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 test_sparse_vector():
sv = sparse_vector()
sv.append(pair(3, .1))
sv.append(pair(3, .2))
sv.append(pair(2, .3))
sv.append(pair(1, .4))
assert len(sv) == 4
make_sparse_vector(sv)
assert len(sv) == 3
assert sv[0].first == 1
assert sv[0].second == .4
assert sv[1].first == 2
assert sv[1].second == .3
assert sv[2].first == 3
assert sv[2].second == approx(.3)
assert str(sv) == "1: 0.4\n2: 0.3\n3: 0.3"
assert repr(
sv) == "< dlib.sparse_vector containing: \n1: 0.4\n2: 0.3\n3: 0.3 >"
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
def test_pair():
p = pair(4, .9)
assert p.first == 4
assert p.second == .9
p.first = 3
p.second = .4
assert p.first == 3
assert p.second == .4
assert str(p) == "3: 0.4"
assert repr(p) == "dlib.pair(3, 0.4)"
deser = pickle.loads(pickle.dumps(p, 2))
assert deser.first == p.first
assert deser.second == p.second
# 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) trainer = dlib.svm_rank_trainer_sparse() rank = trainer.train(data) print "ranking score for a relevant vector: ", rank(data.relevant[0]) print "ranking score for a non-relevant vector: ", rank(data.nonrelevant[0]) # Just as before, the output is the following: # ranking score for a relevant vector: 0.5 # ranking score for a non-relevant vector: -0.5
# 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)
trainer = dlib.svm_rank_trainer_sparse()
rank = trainer.train(data)
print("Ranking score for a relevant vector: {}".format(
rank(data.relevant[0])))
print("Ranking score for a non-relevant vector: {}".format(
rank(data.nonrelevant[0])))
# Just as before, the output is the following:
# ranking score for a relevant vector: 0.5
