def _to_dense(self):
row = ops.convert_to_tensor(self.row)
col = ops.convert_to_tensor(self.col)
total_shape = array_ops.broadcast_dynamic_shape(
prefer_static.shape(row), prefer_static.shape(col))
n = prefer_static.shape(row)[-1]
row = _ops.broadcast_to(row, total_shape)
col = _ops.broadcast_to(col, total_shape)
# We concatenate the column in reverse order to the row.
# This gives us 2*n + 1 elements.
elements = prefer_static.concat(
[array_ops.reverse(col, axis=[-1]), row[..., 1:]], axis=-1)
# Given the above vector, the i-th row of the Toeplitz matrix
# is the last n elements of the above vector shifted i right
# (hence the first row is just the row vector provided, and
# the first element of each row will belong to the column vector).
# We construct these set of indices below.
indices = math_ops.mod(
# How much to shift right. This corresponds to `i`.
array_ops.range(0, n) +
# Specifies the last `n` indices.
array_ops.range(n - 1, -1, -1)[..., _ops.newaxis],
# Mod out by the total number of elements to ensure the index is
# non-negative (for tf.gather) and < 2 * n - 1.
2 * n - 1)
return array_ops.gather(elements, indices, axis=-1)
def _rotate_last_dim(x, rotate_right=False):
"""Rotate the last dimension either left or right."""
ndims = array_ops.rank(x)
if rotate_right:
transpose_perm = array_ops.concat(
[[ndims - 1], array_ops.range(0, ndims - 1)], axis=0)
else:
transpose_perm = array_ops.concat([array_ops.range(1, ndims), [0]],
axis=0)
return array_ops.transpose(x, transpose_perm)
def _trace(self):
# The diagonal of the [[nested] block] circulant operator is the mean of
# the spectrum.
# Proof: For the [0,...,0] element, this follows from the IDFT formula.
# Then the result follows since all diagonal elements are the same.
# Therefore, the trace is the sum of the spectrum.
# Get shape of diag along with the axis over which to reduce the spectrum.
# We will reduce the spectrum over all block indices.
if tensor_shape.TensorShape(self.spectrum.shape).is_fully_defined():
spec_rank = tensor_shape.TensorShape(self.spectrum.shape).ndims
axis = np.arange(spec_rank - self.block_depth,
spec_rank,
dtype=np.int32)
else:
spec_rank = array_ops.rank(self.spectrum)
axis = array_ops.range(spec_rank - self.block_depth, spec_rank)
# Real diag part "re_d".
# Suppose tensor_shape.TensorShape(spectrum.shape) = [B1,...,Bb, N1, N2]
# tensor_shape.TensorShape(self.shape) = [B1,...,Bb, N, N], with N1 * N2 = N.
# tensor_shape.TensorShape(re_d_value.shape) = [B1,...,Bb]
re_d_value = math_ops.reduce_sum(math_ops.real(self.spectrum),
axis=axis)
if not np.issubdtype(self.dtype, np.complexfloating):
return _ops.cast(re_d_value, self.dtype)
# Imaginary part, "im_d".
if self.is_self_adjoint:
im_d_value = array_ops.zeros_like(re_d_value)
else:
im_d_value = math_ops.reduce_sum(math_ops.imag(self.spectrum),
axis=axis)
return _ops.cast(math_ops.complex(re_d_value, im_d_value), self.dtype)