How to use the dlib.sparse_vector function in dlib

def sentence_to_sparse_vectors(sentence):
    vects = dlib.sparse_vectors()
    has_cap = dlib.sparse_vector()
    no_cap = dlib.sparse_vector()
    # make has_cap equivalent to dlib.vector([1])
    has_cap.append(dlib.pair(0, 1))

    # Since we didn't add anything to no_cap it is equivalent to
    # dlib.vector([0])
    for word in sentence.split():
        if word[0].isupper():
            vects.append(has_cap)
        else:
            vects.append(no_cap)
    return vects

def sentence_to_sparse_vectors(sentence):
    vects = dlib.sparse_vectors()
    has_cap = dlib.sparse_vector()
    no_cap = dlib.sparse_vector()
    # make has_cap equivalent to dlib.vector([1])
    has_cap.append(dlib.pair(0, 1))

    # Since we didn't add anything to no_cap it is equivalent to
    # dlib.vector([0])
    for word in sentence.split():
        if word[0].isupper():
            vects.append(has_cap)
        else:
            vects.append(no_cap)
    return vects

def __call__(self, question, guess):
        v = dlib.sparse_vector()
        for feat_type in self._feat:
            extracted = feat_type(question, guess)
            for feat_val in extracted:
                v.append(dlib.pair(self._feat_names[feat_val],
                                   extracted[feat_val]))
        return v

# cross_validate_ranking_trainer().  This performs cross-validation by splitting
# the queries up into folds.  That is, it lets the trainer train on a subset of
# ranking_pair instances and tests on the rest.  It does this over 4 different
# splits and returns the overall ranking accuracy based on the held out data.
# Just like test_ranking_function(), it reports both the ordering accuracy and
# mean average precision.
print("Cross validation results: {}".format(
    dlib.cross_validate_ranking_trainer(trainer, queries, 4)))

# Finally, note that the ranking tools also support the use of sparse vectors in
# addition to dense vectors (which we used above).  So if we wanted to do
# exactly what we did in the first part of the example program above but using
# sparse vectors we would do it like so:

data = dlib.sparse_ranking_pair()
samp = dlib.sparse_vector()

# Make samp represent the same vector as dlib.vector([1, 0]).  In dlib, a sparse
# vector is just an array of pair objects.  Each pair stores an index and a
# value.  Moreover, the svm-ranking tools require sparse vectors to be sorted
# and to have unique indices.  This means that the indices are listed in
# increasing order and no index value shows up more than once.  If necessary,
# you can use the dlib.make_sparse_vector() routine to make a sparse vector
# object properly sorted and contain unique indices. 
samp.append(dlib.pair(0, 1))
data.relevant.append(samp)

# Now make samp represent the same vector as dlib.vector([0, 1])
samp.clear()
samp.append(dlib.pair(1, 1))
data.nonrelevant.append(samp)