kernels.py 文件源码

def kfucompDer(dL_dkfu, kfu, variance, lengthscale, Z, mu, grad_x):
    # here are the "statistics" for psi1
    # Produced intermediate results: dL_dparams w.r.t. psi1
    # _dL_dvariance     1
    # _dL_dlengthscale  Q
    # _dL_dZ            MxQ

    lengthscale2 = np.square(lengthscale)

    Lpsi1 = dL_dkfu * kfu
    Zmu = Z[None, :, :] - mu[:, None, :]  # NxMxQ
    _dL_dvar = Lpsi1.sum() / variance
    _dL_dZ = -np.einsum('nm,nmq->mq', Lpsi1, Zmu / lengthscale2)
    _dL_dl = np.einsum('nm,nmq->q', Lpsi1, np.square(Zmu) / lengthscale**3)
    if grad_x:
        _dL_dx = np.einsum('nm,nmq->nq', Lpsi1, Zmu / lengthscale2)
        return _dL_dvar, _dL_dl, _dL_dZ, _dL_dx
    else:
        return _dL_dvar, _dL_dl, _dL_dZ