def test_np_format_as_index2vector():
# Test 5 random batches for shape, number of non-zeros
for _ in xrange(5):
max_labels = np.random.randint(2, 10)
batch_size = np.random.randint(1, 10)
labels = np.random.randint(1, 10)
batch = np.random.random_integers(max_labels - 1,
size=(batch_size, labels))
index_space = IndexSpace(dim=labels, max_labels=max_labels)
vector_space_merge = VectorSpace(dim=max_labels)
vector_space_concatenate = VectorSpace(dim=max_labels * labels)
merged = index_space.np_format_as(batch, vector_space_merge)
concatenated = index_space.np_format_as(batch, vector_space_concatenate)
if batch_size > 1:
assert merged.shape == (batch_size, max_labels)
assert concatenated.shape == (batch_size, max_labels * labels)
else:
assert merged.shape == (max_labels,)
assert concatenated.shape == (max_labels * labels,)
assert np.count_nonzero(merged) <= batch.size
assert np.count_nonzero(concatenated) == batch.size
assert np.all(np.unique(concatenated) == np.array([0, 1]))
# Make sure Theano variables give the same result
batch = tensor.lmatrix('batch')
single = tensor.lvector('single')
batch_size = np.random.randint(2, 10)
np_batch = np.random.random_integers(max_labels - 1,
size=(batch_size, labels))
np_single = np.random.random_integers(max_labels - 1,
size=(labels))
f_batch_merge = theano.function(
[batch], index_space._format_as(batch, vector_space_merge)
)
f_batch_concatenate = theano.function(
[batch], index_space._format_as(batch, vector_space_concatenate)
)
f_single_merge = theano.function(
[single], index_space._format_as(single, vector_space_merge)
)
f_single_concatenate = theano.function(
[single], index_space._format_as(single, vector_space_concatenate)
)
np.testing.assert_allclose(
f_batch_merge(np_batch),
index_space.np_format_as(np_batch, vector_space_merge)
)
np.testing.assert_allclose(
f_batch_concatenate(np_batch),
index_space.np_format_as(np_batch, vector_space_concatenate)
)
np.testing.assert_allclose(
f_single_merge(np_single),
index_space.np_format_as(np_single, vector_space_merge)
)
np.testing.assert_allclose(
f_single_concatenate(np_single),
index_space.np_format_as(np_single, vector_space_concatenate)
)
def test_np_format_as_index2vector():
# Test 5 random batches for shape, number of non-zeros
for _ in xrange(5):
max_labels = np.random.randint(2, 10)
batch_size = np.random.randint(1, 10)
labels = np.random.randint(1, 10)
batch = np.random.random_integers(max_labels - 1,
size=(batch_size, labels))
index_space = IndexSpace(dim=labels, max_labels=max_labels)
vector_space_merge = VectorSpace(dim=max_labels)
vector_space_concatenate = VectorSpace(dim=max_labels * labels)
merged = index_space.np_format_as(batch, vector_space_merge)
concatenated = index_space.np_format_as(batch,
vector_space_concatenate)
if batch_size > 1:
assert merged.shape == (batch_size, max_labels)
assert concatenated.shape == (batch_size, max_labels * labels)
else:
assert merged.shape == (max_labels, )
assert concatenated.shape == (max_labels * labels, )
assert np.count_nonzero(merged) <= batch.size
assert np.count_nonzero(concatenated) == batch.size
assert np.all(np.unique(concatenated) == np.array([0, 1]))
# Make sure Theano variables give the same result
batch = tensor.lmatrix('batch')
single = tensor.lvector('single')
batch_size = np.random.randint(2, 10)
np_batch = np.random.random_integers(max_labels - 1,
size=(batch_size, labels))
np_single = np.random.random_integers(max_labels - 1, size=(labels))
f_batch_merge = theano.function([batch],
index_space._format_as(
batch, vector_space_merge))
f_batch_concatenate = theano.function([batch],
index_space._format_as(
batch, vector_space_concatenate))
f_single_merge = theano.function([single],
index_space._format_as(
single, vector_space_merge))
f_single_concatenate = theano.function([single],
index_space._format_as(
single,
vector_space_concatenate))
np.testing.assert_allclose(
f_batch_merge(np_batch),
index_space.np_format_as(np_batch, vector_space_merge))
np.testing.assert_allclose(
f_batch_concatenate(np_batch),
index_space.np_format_as(np_batch, vector_space_concatenate))
np.testing.assert_allclose(
f_single_merge(np_single),
index_space.np_format_as(np_single, vector_space_merge))
np.testing.assert_allclose(
f_single_concatenate(np_single),
index_space.np_format_as(np_single, vector_space_concatenate))
