mvnormal.py 文件源码

def _covariance_factor(Sigma):
  # Assume it is positive-definite and try Cholesky decomposition.
  try:
    return Covariance_Cholesky(Sigma)
  except la.LinAlgError:
    pass

  # XXX In the past, we tried LU decomposition if, owing to
  # floating-point rounding error, the matrix is merely nonsingular,
  # not positive-definite.  However, empirically, that seemed to lead
  # to bad numerical results.  Until we have better numerical analysis
  # of the situation, let's try just falling back to least-squares
  # pseudoinverse approximation.

  # Otherwise, fall back to whatever heuristics scipy can manage.
  return Covariance_Loser(Sigma)