def test_numba_vdot():
for a, b in ((np.array([1 + 2j, 3 + 4j]), np.array([5 + 6j, 7 + 8j])),
(np.array([[1, 4], [5, 6]]), np.array([[4, 1], [2, 2]]))):
result_type, result = numba_vdot(a, b)
assert result == np.vdot(a, b)
assert result_type == typesystem.from_numpy_dtype(a.dtype).dtype
result_type, result = numba_vdot(b, a)
assert result == np.vdot(b, a)
assert result_type == typesystem.from_numpy_dtype(b.dtype).dtype
def resolve_attribute_dtype(dtype, default=None):
"Resolve the type for numpy dtype attributes"
if dtype.is_numpy_dtype:
return dtype
if dtype.is_numpy_attribute:
numpy_attr = getattr(dtype.module, dtype.attr, None)
if isinstance(numpy_attr, np.dtype):
return typesystem.from_numpy_dtype(numpy_attr)
elif issubclass(numpy_attr, np.generic):
return typesystem.from_numpy_dtype(np.dtype(numpy_attr))
def test_numba_vdot():
for a, b in ((np.array([1+2j,3+4j]),
np.array([5+6j,7+8j])),
(np.array([[1, 4], [5, 6]]),
np.array([[4, 1], [2, 2]]))):
result_type, result = numba_vdot(a, b)
assert result == np.vdot(a, b)
assert result_type == typesystem.from_numpy_dtype(a.dtype).dtype
result_type, result = numba_vdot(b, a)
assert result == np.vdot(b, a)
assert result_type == typesystem.from_numpy_dtype(b.dtype).dtype
def test_numba_inner():
# Note these tests assume that the lhs' type is the same as the
# promotion type for both arguments. They will fail if additional
# test data doesn't adhere to this policy.
for a, b in ((np.array([1, 2,
3]), np.array([0, 1, 0])), (np.arange(24).reshape(
(2, 3, 4)), np.arange(4)), (np.eye(2), 7)):
result_type, result = numba_inner(a, b)
if result_type.is_array:
assert (result == np.inner(a, b)).all()
assert (result_type.dtype == typesystem.from_numpy_dtype(
result.dtype).dtype)
assert (result_type.dtype == typesystem.from_numpy_dtype(
a.dtype).dtype)
else:
assert result == np.inner(a, b)
assert result_type == typesystem.from_numpy_dtype(a.dtype).dtype
def test_numba_inner():
# Note these tests assume that the lhs' type is the same as the
# promotion type for both arguments. They will fail if additional
# test data doesn't adhere to this policy.
for a, b in ((np.array([1,2,3]), np.array([0,1,0])),
(np.arange(24).reshape((2,3,4)), np.arange(4)),
(np.eye(2), 7)):
result_type, result = numba_inner(a, b)
if result_type.is_array:
assert (result == np.inner(a, b)).all()
assert (result_type.dtype ==
typesystem.from_numpy_dtype(result.dtype).dtype)
assert (result_type.dtype ==
typesystem.from_numpy_dtype(a.dtype).dtype)
else:
assert result == np.inner(a, b)
assert result_type == typesystem.from_numpy_dtype(a.dtype).dtype
def test_numba_outer():
for a, b in ((np.ones((5, )), np.linspace(-2, 2, 5)),
(1j * np.linspace(2, -2, 5), np.ones(
(5, ))), (np.array(['a', 'b', 'c'], dtype=object),
np.arange(1, 4)), (np.array([1]), 1), (np.ones(
(2, 2, 2)), np.linspace(-2, 2, 5))):
result_type, result = numba_outer(a, b)
assert (result == np.outer(a, b)).all()
assert (result_type.is_array and result_type.ndim == 2)
assert result_type.dtype == typesystem.from_numpy_dtype(a.dtype).dtype
def test_numba_outer():
for a, b in ((np.ones((5,)), np.linspace(-2, 2, 5)),
(1j * np.linspace(2, -2, 5), np.ones((5,))),
(np.array(['a', 'b', 'c'], dtype=object), np.arange(1,4)),
(np.array([1]), 1),
(np.ones((2,2,2)), np.linspace(-2, 2, 5))):
result_type, result = numba_outer(a, b)
assert (result == np.outer(a, b)).all()
assert (result_type.is_array and result_type.ndim == 2)
assert result_type.dtype == typesystem.from_numpy_dtype(a.dtype).dtype
def resolve_attribute_dtype(dtype, default=None):
"Resolve the type for numpy dtype attributes"
if dtype.is_numpy_dtype:
return dtype
if dtype.is_known_value:
numpy_attr = dtype.value
if isinstance(numpy_attr, np.dtype):
return typesystem.from_numpy_dtype(numpy_attr)
elif issubclass(numpy_attr, np.generic):
return typesystem.from_numpy_dtype(np.dtype(numpy_attr))
elif numpy_attr is not None:
try:
dtype = np.dtype(numpy_attr)
except TypeError:
warnings.warn("Unable to infer dtype for '%s'" % numpy_attr)
else:
return typesystem.from_numpy_dtype(dtype)
return None
def test_numba_tensordot2():
A = np.array(1)
B = np.array(2)
dtype = typesystem.from_numpy_dtype(A.dtype).dtype
for i in range(2, 5):
for j in range(2, 5):
shape_A = (1, ) * i
shape_B = (1, ) * j
x = A.reshape(*shape_A)
y = B.reshape(*shape_B)
result_type, result = numba_tensordot2(x, y)
control = np.tensordot(x, y)
assert result == control
#assert result_type == numba.typeof(control)
if i + j - 4 > 0:
assert result.ndim == result_type.ndim
else:
assert result_type == dtype
def test_numba_tensordot2():
A = np.array(1)
B = np.array(2)
dtype = typesystem.from_numpy_dtype(A.dtype).dtype
for i in range(2, 5):
for j in range(2, 5):
shape_A = (1,) * i
shape_B = (1,) * j
x = A.reshape(*shape_A)
y = B.reshape(*shape_B)
result_type, result = numba_tensordot2(x, y)
control = np.tensordot(x, y)
assert result == control
#assert result_type == numba.typeof(control)
if i + j - 4 > 0:
assert result.ndim == result_type.ndim
else:
assert result_type == dtype
def test_numba_dot():
A = np.array(1)
B = np.array(2)
dtype = typesystem.from_numpy_dtype(A.dtype).dtype
for i in range(1, 5):
for j in range(1, 5):
# print i, j
shape_A = (1, ) * i
shape_B = (1, ) * j
x = A.reshape(*shape_A)
y = B.reshape(*shape_B)
result_type, result = numba_dot(x, y)
assert result == np.dot(x, y)
if i + j - 2 > 0:
assert result.ndim == result_type.ndim
else:
assert result_type == dtype
def test_numba_dot():
A = np.array(1)
B = np.array(2)
dtype = typesystem.from_numpy_dtype(A.dtype).dtype
for i in range(1, 5):
for j in range(1, 5):
# print i, j
shape_A = (1,) * i
shape_B = (1,) * j
x = A.reshape(*shape_A)
y = B.reshape(*shape_B)
result_type, result = numba_dot(x, y)
assert result == np.dot(x, y)
if i + j - 2 > 0:
assert result.ndim == result_type.ndim
else:
assert result_type == dtype, (result_type, dtype)
