cleaned this up a little

examples/sequence_labeler_ex.cpp

@@ -73,8 +73,8 @@ void deserialize(feature_extractor&, std::istream&) {}
 // ----------------------------------------------------------------------------------------
 
 void make_dataset (
-    const matrix<double>& emission_probabilities,
     const matrix<double>& transition_probabilities,
+    const matrix<double>& emission_probabilities,
     std::vector<std::vector<unsigned long> >& samples,
     std::vector<std::vector<unsigned long> >& labels,
     unsigned long dataset_size
@@ -90,8 +90,10 @@ void make_dataset (
         - This function randomly samples a bunch of sequences from the HMM defined by 
           transition_probabilities and emission_probabilities. 
         - The HMM is defined by:
-            - P(next_label |previous_label) == transition_probabilities(previous_label, next_label)
-            - P(next_sample|next_label)     == emission_probabilities  (next_label,     next_sample)
+            - The probability of transitioning from hidden state H1 to H2 
+              is given by transition_probabilities(H1,H2).
+            - The probability of a hidden state H producing an observed state
+              O is given by emission_probabilities(H,O).
         - #samples.size() == labels.size() == dataset_size
         - for all valid i:
             - #labels[i] is a randomly sampled sequence of hidden states from the
@@ -103,6 +105,10 @@ void make_dataset (
 
 int main()
 {
+    matrix<double> transition_probabilities(num_label_states, num_label_states);
+    transition_probabilities = 0.05, 0.90, 0.05,
+                               0.05, 0.05, 0.90,
+                               0.90, 0.05, 0.05;
 
     // set this up so emission_probabilities(L,X) == The probability of a state with label L 
     // emitting an X.
@@ -111,17 +117,11 @@ int main()
                              0.0, 0.5, 0.5,
                              0.5, 0.0, 0.5;
 
-    matrix<double> transition_probabilities(num_label_states, num_label_states);
-
-    transition_probabilities = 0.05, 0.90, 0.05,
-                               0.05, 0.05, 0.90,
-                               0.90, 0.05, 0.05;
-                    
 
 
     std::vector<std::vector<unsigned long> > samples;
     std::vector<std::vector<unsigned long> > labels;
-    make_dataset(emission_probabilities, transition_probabilities,
+    make_dataset(transition_probabilities,emission_probabilities, 
                  samples, labels, 1000);
 
     cout << "samples.size(): "<< samples.size() << endl;
@@ -139,17 +139,19 @@ int main()
     trainer.set_num_threads(4);
 
 
-    matrix<double> confusion_matrix;
 
     // Learn to do sequence labeling from the dataset
     sequence_labeler<feature_extractor> labeler = trainer.train(samples, labels);
-    confusion_matrix = test_sequence_labeler(labeler, samples, labels);
-    cout << "trained sequence labeler: " << endl;
-    cout << confusion_matrix;
-    cout << "label accuracy: "<< sum(diag(confusion_matrix))/sum(confusion_matrix) << endl;
+
+    std::vector<unsigned long> predicted_labels = labeler(samples[0]);
+    cout << "true hidden states:      "<< trans(vector_to_matrix(labels[0]));
+    cout << "predicted hidden states: "<< trans(vector_to_matrix(predicted_labels));
+
+
 
 
     // We can also do cross-validation 
+    matrix<double> confusion_matrix;
     confusion_matrix = cross_validate_sequence_labeler(trainer, samples, labels, 4);
     cout << "\ncross-validation: " << endl;
     cout << confusion_matrix;
@@ -236,8 +238,8 @@ void sample_hmm (
 // ----------------------------------------------------------------------------------------
 
 void make_dataset (
-    const matrix<double>& emission_probabilities,
     const matrix<double>& transition_probabilities,
+    const matrix<double>& emission_probabilities,
     std::vector<std::vector<unsigned long> >& samples,
     std::vector<std::vector<unsigned long> >& labels,
     unsigned long dataset_size