def add_matrices(a, b):
if isinstance(a, scisp.spmatrix) and isinstance(b, scisp.spmatrix):
return a.tocsr() + b.tocsr()
elif isinstance(a, sparse.COO) and isinstance(b, sparse.COO):
return sparse.elemwise(np.add, a, b)
else:
logger.error('Adding two different matrix types is not supported')
def test_trinary_broadcasting(shapes, args):
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(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_elemwise_trinary(func, shape, formats):
xs, ys, zs = xyz
x = xs.todense()
y = ys.todense()
z = zs.todense()
fs = sparse.elemwise(func, xs, ys, zs)
assert_eq(fs, func(x, y, z))
def test_elemwise_mixed_broadcast(s1, s2):
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_unsupported(s1):
class A:
pass
x2 = A()
with pytest.raises(TypeError):
s1 + x2
assert sparse.elemwise(operator.add, s1, x2) is NotImplemented
def test_elemwise_trinary(func, shape, formats):
xs = sparse.random(shape, density=0.5, format=formats[0])
ys = sparse.random(shape, density=0.5, format=formats[1])
zs = sparse.random(shape, density=0.5, format=formats[2])
x = xs.todense()
y = ys.todense()
z = zs.todense()
fs = sparse.elemwise(func, xs, ys, zs)
assert_eq(fs, func(x, y, z))
def test_elemwise_unsupported(format):
class A:
pass
s1 = sparse.random((2, 3, 4), density=0.5, format=format)
x2 = A()
with pytest.raises(TypeError):
s1 + x2
assert sparse.elemwise(operator.add, s1, x2) is NotImplemented
def test_elemwise_mixed_broadcast(format):
s1 = sparse.random((2, 3, 4), density=0.5, format=format)
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_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))
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)
if format == "gcxs":
assert isinstance(fs, GCXS)
else:
assert isinstance(fs, COO)
assert_eq(fs, x + y)
def where(*args, **kwargs):
return elemwise(np.where, *args, **kwargs)
def test_elemwise_noargs():
def func():
return np.float_(5.0)
assert_eq(sparse.elemwise(func), func())
