Added more comments about sparse vectors to the python examples

python_examples/sequence_segmenter.py

@@ -49,7 +49,10 @@ def sentence_to_vectors(sentence):
 # than the above form when you have very high dimensional vectors that are mostly full of
 # zeros.  In dlib, each sparse vector is represented as an array of pair objects.  Each
 # pair contains an index and value.  Any index not listed in the vector is implicitly
-# associated with a value of zero.
+# associated with a value of zero.  Additionally, when using sparse vectors with
+# dlib.train_sequence_segmenter() you can use "unsorted" sparse vectors.  This means you
+# can add the index/value pairs into your sparse vectors in any order you want and don't
+# need to worry about them being in sorted order. 
 def sentence_to_sparse_vectors(sentence):
     vects   = dlib.sparse_vectors()
     has_cap = dlib.sparse_vector()

python_examples/svm_struct.py

@@ -204,7 +204,11 @@ class three_class_classifier_problem:
         # problem.  So you need to pick a PSI that doesn't make the label maximization step
         # intractable but also still well models your problem.  
 
-        # Create a dense vector object.
+        # Create a dense vector object (note that you can also use unsorted sparse vectors
+        # (i.e.  dlib.sparse_vector objects) to represent your PSI vector.  This is useful
+        # if you have very high dimensional PSI vectors that are mostly zeros.  In the
+        # context of this example, you would simply return a dlib.sparse_vector at the end
+        # of make_psi() and the rest of the example would still work properly. ).
         psi = dlib.vector()
         # Set it to have 9 dimensions.  Note that the elements of the vector are 0
         # initialized.