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Commit d1b579f0 authored by Davis King's avatar Davis King
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Added try/catch block to main().

parent 8c8c5bf3
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examples/graph_labeling_ex.cpp
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......@@ -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;
}
}
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