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
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 training_data():
r = Random(0)
predictors = vectors()
sparse_predictors = sparse_vectors()
response = array()
for i in range(30):
for c in [-1, 1]:
response.append(c)
values = [r.random() + c * 0.5 for _ in range(3)]
predictors.append(vector(values))
sp = sparse_vector()
for i, v in enumerate(values):
sp.append(pair(i, v))
sparse_predictors.append(sp)
return predictors, sparse_predictors, response
def training_data():
r = Random(0)
predictors = vectors()
sparse_predictors = sparse_vectors()
response = array()
for i in range(30):
for c in [-1, 1]:
response.append(c)
values = [r.random() + c * 0.5 for _ in range(3)]
predictors.append(vector(values))
sp = sparse_vector()
for i, v in enumerate(values):
sp.append(pair(i, v))
sparse_predictors.append(sp)
return predictors, sparse_predictors, response
