def diagonal(a, offset=0, axis1=0, axis2=1): # pylint: disable=missing-docstring
a = asarray(a).data
maybe_rank = a.shape.rank
if maybe_rank is not None and offset == 0 and (
axis1 == maybe_rank - 2
or axis1 == -2) and (axis2 == maybe_rank - 1 or axis2 == -1):
return np_utils.tensor_to_ndarray(array_ops.matrix_diag_part(a))
a = moveaxis(np_utils.tensor_to_ndarray(a), (axis1, axis2), (-2, -1)).data
a_shape = array_ops.shape(a)
def _zeros(): # pylint: disable=missing-docstring
return (array_ops.zeros(array_ops.concat([a_shape[:-1], [0]], 0),
dtype=a.dtype), 0)
# All zeros since diag_part doesn't handle all possible k (aka offset).
# Written this way since cond will run shape inference on both branches,
# and diag_part shape inference will fail when offset is out of bounds.
a, offset = np_utils.cond(
np_utils.logical_or(
np_utils.less_equal(offset, -1 * np_utils.getitem(a_shape, -2)),
np_utils.greater_equal(offset, np_utils.getitem(a_shape, -1)),
), _zeros, lambda: (a, offset))
a = np_utils.tensor_to_ndarray(array_ops.matrix_diag_part(a, k=offset))
return a
def diag(v, k=0): # pylint: disable=missing-docstring
"""Raises an error if input is not 1- or 2-d."""
v = asarray(v).data
v_rank = array_ops.rank(v)
v.shape.with_rank_at_most(2)
# TODO(nareshmodi): Consider a np_utils.Assert version that will fail during
# tracing time if the shape is known.
control_flow_ops.Assert(
np_utils.logical_or(math_ops.equal(v_rank, 1),
math_ops.equal(v_rank, 2)), [v_rank])
def _diag(v, k):
return np_utils.cond(
math_ops.equal(array_ops.size(v), 0),
lambda: array_ops.zeros([abs(k), abs(k)], dtype=v.dtype),
lambda: array_ops.matrix_diag(v, k=k))
def _diag_part(v, k):
v_shape = array_ops.shape(v)
v, k = np_utils.cond(
np_utils.logical_or(
np_utils.less_equal(k, -1 * np_utils.getitem(v_shape, 0)),
np_utils.greater_equal(k, np_utils.getitem(v_shape, 1)),
), lambda: (array_ops.zeros([0, 0], dtype=v.dtype), 0), lambda:
(v, k))
result = array_ops.matrix_diag_part(v, k=k)
return result
result = np_utils.cond(math_ops.equal(v_rank, 1), lambda: _diag(v, k),
lambda: _diag_part(v, k))
return np_utils.tensor_to_ndarray(result)
def f(a, b): # pylint: disable=missing-docstring
return np_utils.cond(
np_utils.logical_or(math_ops.equal(array_ops.rank(a), 0),
math_ops.equal(array_ops.rank(b), 0)),
lambda: a * b,
lambda: np_utils.cond( # pylint: disable=g-long-lambda
math_ops.equal(array_ops.rank(b), 1), lambda: math_ops.
tensordot(a, b, axes=[[-1], [-1]]), lambda: math_ops.tensordot(
a, b, axes=[[-1], [-2]])))
def _diag_part(v, k):
v_shape = array_ops.shape(v)
v, k = np_utils.cond(
np_utils.logical_or(
np_utils.less_equal(k, -1 * np_utils.getitem(v_shape, 0)),
np_utils.greater_equal(k, np_utils.getitem(v_shape, 1)),
), lambda: (array_ops.zeros([0, 0], dtype=v.dtype), 0), lambda:
(v, k))
result = array_ops.matrix_diag_part(v, k=k)
return result
def f(a, b):
return np_utils.cond(
np_utils.logical_or(math_ops.equal(array_ops.rank(a), 0),
math_ops.equal(array_ops.rank(b), 0)),
lambda: a * b, lambda: math_ops.tensordot(a, b, axes=[[-1], [-1]]))
