samples[i].first.size(), samples[i].second.size()) != N)
                     return false;
             }
@@ -418,8 +418,8 @@ namespace dlib
     )
     {
         return cross_validate_trainer_impl(trainer,
-                                           vector_to_matrix(x),
-                                           vector_to_matrix(y),
+                                           mat(x),
+                                           mat(y),
                                            folds);
     }
 
@@ -600,8 +600,8 @@ namespace dlib
             );
 
         // count the number of positive and negative examples
-        const long num_pos = (long)sum(vector_to_matrix(y) > 0);
-        const long num_neg = (long)sum(vector_to_matrix(y) < 0);
+        const long num_pos = (long)sum(mat(y) > 0);
+        const long num_neg = (long)sum(mat(y) < 0);
 
         // figure out how many positive and negative examples we will have in each fold
         const long num_pos_test_samples = num_pos/folds; 

dlib/svm/svm_abstract.h

@@ -30,8 +30,8 @@ namespace dlib
     );
     /*!
         requires
-            - T == a matrix or something convertible to a matrix via vector_to_matrix()
-            - U == a matrix or something convertible to a matrix via vector_to_matrix()
+            - T == a matrix or something convertible to a matrix via mat()
+            - U == a matrix or something convertible to a matrix via mat()
         ensures
             - returns true if all of the following are true and false otherwise:
                 - is_col_vector(x) == true
@@ -50,8 +50,8 @@ namespace dlib
     );
     /*!
         requires
-            - T == a matrix or something convertible to a matrix via vector_to_matrix()
-            - U == a matrix or something convertible to a matrix via vector_to_matrix()
+            - T == a matrix or something convertible to a matrix via mat()
+            - U == a matrix or something convertible to a matrix via mat()
         ensures
             - returns true if all of the following are true and false otherwise:
                 - is_learning_problem(x, x_labels) == true

dlib/svm/svm_c_ekm_trainer.h

@@ -149,15 +149,15 @@ namespace dlib
         )
         {
             // make sure requires clause is not broken
-            DLIB_ASSERT(basis_samples.size() > 0 && is_vector(vector_to_matrix(basis_samples)),
+            DLIB_ASSERT(basis_samples.size() > 0 && is_vector(mat(basis_samples)),
                 "\tvoid svm_c_ekm_trainer::set_basis(basis_samples)"
                 << "\n\t You have to give a non-empty set of basis_samples and it must be a vector"
                 << "\n\t basis_samples.size():                       " << basis_samples.size() 
-                << "\n\t is_vector(vector_to_matrix(basis_samples)): " << is_vector(vector_to_matrix(basis_samples)) 
+                << "\n\t is_vector(mat(basis_samples)): " << is_vector(mat(basis_samples)) 
                 << "\n\t this: " << this
                 );
 
-            basis = vector_to_matrix(basis_samples);
+            basis = mat(basis_samples);
             ekm_stale = true;
         }
 
@@ -311,9 +311,9 @@ namespace dlib
         {
             scalar_type obj;
             if (basis_loaded())
-                return do_train_user_basis(vector_to_matrix(x),vector_to_matrix(y),obj);
+                return do_train_user_basis(mat(x),mat(y),obj);
             else
-                return do_train_auto_basis(vector_to_matrix(x),vector_to_matrix(y),obj);
+                return do_train_auto_basis(mat(x),mat(y),obj);
         }
 
         template <
@@ -327,9 +327,9 @@ namespace dlib
         ) const
         {
             if (basis_loaded())
-                return do_train_user_basis(vector_to_matrix(x),vector_to_matrix(y),svm_objective);
+                return do_train_user_basis(mat(x),mat(y),svm_objective);
             else
-                return do_train_auto_basis(vector_to_matrix(x),vector_to_matrix(y),svm_objective);
+                return do_train_auto_basis(mat(x),mat(y),svm_objective);
         }
 
 

dlib/svm/svm_c_ekm_trainer_abstract.h

@@ -169,7 +169,7 @@ namespace dlib
         );
         /*!
             requires
-                - T must be a dlib::matrix type or something convertible to a matrix via vector_to_matrix()
+                - T must be a dlib::matrix type or something convertible to a matrix via mat()
                   (e.g. a std::vector)
                 - is_vector(basis_samples) == true
                 - basis_samples.size() > 0
@@ -324,9 +324,9 @@ namespace dlib
         /*!
             requires
                 - is_binary_classification_problem(x,y) == true
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via mat().
                   Also, y should contain scalar_type objects.
             ensures
                 - trains a C support vector classifier given the training samples in x and 
@@ -355,9 +355,9 @@ namespace dlib
         /*!
             requires
                 - is_binary_classification_problem(x,y) == true
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via mat().
                   Also, y should contain scalar_type objects.
             ensures
                 - trains a C support vector classifier given the training samples in x and 

dlib/svm/svm_c_linear_dcd_trainer.h

@@ -441,7 +441,7 @@ namespace dlib
         ) const
         {
             optimizer_state state;
-            return do_train(vector_to_matrix(x), vector_to_matrix(y), state);
+            return do_train(mat(x), mat(y), state);
         }
 
         template <
@@ -454,7 +454,7 @@ namespace dlib
             optimizer_state& state 
         ) const
         {
-            return do_train(vector_to_matrix(x), vector_to_matrix(y), state);
+            return do_train(mat(x), mat(y), state);
         }
 
     private:

dlib/svm/svm_c_linear_dcd_trainer_abstract.h

@@ -264,9 +264,9 @@ namespace dlib
                 - is_learning_problem(x,y) == true
                   (Note that it is ok for x.size() == 1)
                 - All elements of y must be equal to +1 or -1
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via mat().
                   Also, y should contain scalar_type objects.
             ensures
                 - Trains a C support vector classifier given the training samples in x and 
@@ -319,9 +319,9 @@ namespace dlib
                             - max_index_plus_one(x) == max_index_plus_one(X)
                         - else
                             - max_index_plus_one(x) >= max_index_plus_one(X)
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via mat().
                   Also, y should contain scalar_type objects.
             ensures
                 - Trains a C support vector classifier given the training samples in x and 

dlib/svm/svm_c_linear_trainer.h

@@ -511,7 +511,7 @@ namespace dlib
         ) const
         {
             scalar_type obj;
-            return do_train(vector_to_matrix(x),vector_to_matrix(y),obj);
+            return do_train(mat(x),mat(y),obj);
         }
 
 
@@ -525,7 +525,7 @@ namespace dlib
             scalar_type& svm_objective
         ) const
         {
-            return do_train(vector_to_matrix(x),vector_to_matrix(y),svm_objective);
+            return do_train(mat(x),mat(y),svm_objective);
         }
 
     private:

dlib/svm/svm_c_linear_trainer_abstract.h

@@ -236,9 +236,9 @@ namespace dlib
         /*!
             requires
                 - is_binary_classification_problem(x,y) == true
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via mat().
                   Also, y should contain scalar_type objects.
             ensures
                 - trains a C support vector classifier given the training samples in x and 
@@ -265,9 +265,9 @@ namespace dlib
         /*!
             requires
                 - is_binary_classification_problem(x,y) == true
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via mat().
                   Also, y should contain scalar_type objects.
             ensures
                 - trains a C support vector classifier given the training samples in x and 

dlib/svm/svm_c_trainer.h

@@ -178,7 +178,7 @@ namespace dlib
             const in_scalar_vector_type& y
         ) const
         {
-            return do_train(vector_to_matrix(x), vector_to_matrix(y));
+            return do_train(mat(x), mat(y));
         }
 
         void swap (

dlib/svm/svm_c_trainer_abstract.h

@@ -195,9 +195,9 @@ namespace dlib
         /*!
             requires
                 - is_binary_classification_problem(x,y) == true
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via mat().
                   Also, y should contain scalar_type objects.
             ensures
                 - trains a C support vector classifier given the training samples in x and 

dlib/svm/svm_multiclass_linear_trainer.h

@@ -75,7 +75,7 @@ namespace dlib
             psi.push_back(std::make_pair(dims-1,static_cast<scalar_type>(-1)));
 
             // Find which distinct label goes with this psi.
-            const long label_idx = index_of_max(vector_to_matrix(distinct_labels) == labels[idx]);
+            const long label_idx = index_of_max(mat(distinct_labels) == labels[idx]);
 
             offset_feature_vector(psi, dims*label_idx);
         }
@@ -96,7 +96,7 @@ namespace dlib
             {
                 // Compute the F(x,y) part:
                 // perform: temp == dot(relevant part of current solution, samples[idx]) - current_bias
-                scalar_type temp = dot(pointer_to_column_vector(&current_solution(i*dims),dims-1), samples[idx]) - current_solution((i+1)*dims-1);
+                scalar_type temp = dot(mat(&current_solution(i*dims),dims-1), samples[idx]) - current_solution((i+1)*dims-1);
 
                 // Add the LOSS(idx,y) part:
                 if (labels[idx] != distinct_labels[i])

dlib/svm/svm_nu_trainer.h

@@ -138,7 +138,7 @@ namespace dlib
             const in_scalar_vector_type& y
         ) const
         {
-            return do_train(vector_to_matrix(x), vector_to_matrix(y));
+            return do_train(mat(x), mat(y));
         }
 
         void swap (

dlib/svm/svm_nu_trainer_abstract.h

@@ -164,9 +164,9 @@ namespace dlib
         /*!
             requires
                 - is_binary_classification_problem(x,y) == true
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via mat().
                   Also, y should contain scalar_type objects.
             ensures
                 - trains a nu support vector classifier given the training samples in x and 

dlib/svm/svm_one_class_trainer.h

@@ -133,7 +133,7 @@ namespace dlib
             const in_sample_vector_type& x
         ) const
         {
-            return do_train(vector_to_matrix(x));
+            return do_train(mat(x));
         }
 
         void swap (

dlib/svm/svm_one_class_trainer_abstract.h

@@ -160,7 +160,7 @@ namespace dlib
             requires
                 - x.size() > 0
                 - is_col_vector(x) == true
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
             ensures
                 - trains a one-class support vector classifier given the training samples in x.

dlib/svm/svm_threaded.h

@@ -237,8 +237,8 @@ namespace dlib
     )
     {
         return cross_validate_trainer_threaded_impl(trainer,
-                                           vector_to_matrix(x),
-                                           vector_to_matrix(y),
+                                           mat(x),
+                                           mat(y),
                                            folds,
                                            num_threads);
     }

dlib/svm/svr_trainer.h

@@ -140,7 +140,7 @@ namespace dlib
             const in_scalar_vector_type& y
         ) const
         {
-            return do_train(vector_to_matrix(x), vector_to_matrix(y));
+            return do_train(mat(x), mat(y));
         }
 
         void swap (

dlib/svm/svr_trainer_abstract.h

@@ -170,9 +170,9 @@ namespace dlib
         /*!
             requires
                 - is_learning_problem(x,y) == true
-                - x == a matrix or something convertible to a matrix via vector_to_matrix().
+                - x == a matrix or something convertible to a matrix via mat().
                   Also, x should contain sample_type objects.
-                - y == a matrix or something convertible to a matrix via vector_to_matrix().
+                - y == a matrix or something convertible to a matrix via mat().
                   Also, y should contain scalar_type objects.
             ensures
                 - performs support vector regression given the training samples in x and 

dlib/test/assignment_learning.cpp

@@ -315,9 +315,9 @@ namespace
         for (unsigned long i = 0; i < samples.size(); ++i)
         {
             std::vector<long> out = ass(samples[i]);
-            dlog << LINFO << "true labels: " << trans(vector_to_matrix(labels[i]));
-            dlog << LINFO << "pred labels: " << trans(vector_to_matrix(out));
-            DLIB_TEST(trans(vector_to_matrix(labels[i])) == trans(vector_to_matrix(out)));
+            dlog << LINFO << "true labels: " << trans(mat(labels[i]));
+            dlog << LINFO << "pred labels: " << trans(mat(out));
+            DLIB_TEST(trans(mat(labels[i])) == trans(mat(out)));
         }
 
         double accuracy;
@@ -343,9 +343,9 @@ namespace
         for (unsigned long i = 0; i < samples.size(); ++i)
         {
             std::vector<long> out = ass2(samples[i]);
-            dlog << LINFO << "true labels: " << trans(vector_to_matrix(labels[i]));
-            dlog << LINFO << "pred labels: " << trans(vector_to_matrix(out));
-            DLIB_TEST(trans(vector_to_matrix(labels[i])) == trans(vector_to_matrix(out)));
+            dlog << LINFO << "true labels: " << trans(mat(labels[i]));
+            dlog << LINFO << "pred labels: " << trans(mat(out));
+            DLIB_TEST(trans(mat(labels[i])) == trans(mat(out)));
         }
     }