T3Tangent.probe_derivatives =========================== .. py:method:: t3toolbox.manifold.T3Tangent.probe_derivatives(ww, pp, order) .. code-block:: python def probe_derivatives( self, ww: typ.Sequence[NDArray], # probe vectors X, len=d, elm_shape=W+(Ni,) pp: typ.Sequence[NDArray], # perturbation vectors P, len=d, elm_shape=W+(Ni,) order: int, # highest derivative order ) -> typ.Sequence[NDArray]: # len=d, elm_shape=(order+1,)+W+K+C+(Ni,) Symmetric directional derivatives of probing this tangent vector, in one repeated direction. Returns, for each mode ``i``, the stack ``y_i^(t) = d^t/ds^t [probe(X + s P)]_i|_0`` for ``t=0..order`` -- the derivative analogue of :py:meth:`probe`, obtained by perturbing every probe vector in the same direction ``P``. Index ``0`` is the ordinary :py:meth:`probe`. Stacks ``order + W + K + C`` (order outermost; sample stack ``W``, tangent stack ``K``, frame stack ``C``). The points ``X`` (``ww``) and the perturbations ``P`` (``pp``) must share the sample stack ``W``. See ``docs/symmetric_probe_derivatives.tex``. .. seealso:: :py:obj:`probe`, :py:obj:`apply_derivatives`, :py:obj:`probe_derivatives_transpose` .. rubric:: Examples >>> import numpy as np >>> import t3toolbox.tucker_tensor_train as t3 >>> import t3toolbox.frame_variations_format as bvf >>> import t3toolbox.manifold as t3m >>> np.random.seed(0) >>> x = t3.TuckerTensorTrain.randn((10, 11, 12), (5, 6, 4), (1, 2, 3, 1)) >>> frame, variations = bvf.t3_orthogonal_representations(x) >>> v = t3m.T3Tangent(frame, variations) >>> ww = (np.random.randn(10), np.random.randn(11), np.random.randn(12)) >>> pp = (np.random.randn(10), np.random.randn(11), np.random.randn(12)) >>> zj = v.probe_derivatives(ww, pp, 3) >>> print([z.shape for z in zj]) # (order+1,) + (Ni,) [(4, 10), (4, 11), (4, 12)] >>> print([bool(np.allclose(z[0], z0)) for z, z0 in zip(zj, v.probe(ww))]) # order 0 == probe [True, True, True]