Added try/catch block to main().

examples/graph_labeling_ex.cpp

@@ -170,79 +170,87 @@ void make_training_examples(
 
 int main()
 {
-    // Get the training samples we defined above.
-    dlib::array<graph_type> samples;
-    std::vector<std::vector<bool> > labels;
-    make_training_examples(samples, labels);
-
-
-    // Create a structural SVM trainer for graph labeling problems.  The vector_type
-    // needs to be set to a type capable of holding node or edge vectors.
-    typedef matrix<double,0,1> vector_type;
-    structural_graph_labeling_trainer<vector_type> trainer;
-    // This is the usual SVM C parameter.  Larger values make the trainer try 
-    // harder to fit the training data but might result in overfitting.  You 
-    // should set this value to whatever gives the best cross-validation results.
-    trainer.set_c(10);
-
-    // Do 3-fold cross-validation and print the results.  In this case it will
-    // indicate that all nodes were correctly classified.  
-    cout << "3-fold cross-validation: " << cross_validate_graph_labeling_trainer(trainer, samples, labels, 3) << endl;
-
-    // Since the trainer is working well.  Lets have it make a graph_labeler 
-    // based on the training data.
-    graph_labeler<vector_type> labeler = trainer.train(samples, labels);
-
-
-    /*
-        Lets try the graph_labeler on a new test graph.  In particular, lets
-        use one with 5 nodes as shown below:
-
-         (0 F)-----(1 T)
-           |         |
-           |         |
-           |         |
-         (3 T)-----(2 T)------(4 T)
-
-        I have annotated each node with either T or F to indicate the correct 
-        output (true or false).  
-    */
-    graph_type g;
-    g.set_number_of_nodes(5);
-    g.node(0).data = 1, 0;  // Node data indicates a false node.
-    g.node(1).data = 0, 1;  // Node data indicates a true node.
-    g.node(2).data = 0, 0;  // Node data is ambiguous.
-    g.node(3).data = 0, 0;  // Node data is ambiguous.
-    g.node(4).data = 0.1, 0; // Node data slightly indicates a false node.
-
-    g.add_edge(0,1);
-    g.add_edge(1,2);
-    g.add_edge(2,3);
-    g.add_edge(3,0);
-    g.add_edge(2,4);
-
-    // Set the edges up so nodes 1, 2, 3, and 4 are all strongly connected.
-    edge(g,0,1) = 0;
-    edge(g,1,2) = 1;
-    edge(g,2,3) = 1;
-    edge(g,3,0) = 0;
-    edge(g,2,4) = 1;
-
-    // The output of this shows all the nodes are correctly labeled.
-    cout << "Predicted labels: " << endl;
-    std::vector<bool> temp = labeler(g);
-    for (unsigned long i = 0; i < temp.size(); ++i)
-        cout << " " << i << ": " << temp[i] << endl;
-
-
-
-    // Breaking the strong labeling consistency link between node 1 and 2 causes
-    // nodes 2, 3, and 4 to flip to false.  This is because of their connection
-    // to node 4 which has a small preference for false.
-    edge(g,1,2) = 0;
-    cout << "Predicted labels: " << endl;
-    temp = labeler(g);
-    for (unsigned long i = 0; i < temp.size(); ++i)
-        cout << " " << i << ": " << temp[i] << endl;
+    try
+    {
+        // Get the training samples we defined above.
+        dlib::array<graph_type> samples;
+        std::vector<std::vector<bool> > labels;
+        make_training_examples(samples, labels);
+
+
+        // Create a structural SVM trainer for graph labeling problems.  The vector_type
+        // needs to be set to a type capable of holding node or edge vectors.
+        typedef matrix<double,0,1> vector_type;
+        structural_graph_labeling_trainer<vector_type> trainer;
+        // This is the usual SVM C parameter.  Larger values make the trainer try 
+        // harder to fit the training data but might result in overfitting.  You 
+        // should set this value to whatever gives the best cross-validation results.
+        trainer.set_c(10);
+
+        // Do 3-fold cross-validation and print the results.  In this case it will
+        // indicate that all nodes were correctly classified.  
+        cout << "3-fold cross-validation: " << cross_validate_graph_labeling_trainer(trainer, samples, labels, 3) << endl;
+
+        // Since the trainer is working well.  Lets have it make a graph_labeler 
+        // based on the training data.
+        graph_labeler<vector_type> labeler = trainer.train(samples, labels);
+
+
+        /*
+            Lets try the graph_labeler on a new test graph.  In particular, lets
+            use one with 5 nodes as shown below:
+
+            (0 F)-----(1 T)
+              |         |
+              |         |
+              |         |
+            (3 T)-----(2 T)------(4 T)
+
+            I have annotated each node with either T or F to indicate the correct 
+            output (true or false).  
+        */
+        graph_type g;
+        g.set_number_of_nodes(5);
+        g.node(0).data = 1, 0;  // Node data indicates a false node.
+        g.node(1).data = 0, 1;  // Node data indicates a true node.
+        g.node(2).data = 0, 0;  // Node data is ambiguous.
+        g.node(3).data = 0, 0;  // Node data is ambiguous.
+        g.node(4).data = 0.1, 0; // Node data slightly indicates a false node.
+
+        g.add_edge(0,1);
+        g.add_edge(1,2);
+        g.add_edge(2,3);
+        g.add_edge(3,0);
+        g.add_edge(2,4);
+
+        // Set the edges up so nodes 1, 2, 3, and 4 are all strongly connected.
+        edge(g,0,1) = 0;
+        edge(g,1,2) = 1;
+        edge(g,2,3) = 1;
+        edge(g,3,0) = 0;
+        edge(g,2,4) = 1;
+
+        // The output of this shows all the nodes are correctly labeled.
+        cout << "Predicted labels: " << endl;
+        std::vector<bool> temp = labeler(g);
+        for (unsigned long i = 0; i < temp.size(); ++i)
+            cout << " " << i << ": " << temp[i] << endl;
+
+
+
+        // Breaking the strong labeling consistency link between node 1 and 2 causes
+        // nodes 2, 3, and 4 to flip to false.  This is because of their connection
+        // to node 4 which has a small preference for false.
+        edge(g,1,2) = 0;
+        cout << "Predicted labels: " << endl;
+        temp = labeler(g);
+        for (unsigned long i = 0; i < temp.size(); ++i)
+            cout << " " << i << ": " << temp[i] << endl;
+    }
+    catch (std::exception& e)
+    {
+        cout << "Error, an exception was thrown!" << endl;
+        cout << e.what() << endl;
+    }
 }