def test_EnsureSortedIndices():
input_tensor = theano.sparse.csc_dmatrix()
sort_op = sp.ensure_sorted_indices(input_tensor)
f = theano.function([input_tensor], sort_op)
# test_sample = scipy.sparse.rand(1000, 1000, density=0.01)
# the function above is not working since scipy has lower version
# specify the number of rows and columns in the test case
r = 2000
c = 2000
# construct a test sample
test_sample = numpy.random.rand(r, c)
indices_zero_x = numpy.random.permutation(range(r))
indices_zero_y = numpy.random.permutation(range(c))
# take 70% of the test_sample and make them 0
sparse_ratio_x = int(r * 0.9)
sparse_ratio_y = int(c * 0.9)
for i in indices_zero_x[:sparse_ratio_x]:
for j in indices_zero_y[:sparse_ratio_y]:
test_sample[i, j] = 0
# sort test sample by scipy
test_sample = scipy.sparse.csc_matrix(test_sample)
sorted_for_real = test_sample.sorted_indices()
sorted_theano = f(test_sample)
assert numpy.all(sorted_theano.todense() == sorted_for_real.todense())
def test_ensure_sorted_indices():
x = 2000
y = 2000
sparsity = 1000
for i in range(2):
# testing both csc and csr
if i is 0:
# csc
input_tensor = theano.sparse.csc_dmatrix()
sample = scipy.sparse.csc_matrix(random_lil((x, y), "float64", sparsity))
else:
# csr
input_tensor = theano.sparse.csr_dmatrix()
sample = scipy.sparse.csr_matrix(random_lil((x, y), "float64", sparsity))
sort_op = sp.ensure_sorted_indices(input_tensor)
f = theano.function([input_tensor], sort_op)
sorted_scipy = sample.sorted_indices()
sorted_theano = f(sample)
assert numpy.all(sorted_theano.todense() == sorted_scipy.todense())
def test_ensure_sorted_indices():
x = 2000
y = 2000
sparsity = 1000
for i in range(2):
# testing both csc and csr
if i is 0:
# csc
input_tensor = theano.sparse.csc_dmatrix()
sample = scipy.sparse.csc_matrix(
random_lil((x, y), 'float64', sparsity))
else:
# csr
input_tensor = theano.sparse.csr_dmatrix()
sample = scipy.sparse.csr_matrix(
random_lil((x, y), 'float64', sparsity))
sort_op = sp.ensure_sorted_indices(input_tensor)
f = theano.function([input_tensor], sort_op)
sorted_scipy = sample.sorted_indices()
sorted_theano = f(sample)
assert numpy.all(sorted_theano.todense() == sorted_scipy.todense())
