def repeat_to_match_shape(g, shape, dtype, axis, keepdims):
"""Returns the array g repeated along axis to fit vector space vs.
Also returns the number of repetitions of the array."""
with ua.set_backend(numpy_backend, coerce=True):
if shape == ():
return g, 1
axis = list(axis) if isinstance(axis, tuple) else axis
new_shape = np.array(shape, dtype=int)
new_shape[axis] = 1
num_reps = np.prod(np.array(shape)[axis])
return np.broadcast_to(np.reshape(g, new_shape), shape), num_reps
def var(self, value):
if not isinstance(value, Variable):
raise ValueError("var must be a Variable")
if self._var is not None:
raise ValueError("variable already set")
self._var = value
self.diffs[value] = np.array(1)
def jvp(g):
if axis is None:
num_reps = np.size(g)
elif isinstance(axis, int):
num_reps = np.shape(g)[axis]
elif isinstance(axis, tuple):
num_reps = np.prod(np.array(np.shape(g))[list(axis)])
if num_reps <= 1:
return np.zeros_like(ans)
x_minus_mean = np.conj(x - np.mean(x, axis=axis, keepdims=True))
return np.sum(np.real(g * x_minus_mean), axis=axis,
keepdims=keepdims) / ((num_reps - ddof) * ans)
raise
pytest.xfail(
reason="The backend has no implementation for this ufunc.")
assert all(isinstance(arr, types) for arr in ret)
for arr in ret:
if isinstance(arr, da.Array):
arr.compute()
@pytest.mark.parametrize(
"method, args, kwargs",
[
(np.empty, (2, ), {}),
(np.empty_like, (np.array([1, 2, 3]), ), {}),
(np.eye, (2, ), {}),
(np.full, ((1, 2, 3), 1.3), {}),
(np.ones, ((1, 2, 3), ), {}),
(np.zeros, ((1, 2, 3), ), {}),
],
)
def test_array_creation(backend, method, args, kwargs):
backend, types = backend
for dtype in dtypes:
try:
with ua.set_backend(backend, coerce=True):
kwargs["dtype"] = dtype
ret = method(*args, **kwargs)
except ua.BackendNotImplementedError:
if backend in FULLY_TESTED_BACKENDS and (backend,
(np.conjugate, ([1.0 + 2.0j, -1.0 - 1j],), {}, [1.0 - 2.0j, -1.0 + 1j]),
(np.conj, ([1.0 + 2.0j, -1.0 - 1j],), {}, [1.0 - 2.0j, -1.0 + 1j]),
(np.exp, ([0, 1, 2],), {}, [1.0, 2.718281828459045, 7.38905609893065]),
(np.exp2, ([3, 4],), {}, [8, 16]),
(np.log, ([1.0, np.e, np.e ** 2],), {}, [0.0, 1.0, 2.0]),
(np.log2, ([1, 2, 2 ** 4],), {}, [0.0, 1.0, 4.0]),
(np.log10, ([1e-5, -3.0],), {}, [-5.0, np.NaN]),
(np.sqrt, ([1, 4, 9],), {}, [1, 2, 3]),
(np.square, ([2, 3, 4],), {}, [4, 9, 16]),
(np.cbrt, ([1, 8, 27],), {}, [1.0, 2.0, 3.0]),
(np.reciprocal, ([1.0, 2.0, 4.0],), {}, [1.0, 0.5, 0.25]),
(
np.broadcast_to,
([1, 2, 3], (3, 3)),
{},
np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]]),
),
(
np.degrees,
([0.0, 0.52359878, 1.04719755, 1.57079633],),
{},
[0.0, 30.0, 60.0, 90.0],
),
(
np.radians,
([0.0, 30.0, 60.0, 90.0],),
{},
[0.0, 0.52359878, 1.04719755, 1.57079633],
),
],
)