def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
sess = ops.get_default_session()
shape = list(shape)
# Either 1 or 2 matrices, depending.
num_operators = rng.randint(low=1, high=3)
matrices = [
linear_operator_test_util.random_positive_definite_matrix(shape, dtype, force_well_conditioned=True)
for _ in range(num_operators)
]
if use_placeholder:
matrices_ph = [array_ops.placeholder(dtype=dtype) for _ in range(num_operators)]
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrices = sess.run(matrices)
operator = linalg.LinearOperatorComposition([linalg.LinearOperatorMatrix(m_ph) for m_ph in matrices_ph])
feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)}
else:
operator = linalg.LinearOperatorComposition([linalg.LinearOperatorMatrix(m) for m in matrices])
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated each matrix to a numpy array.
apply_order_list = list(reversed(matrices))
mat = ops.convert_to_tensor(apply_order_list[0])
for other_mat in apply_order_list[1:]:
mat = math_ops.matmul(other_mat, mat)
return operator, mat, feed_dict
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
shape = list(shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
if use_placeholder:
matrix_ph = array_ops.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
# is_square is auto-set because of self_adjoint/pd.
operator = linalg.LinearOperatorFullMatrix(
matrix_ph, is_self_adjoint=True, is_positive_definite=True)
feed_dict = {matrix_ph: matrix}
else:
operator = linalg.LinearOperatorFullMatrix(
matrix, is_self_adjoint=True, is_positive_definite=True)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = ops.convert_to_tensor(matrix)
return operator, mat, feed_dict
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
sess = tf.get_default_session()
shape = list(shape)
# Either 1 or 2 matrices, depending.
num_operators = rng.randint(low=1, high=3)
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
for _ in range(num_operators)
]
if use_placeholder:
matrices_ph = [tf.placeholder(dtype=dtype) for _ in range(num_operators)]
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrices = sess.run(matrices)
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorMatrix(m_ph) for m_ph in matrices_ph])
feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)}
else:
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorMatrix(m) for m in matrices])
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated each matrix to a numpy array.
apply_order_list = list(reversed(matrices))
mat = tf.convert_to_tensor(apply_order_list[0])
for other_mat in apply_order_list[1:]:
mat = tf.matmul(other_mat, mat)
return operator, mat, feed_dict
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
shape = list(shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype)
if use_placeholder:
matrix_ph = tf.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
operator = linalg.LinearOperatorMatrix(matrix)
feed_dict = {matrix_ph: matrix}
else:
operator = linalg.LinearOperatorMatrix(matrix)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = tf.convert_to_tensor(matrix)
return operator, mat, feed_dict
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
shape = list(shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype)
if use_placeholder:
matrix_ph = tf.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
operator = linalg.LinearOperatorMatrix(matrix)
feed_dict = {matrix_ph: matrix}
else:
operator = linalg.LinearOperatorMatrix(matrix)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = tf.convert_to_tensor(matrix)
return operator, mat, feed_dict
