conditionals.py 文件源码

def base_conditional(Kmn, Kmm, Knn, f, *, full_cov=False, q_sqrt=None, white=False):
    # compute kernel stuff
    num_func = tf.shape(f)[1]  # K
    Lm = tf.cholesky(Kmm)

    # Compute the projection matrix A
    A = tf.matrix_triangular_solve(Lm, Kmn, lower=True)

    # compute the covariance due to the conditioning
    if full_cov:
        fvar = Knn - tf.matmul(A, A, transpose_a=True)
        shape = tf.stack([num_func, 1, 1])
    else:
        fvar = Knn - tf.reduce_sum(tf.square(A), 0)
        shape = tf.stack([num_func, 1])
    fvar = tf.tile(tf.expand_dims(fvar, 0), shape)  # K x N x N or K x N

    # another backsubstitution in the unwhitened case
    if not white:
        A = tf.matrix_triangular_solve(tf.transpose(Lm), A, lower=False)

    # construct the conditional mean
    fmean = tf.matmul(A, f, transpose_a=True)

    if q_sqrt is not None:
        if q_sqrt.get_shape().ndims == 2:
            LTA = A * tf.expand_dims(tf.transpose(q_sqrt), 2)  # K x M x N
        elif q_sqrt.get_shape().ndims == 3:
            L = tf.matrix_band_part(tf.transpose(q_sqrt, (2, 0, 1)), -1, 0)  # K x M x M
            A_tiled = tf.tile(tf.expand_dims(A, 0), tf.stack([num_func, 1, 1]))
            LTA = tf.matmul(L, A_tiled, transpose_a=True)  # K x M x N
        else:  # pragma: no cover
            raise ValueError("Bad dimension for q_sqrt: %s" %
                             str(q_sqrt.get_shape().ndims))
        if full_cov:
            fvar = fvar + tf.matmul(LTA, LTA, transpose_a=True)  # K x N x N
        else:
            fvar = fvar + tf.reduce_sum(tf.square(LTA), 1)  # K x N
    fvar = tf.transpose(fvar)  # N x K or N x N x K

    return fmean, fvar