def test_sparse_vectorss():
svss = sparse_vectorss()
assert len(svss) == 0
svss.resize(5)
for svs in svss:
assert len(svs) == 0
svss.clear()
assert len(svss) == 0
svss.extend([
sparse_vectors([
sparse_vector([pair(1, 2), pair(3, 4)]),
sparse_vector([pair(5, 6), pair(7, 8)])
])
])
assert len(svss) == 1
assert svss[0][0][0].first == 1
assert svss[0][0][0].second == 2
assert svss[0][0][1].first == 3
assert svss[0][0][1].second == 4
assert svss[0][1][0].first == 5
assert svss[0][1][0].second == 6
assert svss[0][1][1].first == 7
assert svss[0][1][1].second == 8
deser = pickle.loads(pickle.dumps(svss, 2))
assert deser == svss
names.append(dlib.range(5, 8))
segments.append(names)
names.clear()
sentences.append("No names in this sentence at all")
segments.append(names)
names.clear()
# Now before we can pass these training sentences to the dlib tools we need to
# convert them into arrays of vectors as discussed above. We can use either a
# sparse or dense representation depending on our needs. In this example, we
# show how to do it both ways.
use_sparse_vects = False
if use_sparse_vects:
# Make an array of arrays of dlib.sparse_vector objects.
training_sequences = dlib.sparse_vectorss()
for s in sentences:
training_sequences.append(sentence_to_sparse_vectors(s))
else:
# Make an array of arrays of dlib.vector objects.
training_sequences = dlib.vectorss()
for s in sentences:
training_sequences.append(sentence_to_vectors(s))
# Now that we have a simple training set we can train a sequence segmenter.
# However, the sequence segmentation trainer has some optional parameters we can
# set. These parameters determine properties of the segmentation model we will
# learn. See the dlib documentation for the sequence_segmenter object for a
# full discussion of their meanings.
params = dlib.segmenter_params()
params.window_size = 3
segments.append(names)
names.clear()
sentences.append("No names in this sentence at all")
segments.append(names)
names.clear()
# Now before we can pass these training sentences to the dlib tools we need to convert them
# into arrays of vectors as discussed above. We can use either a sparse or dense
# representation depending on our needs. In this example, we show how to do it both ways.
use_sparse_vects = False
if use_sparse_vects:
# Make an array of arrays of dlib.sparse_vector objects.
training_sequences = dlib.sparse_vectorss()
for s in sentences:
training_sequences.append(sentence_to_sparse_vectors(s))
else:
# Make an array of arrays of dlib.vector objects.
training_sequences = dlib.vectorss()
for s in sentences:
training_sequences.append(sentence_to_vectors(s))
# Now that we have a simple training set we can train a sequence segmenter. However, the
# sequence segmentation trainer has some optional parameters we can set. These parameters
# determine properties of the segmentation model we will learn. See the dlib documentation
# for the sequence_segmenter object for a full discussion of their meanings.
params = dlib.segmenter_params()
