tensorflow_probability/python/internal/backend/numpy/linalg.py
def _matmul(a, b,
            transpose_a=False, transpose_b=False,
            adjoint_a=False, adjoint_b=False,
            a_is_sparse=False, b_is_sparse=False,
            name=None):  # pylint: disable=unused-argument
  """Numpy matmul wrapper."""
  if a_is_sparse or b_is_sparse:
    raise NotImplementedError('Numpy backend does not support sparse matmul.')
  if transpose_a or adjoint_a:
    a = _matrix_transpose(a, conjugate=adjoint_a)
  if transpose_b or adjoint_b:
    b = _matrix_transpose(b, conjugate=adjoint_b)
  return np.matmul(a, b)
def _matmul(a, b,
            transpose_a=False, transpose_b=False,
            adjoint_a=False, adjoint_b=False,
            a_is_sparse=False, b_is_sparse=False,
            name=None):  # pylint: disable=unused-argument
  if a_is_sparse or b_is_sparse:
    raise NotImplementedError('Numpy backend does not support sparse matmul.')
  if transpose_a or adjoint_a:
    a = _matrix_transpose(a, conjugate=adjoint_a)
  if transpose_b or adjoint_b:
    b = _matrix_transpose(b, conjugate=adjoint_b)
  return np.matmul(a, b)
def _matmul ( a , b , transpose_a = False , transpose_b = False , adjoint_a = False , adjoint_b = False , a_is_sparse = False , b_is_sparse = False , name = None ) : # pylint: disable=unused-argument if a_is_sparse or b_is_sparse : raise NotImplementedError ( 'Numpy backend does not support sparse matmul.' ) if transpose_a or adjoint_a : a = _matrix_transpose ( a , conjugate = adjoint_a ) if transpose_b or adjoint_b : b = _matrix_transpose ( b , conjugate = adjoint_b ) return np . matmul ( a , b )
Numpy matmul wrapper.
Numpy matmul wrapper .