Changed test_regression_function() and cross_validate_regression_trainer() to

output 2 more statistics, which are the mean absolute error and the standard
deviation of the absolute error.  This means these functions now return 4D
rather than 2D vectors.

I also made test_regression_function() take a non-const reference to the
regression function so that DNN objects can be tested.

dlib/svm/cross_validate_regression_trainer.h

@@ -18,9 +18,9 @@ namespace dlib
         typename sample_type,
         typename label_type
         >
-    matrix<double,1,2>
+    matrix<double,1,4>
     test_regression_function (
-        const reg_funct_type& reg_funct,
+        reg_funct_type& reg_funct,
         const std::vector<sample_type>& x_test,
         const std::vector<label_type>& y_test
     )
@@ -33,7 +33,7 @@ namespace dlib
                     << "\n\t is_learning_problem(x_test,y_test): " 
                     << is_learning_problem(x_test,y_test));
 
-        running_stats<double> rs;
+        running_stats<double> rs, rs_mae;
         running_scalar_covariance<double> rc;
 
         for (unsigned long i = 0; i < x_test.size(); ++i)
@@ -42,12 +42,13 @@ namespace dlib
             const double output = reg_funct(x_test[i]);
             const double temp = output - y_test[i];
 
+            rs_mae.add(std::abs(temp));
             rs.add(temp*temp);
             rc.add(output, y_test[i]);
         }
 
-        matrix<double,1,2> result;
-        result = rs.mean(), std::pow(rc.correlation(),2);
+        matrix<double,1,4> result;
+        result = rs.mean(), std::pow(rc.correlation(),2), rs_mae.mean(), rs_mae.stddev();
         return result;
     }
 
@@ -58,7 +59,7 @@ namespace dlib
         typename sample_type,
         typename label_type 
         >
-    matrix<double,1,2> 
+    matrix<double,1,4> 
     cross_validate_regression_trainer (
         const trainer_type& trainer,
         const std::vector<sample_type>& x,
@@ -82,7 +83,7 @@ namespace dlib
         const long num_in_test = x.size()/folds;
         const long num_in_train = x.size() - num_in_test;
 
-        running_stats<double> rs;
+        running_stats<double> rs, rs_mae;
         running_scalar_covariance<double> rc;
 
         std::vector<sample_type> x_test, x_train;
@@ -128,6 +129,7 @@ namespace dlib
                     const double output = df(x_test[j]);
                     const double temp = output - y_test[j];
 
+                    rs_mae.add(std::abs(temp));
                     rs.add(temp*temp);
                     rc.add(output, y_test[j]);
                 }
@@ -139,8 +141,8 @@ namespace dlib
 
         } // for (long i = 0; i < folds; ++i)
 
-        matrix<double,1,2> result;
-        result = rs.mean(), std::pow(rc.correlation(),2);
+        matrix<double,1,4> result;
+        result = rs.mean(), std::pow(rc.correlation(),2), rs_mae.mean(), rs_mae.stddev();
         return result;
     }
 

dlib/svm/cross_validate_regression_trainer_abstract.h

@@ -16,9 +16,9 @@ namespace dlib
         typename sample_type,
         typename label_type
         >
-    matrix<double,1,2>
+    matrix<double,1,4>
     test_regression_function (
-        const reg_funct_type& reg_funct,
+        reg_funct_type& reg_funct,
         const std::vector<sample_type>& x_test,
         const std::vector<label_type>& y_test
     );
@@ -35,6 +35,9 @@ namespace dlib
                 - M(1) == the R-squared value (i.e. the squared correlation between
                   reg_funct(x_test[i]) and y_test[i]).  This is a number between 0
                   and 1.
+                - M(2) == the mean absolute error.  
+                  This is given by: sum over i: abs(reg_funct(x_test[i]) - y_test[i])
+                - M(3) == the standard deviation of the absolute error.
     !*/
 
 // ----------------------------------------------------------------------------------------
@@ -44,7 +47,7 @@ namespace dlib
         typename sample_type,
         typename label_type 
         >
-    matrix<double,1,2>
+    matrix<double,1,4>
     cross_validate_regression_trainer (
         const trainer_type& trainer,
         const std::vector<sample_type>& x,
@@ -66,6 +69,9 @@ namespace dlib
                 - M(1) == the R-squared value (i.e. the squared correlation between
                   a predicted y value and its true value).  This is a number between 
                   0 and 1.
+                - M(2) == the mean absolute error.  
+                  This is given by: sum over i: abs(reg_funct(x_test[i]) - y_test[i])
+                - M(3) == the standard deviation of the absolute error.
     !*/
 
 }

dlib/test/svm.cpp

@@ -247,7 +247,7 @@ namespace
         randomize_samples(samples, labels);
         dlog << LINFO << "KRR MSE and R-squared: "<< cross_validate_regression_trainer(krr_test, samples, labels, 6);
         dlog << LINFO << "SVR MSE and R-squared: "<< cross_validate_regression_trainer(svr_test, samples, labels, 6);
-        matrix<double,1,2> cv = cross_validate_regression_trainer(krr_test, samples, labels, 6);
+        matrix<double,1,4> cv = cross_validate_regression_trainer(krr_test, samples, labels, 6);
         DLIB_TEST(cv(0) < 1e-4);
         DLIB_TEST(cv(1) > 0.99);
         cv = cross_validate_regression_trainer(svr_test, samples, labels, 6);