def __init__(self, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None, woodbury_inv=None, prior_mean=0):
        #obligatory
        self._K = K

        if ((woodbury_chol is not None) and (woodbury_vector is not None))\
                or ((woodbury_inv is not None) and (woodbury_vector is not None))\
                or ((woodbury_inv is not None) and (mean is not None))\
                or ((mean is not None) and (cov is not None)):
            pass # we have sufficient to compute the posterior
        else:
            raise ValueError("insufficient information to compute the posterior")

        self._K_chol = K_chol
        self._K = K
        #option 1:
        self._woodbury_chol = woodbury_chol
        self._woodbury_vector = woodbury_vector

        #option 2.
        self._woodbury_inv = woodbury_inv
        #and woodbury vector

        #option 2:
        self._mean = mean
        self._covariance = cov
        self._prior_mean = prior_mean

        #compute this lazily
        self._precision = None
Writes a single request log with currently known information.
dataset/ETHPy150Open SheffieldML/GPy/GPy/inference/latent_function_inference/posterior.py/Posterior.__init__--crossover with--dataset/ETHPy150Open AppScale/appscale/AppServer/google/appengine/api/logservice/logservice_stub.py/RequestLogWriter.write_request_info