switched examples over to the new mat() method.

examples/assignment_learning_ex.cpp

@@ -177,8 +177,8 @@ int main()
         {
             // Predict the assignments for the LHS and RHS in samples[i].   
             std::vector<long> predicted_assignments = assigner(samples[i]);
-            cout << "true labels:      " << trans(vector_to_matrix(labels[i]));
+            cout << "true labels:      " << trans(mat(labels[i]));
-            cout << "predicted labels: " << trans(vector_to_matrix(predicted_assignments)) << endl;
+            cout << "predicted labels: " << trans(mat(predicted_assignments)) << endl;
         }
 
         // We can also use this tool to compute the percentage of assignments predicted correctly.

examples/custom_trainer_ex.cpp

@@ -118,9 +118,9 @@ public:
         }
 
         // divide by number of +1 samples
-        positive_center /= sum(vector_to_matrix(labels) == +1);
+        positive_center /= sum(mat(labels) == +1);
         // divide by number of -1 samples
-        negative_center /= sum(vector_to_matrix(labels) == -1);
+        negative_center /= sum(mat(labels) == -1);
 
         custom_decision_function df;
         df.positive_center = positive_center;

examples/krr_classification_ex.cpp

@@ -65,8 +65,8 @@ int main()
     }
 
     cout << "samples generated: " << samples.size() << endl;
-    cout << "  number of +1 samples: " << sum(vector_to_matrix(labels) > 0) << endl;
+    cout << "  number of +1 samples: " << sum(mat(labels) > 0) << endl;
-    cout << "  number of -1 samples: " << sum(vector_to_matrix(labels) < 0) << endl;
+    cout << "  number of -1 samples: " << sum(mat(labels) < 0) << endl;
 
     // Here we normalize all the samples by subtracting their mean and dividing by their standard deviation.
     // This is generally a good idea since it often heads off numerical stability problems and also 

examples/matrix_expressions_ex.cpp

@@ -384,7 +384,7 @@ void custom_matrix_expressions_example(
         As an aside, note that dlib contains functions equivalent to the ones we 
         defined above.  They are:
             - dlib::trans()
-            - dlib::vector_to_matrix()
+            - dlib::mat() (converts things into matrices)
             - operator+ (e.g. you can say my_mat + 1)
 
 

examples/rank_features_ex.cpp

@@ -79,8 +79,8 @@ int main()
     // Here we normalize all the samples by subtracting their mean and dividing by their standard deviation.
     // This is generally a good idea since it often heads off numerical stability problems and also 
     // prevents one large feature from smothering others.
-    const sample_type m(mean(vector_to_matrix(samples)));  // compute a mean vector
+    const sample_type m(mean(mat(samples)));  // compute a mean vector
-    const sample_type sd(reciprocal(sqrt(variance(vector_to_matrix(samples))))); // compute a standard deviation vector
+    const sample_type sd(reciprocal(stddev(mat(samples)))); // compute a standard deviation vector
     // now normalize each sample
     for (unsigned long i = 0; i < samples.size(); ++i)
         samples[i] = pointwise_multiply(samples[i] - m, sd); 

examples/sequence_labeler_ex.cpp

@@ -228,8 +228,8 @@ int main()
     // print out some of the randomly sampled sequences
     for (int i = 0; i < 10; ++i)
     {
-        cout << "hidden states:   " << trans(vector_to_matrix(labels[i]));
+        cout << "hidden states:   " << trans(mat(labels[i]));
-        cout << "observed states: " << trans(vector_to_matrix(samples[i]));
+        cout << "observed states: " << trans(mat(samples[i]));
         cout << "******************************" << endl;
     }
 
@@ -251,8 +251,8 @@ int main()
     // Test the learned labeler on one of the training samples.  In this
     // case it will give the correct sequence of labels.
     std::vector<unsigned long> predicted_labels = labeler(samples[0]);
-    cout << "true hidden states:      "<< trans(vector_to_matrix(labels[0]));
+    cout << "true hidden states:      "<< trans(mat(labels[0]));
-    cout << "predicted hidden states: "<< trans(vector_to_matrix(predicted_labels));
+    cout << "predicted hidden states: "<< trans(mat(predicted_labels));
 
 
 

examples/using_custom_kernels_ex.cpp

@@ -160,8 +160,8 @@ int main()
         }
     }
     cout << "samples generated: " << samples.size() << endl;
-    cout << "  number of +1 samples: " << sum(vector_to_matrix(labels) > 0) << endl;
+    cout << "  number of +1 samples: " << sum(mat(labels) > 0) << endl;
-    cout << "  number of -1 samples: " << sum(vector_to_matrix(labels) < 0) << endl;
+    cout << "  number of -1 samples: " << sum(mat(labels) < 0) << endl;
 
 
     // A valid kernel must always give rise to kernel matrices which are symmetric