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 utils.tensor_to_ndarray(tf.linalg.diag_part(a))
a = moveaxis(utils.tensor_to_ndarray(a), (axis1, axis2), (-2, -1)).data
a_shape = tf.shape(a)
def _zeros(): # pylint: disable=missing-docstring
return (tf.zeros(tf.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 = utils.cond(
utils.logical_or(
utils.less_equal(offset, -1 * utils.getitem(a_shape, -2)),
utils.greater_equal(offset, utils.getitem(a_shape, -1)),
), _zeros, lambda: (a, offset))
a = utils.tensor_to_ndarray(tf.linalg.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 = tf.rank(v)
v.shape.with_rank_at_most(2)
# TODO(nareshmodi): Consider a utils.Assert version that will fail during
# tracing time if the shape is known.
tf.debugging.Assert(
utils.logical_or(tf.equal(v_rank, 1), tf.equal(v_rank, 2)), [v_rank])
def _diag(v, k):
return utils.cond(
tf.equal(tf.size(v), 0),
lambda: tf.zeros([abs(k), abs(k)], dtype=v.dtype),
lambda: tf.linalg.diag(v, k=k))
def _diag_part(v, k):
v_shape = tf.shape(v)
v, k = utils.cond(
utils.logical_or(
utils.less_equal(k, -1 * utils.getitem(v_shape, 0)),
utils.greater_equal(k, utils.getitem(v_shape, 1)),
), lambda: (tf.zeros([0, 0], dtype=v.dtype), 0), lambda: (v, k))
result = tf.linalg.diag_part(v, k=k)
return result
result = utils.cond(
tf.equal(v_rank, 1), lambda: _diag(v, k), lambda: _diag_part(v, k))
return utils.tensor_to_ndarray(result)
def f(a, b): # pylint: disable=missing-docstring
return utils.cond(
utils.logical_or(tf.rank(a) == 0,
tf.rank(b) == 0),
lambda: a * b,
lambda: utils.cond( # pylint: disable=g-long-lambda
tf.rank(b) == 1, lambda: tf.tensordot(a, b, axes=[[-1], [-1]]),
lambda: tf.tensordot(a, b, axes=[[-1], [-2]])))
def _diag_part(v, k):
v_shape = tf.shape(v)
v, k = utils.cond(
utils.logical_or(
utils.less_equal(k, -1 * utils.getitem(v_shape, 0)),
utils.greater_equal(k, utils.getitem(v_shape, 1)),
), lambda: (tf.zeros([0, 0], dtype=v.dtype), 0), lambda: (v, k))
result = tf.linalg.diag_part(v, k=k)
return result
def trace(a, offset=0, axis1=0, axis2=1, dtype=None): # pylint: disable=missing-docstring
a = array_ops.asarray(a).data
if offset == 0:
a_shape = a.shape
if a_shape.rank is not None:
rank = len(a_shape)
if (axis1 == -2 or axis1 == rank - 2) and (axis2 == -1
or axis2 == rank - 1):
return utils.tensor_to_ndarray(tf.linalg.trace(a))
a_rank = tf.rank(a)
if axis1 < 0:
axis1 += a_rank
if axis2 < 0:
axis2 += a_rank
minaxis = tf.minimum(axis1, axis2)
maxaxis = tf.maximum(axis1, axis2)
# Move axes of interest to the end.
range_rank = tf.range(a_rank)
perm = tf.concat([
range_rank[0:minaxis], range_rank[minaxis + 1:maxaxis],
range_rank[maxaxis + 1:], [axis1, axis2]
],
axis=0)
a = tf.transpose(a, perm)
a_shape = tf.shape(a)
# 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 = utils.cond(
utils.logical_or(
utils.less_equal(offset, -1 * utils.getitem(a_shape, -2)),
utils.greater_equal(offset, utils.getitem(a_shape, -1)),
), lambda: (tf.zeros_like(a), 0), lambda: (a, offset))
a = utils.tensor_to_ndarray(tf.linalg.diag_part(a, k=offset))
return array_ops.sum(a, -1, dtype)
def f(a, b):
return utils.cond(utils.logical_or(tf.rank(a) == 0,
tf.rank(b) == 0), lambda: a * b,
lambda: tf.tensordot(a, b, axes=[[-1], [-1]]))
