conditionals.py 文件源码

def conditional(Xnew, X, kern, f, *, full_cov=False, q_sqrt=None, white=False):
    """
    Given f, representing the GP at the points X, produce the mean and
    (co-)variance of the GP at the points Xnew.

    Additionally, there may be Gaussian uncertainty about f as represented by
    q_sqrt. In this case `f` represents the mean of the distribution and
    q_sqrt the square-root of the covariance.

    Additionally, the GP may have been centered (whitened) so that
        p(v) = N(0, I)
        f = L v
    thus
        p(f) = N(0, LL^T) = N(0, K).
    In this case `f` represents the values taken by v.

    The method can either return the diagonals of the covariance matrix for
    each output (default) or the full covariance matrix (full_cov=True).

    We assume K independent GPs, represented by the columns of f (and the
    last dimension of q_sqrt).

    :param Xnew: data matrix, size N x D.
    :param X: data points, size M x D.
    :param kern: GPflow kernel.
    :param f: data matrix, M x K, representing the function values at X,
        for K functions.
    :param q_sqrt: matrix of standard-deviations or Cholesky matrices,
        size M x K or M x M x K.
    :param white: boolean of whether to use the whitened representation as
        described above.

    :return: two element tuple with conditional mean and variance.
    """
    num_data = tf.shape(X)[0]  # M
    Kmm = kern.K(X) + tf.eye(num_data, dtype=settings.float_type) * settings.numerics.jitter_level
    Kmn = kern.K(X, Xnew)
    if full_cov:
        Knn = kern.K(Xnew)
    else:
        Knn = kern.Kdiag(Xnew)
    return base_conditional(Kmn, Kmm, Knn, f, full_cov=full_cov, q_sqrt=q_sqrt, white=white)