def test_indexing(matrix_type, idx):
a = assparsearray(
matrix_type(sparse.random(50, 100, format="csr", density=0.4)))
true_result = a.value.toarray()[idx]
curr_result = a[idx]
np.testing.assert_array_equal(asnparray(curr_result), true_result)
def test_union(matrix_type, matrix_type2):
a = matrix_type(sparse.random(1000, 1000, density=0.2, dtype=bool))
b = matrix_type2(sparse.random(1000, 1000, density=0.2, dtype=bool))
truth = a + b
test = op_union_indices(njit(lambda x, y: x | y), a, b)
assert (truth != test).sum() == 0
a = matrix_type(sparse.random(1000, 1000, density=0.2))
b = matrix_type2(sparse.random(1000, 1000, density=0.2))
truth = a + b
test = op_union_indices(njit(lambda x, y: x + y), a, b)
assert (truth != test).sum() == 0
def test_symdifference(matrix_type, matrix_type2):
a = np.random.randint(2, size=(100, 100), dtype=bool)
b = np.random.randint(2, size=(100, 100), dtype=bool)
truth = a ^ b
test = symdifference_indices(matrix_type(a), matrix_type2(b))
assert (truth != test).sum() == 0
def test_difference(matrix_type, matrix_type2):
a = matrix_type(sparse.random(1000, 1000, density=0.2, dtype=bool))
b = matrix_type2(sparse.random(1000, 1000, density=0.2, dtype=bool))
truth = a > b
test = difference_indices(a, b)
assert (truth != test).sum() == 0
def test_intersect(matrix_type, matrix_type2):
a = matrix_type(sparse.random(1000, 1000, density=0.2))
b = matrix_type2(sparse.random(1000, 1000, density=0.2))
truth = a.multiply(b)
test = op_intersect_indices(njit(lambda x, y: x * y), a, b)
assert (truth != test).sum() == 0
def test_boolean_op(matrix_type, another_matrix_type, boolean_op,
alternate_form):
if boolean_op in SCIPYSPARSE_NOT_IMPLEMENTED and alternate_form == asspmatrix:
pytest.skip("Not supported")
sarr1 = assparsearray(matrix_type(ss.random(100, 100, dtype=bool)))
sarr2 = assparsearray(another_matrix_type(ss.random(100, 100, dtype=bool)))
alt1, alt2 = alternate_form(sarr1), alternate_form(sarr2)
sarr_res = boolean_op(sarr1, sarr2)
alt_res = boolean_op(alt1, alt2)
assert not np.any(sarr_res != alt_res)
def test_matmul(matrix_type, another_matrix_type):
mtx1 = matrix_type(ss.random(100, 100))
mtx2 = another_matrix_type(ss.random(100, 100))
arr1, arr2 = assparsearray(mtx1), assparsearray(mtx2)
assert np.all(arr1 @ arr2 == mtx1 @ mtx2)
def test_divide_scalar(matrix_type):
mtx = matrix_type(ss.random(100, 100))
arr = assparsearray(mtx)
assert np.all(arr / 2 == mtx / 2)
def test_subtract(matrix_type, another_matrix_type):
mtx1 = matrix_type(ss.random(100, 100))
mtx2 = another_matrix_type(ss.random(100, 100))
arr1, arr2 = assparsearray(mtx1), assparsearray(mtx2)
assert np.all(arr1 - arr2 == mtx1 - mtx2)
def test_any(matrix_type):
assert not np.any(assparsearray(matrix_type(np.zeros((100, 100)))))
assert np.any(assparsearray(matrix_type(np.eye(100))))
def test_all(matrix_type):
mtx = matrix_type(np.eye(100))
arr = assparsearray(mtx)
assert not np.all(arr)
assert np.all(assparsearray(matrix_type(np.ones((10, 10))))) is npTrue