def test_borrow():
vecs = Vectors(128)
good = numpy.ndarray(shape=(vecs.nr_dim,), dtype='float32')
vecs.borrow(good)
bad = numpy.ndarray(shape=(vecs.nr_dim+1,), dtype='float32')
with pytest.raises(AssertionError) as excinfo:
vecs.borrow(bad)
def test_most_similar():
vecs = Vectors(4)
vecs.add(numpy.asarray([4, 2, 2, 2], dtype='float32'))
vecs.add(numpy.asarray([4, 4, 2, 2], dtype='float32'))
vecs.add(numpy.asarray([4, 4, 4, 2], dtype='float32'))
vecs.add(numpy.asarray([4, 4, 4, 4], dtype='float32'))
indices, scores = vecs.most_similar(
numpy.asarray([4, 2, 2, 2], dtype='float32'), 4)
print(list(scores))
assert list(indices) == [0, 1]
indices, scores = vecs.most_similar(
numpy.asarray([0.1, 1, 1, 1], dtype='float32'), 4)
assert list(indices) == [4, 3]
def test_borrow():
vecs = Vectors(128)
good = numpy.ndarray(shape=(vecs.nr_dim, ), dtype='float32')
vecs.borrow(good)
bad = numpy.ndarray(shape=(vecs.nr_dim + 1, ), dtype='float32')
with pytest.raises(AssertionError) as excinfo:
vecs.borrow(bad)
def test_most_similar():
vecs = Vectors(4)
vecs.add(numpy.asarray([4,2,2,2], dtype='float32'))
vecs.add(numpy.asarray([4,4,2,2], dtype='float32'))
vecs.add(numpy.asarray([4,4,4,2], dtype='float32'))
vecs.add(numpy.asarray([4,4,4,4], dtype='float32'))
indices, scores = vecs.most_similar(
numpy.asarray([4,2,2,2], dtype='float32'), 4)
print(list(scores))
assert list(indices) == [0,1]
indices, scores = vecs.most_similar(
numpy.asarray([0.1,1,1,1], dtype='float32'), 4)
assert list(indices) == [4,3]
def test_init():
vec = Vectors(128)
assert vec.mem is not None
with pytest.raises(AttributeError) as excinfo:
vec.mem = None
def test_init():
vec = Vectors(128)
assert vec.mem is not None
with pytest.raises(AttributeError) as excinfo:
vec.mem = None