<commit_before># Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)

from posterior import Posterior
from .../util.linalg import pdinv, dpotrs, tdot
log_2_pi = np.log(2*np.pi)

class DTCVar(object):
    """
    An object for inference when the likelihood is Gaussian, but we want to do sparse inference.

    The function self.inference returns a Posterior object, which summarizes
    the posterior.

    For efficiency, we sometimes work with the cholesky of Y*Y.T. To save repeatedly recomputing this, we cache it.

    """
    def __init__(self):
        self._YYTfactor_cache = caching.cache()
        self.const_jitter = 1e-6

     def get_YYTfactor(self, Y):
        """
        find a matrix L which satisfies LLT = YYT. 

        Note that L may have fewer columns than Y.
        """
        N, D = Y.shape
        if (N>D):
            return Y
        else:
            #if Y in self.cache, return self.Cache[Y], else stor Y in cache and return L.
            raise NotImplementedError, 'TODO' #TODO

    def inference(self, Kmm, Kmn, Knn_diag, likelihood, Y):

        num_inducing, num_data = Kmn.shape
        const_jitter = np.eye(num_inducing) * self.const_jitter

        #factor Kmm # TODO: cache?
        _Lm = jitchol(Kmm + _const_jitter)

        # The rather complex computations of A
        if has_uncertain_inputs:
            if likelihood.is_heteroscedastic:
                psi2_beta = (psi2 * (likelihood.precision.flatten().reshape(num_data, 1, 1))).sum(0)
            else:
                psi2_beta = psi2.sum(0) * likelihood.precision<commit_msg>lots of messing with the sparse inference method<commit_after># Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)

from posterior import Posterior
from .../util.linalg import pdinv, dpotrs, tdot
log_2_pi = np.log(2*np.pi)

class DTCVar(object):
    """
    An object for inference when the likelihood is Gaussian, but we want to do sparse inference.

    The function self.inference returns a Posterior object, which summarizes
    the posterior.

    For efficiency, we sometimes work with the cholesky of Y*Y.T. To save repeatedly recomputing this, we cache it.

    """
    def __init__(self):
        self._YYTfactor_cache = caching.cache()
        self.const_jitter = 1e-6

     def get_YYTfactor(self, Y):
        """
        find a matrix L which satisfies LLT = YYT. 

        Note that L may have fewer columns than Y.
        """
        N, D = Y.shape
        if (N>D):
            return Y
        else:
            #if Y in self.cache, return self.Cache[Y], else stor Y in cache and return L.
            raise NotImplementedError, 'TODO' #TODO

    def inference(self, Kmm, Kmn, Knn_diag, likelihood, Y):

        num_inducing, num_data = Kmn.shape

        #factor Kmm # TODO: cache?
        _Lm = jitchol(Kmm)

        # The rather complex computations of A
        if has_uncertain_inputs:
            if likelihood.is_heteroscedastic:
                psi2_beta = (psi2 * (likelihood.precision.flatten().reshape(num_data, 1, 1))).sum(0)
            else:
                psi2_beta = psi2.sum(0) * likelihood.precision
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