def _moveaxis(a, source: int, destination: int):
# simplified version of jnp.moveaxis() for local use.
_check_arraylike("moveaxis", a)
a = _asarray(a)
source = _canonicalize_axis(source, np.ndim(a))
destination = _canonicalize_axis(destination, np.ndim(a))
perm = [i for i in range(np.ndim(a)) if i != source]
perm.insert(destination, source)
return lax.transpose(a, perm)
def _cumulative_reduction(a,
axis: Optional[Union[int, Tuple[int,
...]]] = None,
dtype=None,
out=None):
_check_arraylike(np_reduction.__name__, a)
if out is not None:
raise NotImplementedError(
f"The 'out' argument to jnp.{np_reduction.__name__} "
f"is not supported.")
lax_internal._check_user_dtype_supported(dtype, np_reduction.__name__)
if axis is None or _isscalar(a):
a = lax.reshape(a, (np.size(a), ))
axis = 0
a_shape = list(np.shape(a))
num_dims = len(a_shape)
axis = _canonicalize_axis(axis, num_dims)
if fill_nan:
a = _where(lax_internal._isnan(a), _lax_const(a, fill_value), a)
if not dtype and dtypes.dtype(a) == np.bool_:
dtype = dtypes.canonicalize_dtype(dtypes.int_)
if dtype:
a = lax.convert_element_type(a, dtype)
return reduction(a, axis)
def _average(a,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
weights=None,
returned=False):
a = _asarray(a)
if weights is None: # Treat all weights as 1
avg = mean(a, axis=axis)
if axis is None:
weights_sum = lax.full((),
core.dimension_as_value(np.size(a)),
dtype=avg.dtype)
else:
weights_sum = lax.full_like(avg,
core.dimension_as_value(a.shape[axis]),
dtype=avg.dtype)
else:
weights = _asarray(weights)
if dtypes.issubdtype(a.dtype, np.inexact):
out_dtype = dtypes.result_type(a.dtype, weights.dtype)
else:
out_dtype = dtypes.result_type(a.dtype, weights.dtype,
dtypes.float_)
out_dtype = dtypes.canonicalize_dtype(out_dtype)
a_shape = np.shape(a)
a_ndim = len(a_shape)
weights_shape = np.shape(weights)
axis = None if axis is None else _canonicalize_axis(axis, a_ndim)
if a_shape != weights_shape:
# Make sure the dimensions work out
if axis is None:
raise ValueError("Axis must be specified when shapes of a and "
"weights differ.")
if len(weights_shape) != 1:
raise ValueError("1D weights expected when shapes of a and "
"weights differ.")
if not core.symbolic_equal_dim(weights_shape[0], a_shape[axis]):
raise ValueError("Length of weights not "
"compatible with specified axis.")
weights = _broadcast_to(weights,
(a_ndim - 1) * (1, ) + weights_shape)
weights = _moveaxis(weights, -1, axis)
weights_sum = sum(weights, axis=axis, dtype=out_dtype)
avg = sum(a * weights, axis=axis, dtype=out_dtype) / weights_sum
if returned:
if avg.shape != weights_sum.shape:
weights_sum = _broadcast_to(weights_sum, avg.shape)
return avg, weights_sum
return avg
def _canonicalize_axis_allow_named(x, rank):
return maybe_named_axis(x, lambda i: _canonicalize_axis(i, rank),
lambda name: name)