def test_trinary_broadcasting(shapes, func):
args = [sparse.random(s, density=0.5) for s in shapes]
dense_args = [arg.todense() for arg in args]
fs = sparse.elemwise(func, *args)
assert isinstance(fs, COO)
assert_eq(fs, func(*dense_args))
def test_trinary_broadcasting_pathological(shapes, func, value, fraction):
args = [sparse.random(s, density=0.5, data_rvs=random_value_array(value, fraction))
for s in shapes]
dense_args = [arg.todense() for arg in args]
fs = sparse.elemwise(func, *args)
assert isinstance(fs, COO)
assert_eq(fs, func(*dense_args), equal_nan=True)
def test_sparsearray_elemwise(format):
xs = sparse.random((3, 4), density=0.5, format=format)
ys = sparse.random((3, 4), density=0.5, format=format)
x = xs.todense()
y = ys.todense()
fs = sparse.elemwise(operator.add, xs, ys)
assert isinstance(fs, COO)
assert_eq(fs, x + y)
def test_elemwise_unsupported():
class A():
pass
s1 = sparse.random((2, 3, 4), density=0.5)
x2 = A()
with pytest.raises(TypeError):
s1 + x2
assert sparse.elemwise(operator.add, s1, x2) is NotImplemented
def test_elemwise_mixed_broadcast():
s1 = sparse.random((2, 3, 4), density=0.5)
s2 = sparse.random(4, density=0.5)
x3 = np.random.rand(3, 4)
x1 = s1.todense()
x2 = s2.todense()
def func(x1, x2, x3):
return x1 * x2 * x3
assert_eq(sparse.elemwise(func, s1, s2, x3), func(x1, x2, x3))
def test_elemwise_trinary(func, shape):
xs = sparse.random(shape, density=0.5)
ys = sparse.random(shape, density=0.5)
zs = sparse.random(shape, density=0.5)
x = xs.todense()
y = ys.todense()
z = zs.todense()
fs = sparse.elemwise(func, xs, ys, zs)
assert isinstance(fs, COO)
assert_eq(fs, func(x, y, z))
def test_elemwise_noargs():
def func():
return np.float_(5.0)
assert sparse.elemwise(func) == func()
