# Copyright 2016 James Hensman, Valentine Svensson, alexggmatthews
# Copyright 2017 Artem Artemev @awav
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


from __future__ import print_function, absolute_import
from functools import reduce
import warnings

import tensorflow as tf
import numpy as np

from . import transforms
from . import settings

from .params import Parameter, Parameterized
from .decors import params_as_tensors, autoflow


class Kernel(Parameterized):
    """
    The basic kernel class. Handles input_dim and active dims, and provides a
    generic '_slice' function to implement them.
    """

    def __init__(self, input_dim, active_dims=None, name=None):
        """
        input dim is an integer
        active dims is either an iterable of integers or None.

        Input dim is the number of input dimensions to the kernel. If the
        kernel is computed on a matrix X which has more columns than input_dim,
        then by default, only the first input_dim columns are used. If
        different columns are required, then they may be specified by
        active_dims.

        If active dims is None, it effectively defaults to range(input_dim),
        but we store it as a slice for efficiency.
        """
        super().__init__(name=name)
        self.input_dim = int(input_dim)
        if active_dims is None:
            self.active_dims = slice(input_dim)
        elif isinstance(active_dims, slice):
            self.active_dims = active_dims
            if active_dims.start is not None and active_dims.stop is not None and active_dims.step is not None:
                assert len(range(*active_dims)) == input_dim  # pragma: no cover
        else:
            self.active_dims = np.array(active_dims, dtype=np.int32)
            assert len(active_dims) == input_dim

        self.num_gauss_hermite_points = 20

    @autoflow((settings.float_type, [None, None]),
              (settings.float_type, [None, None]))
    def compute_K(self, X, Z):
        return self.K(X, Z)

    @autoflow((settings.float_type, [None, None]))
    def compute_K_symm(self, X):
        return self.K(X)

    @autoflow((settings.float_type, [None, None]))
    def compute_Kdiag(self, X):
        return self.Kdiag(X)

    def on_separate_dims(self, other_kernel):
        """
        Checks if the dimensions, over which the kernels are specified, overlap.
        Returns True if they are defined on different/separate dimensions and False otherwise.
        """
        if isinstance(self.active_dims, slice) or isinstance(other_kernel.active_dims, slice):
            # Be very conservative for kernels defined over slices of dimensions
            return False

        if np.any(self.active_dims.reshape(-1, 1) == other_kernel.active_dims.reshape(1, -1)):
            return False

        return True

    def _slice(self, X, X2):
        """
        Slice the correct dimensions for use in the kernel, as indicated by
        `self.active_dims`.
        :param X: Input 1 (NxD).
        :param X2: Input 2 (MxD), may be None.
        :return: Sliced X, X2, (Nxself.input_dim).
        """
        if isinstance(self.active_dims, slice):
            X = X[:, self.active_dims]
            if X2 is not None:
                X2 = X2[:, self.active_dims]
        else:
            X = tf.transpose(tf.gather(tf.transpose(X), self.active_dims))
            if X2 is not None:
                X2 = tf.transpose(tf.gather(tf.transpose(X2), self.active_dims))
        input_dim_shape = tf.shape(X)[1]
        input_dim = tf.convert_to_tensor(self.input_dim, dtype=settings.tf_int)
        with tf.control_dependencies([tf.assert_equal(input_dim_shape, input_dim)]):
            X = tf.identity(X)

        return X, X2

    def _slice_cov(self, cov):
        """
        Slice the correct dimensions for use in the kernel, as indicated by
        `self.active_dims` for covariance matrices. This requires slicing the
        rows *and* columns. This will also turn flattened diagonal
        matrices into a tensor of full diagonal matrices.
        :param cov: Tensor of covariance matrices (NxDxD or NxD).
        :return: N x self.input_dim x self.input_dim.
        """
        cov = tf.cond(tf.equal(tf.rank(cov), 2), lambda: tf.matrix_diag(cov), lambda: cov)

        if isinstance(self.active_dims, slice):
            cov = cov[..., self.active_dims, self.active_dims]
        else:
            cov_shape = tf.shape(cov)
            covr = tf.reshape(cov, [-1, cov_shape[-1], cov_shape[-1]])
            gather1 = tf.gather(tf.transpose(covr, [2, 1, 0]), self.active_dims)
            gather2 = tf.gather(tf.transpose(gather1, [1, 0, 2]), self.active_dims)
            cov = tf.reshape(tf.transpose(gather2, [2, 0, 1]),
                             tf.concat([cov_shape[:-2], [len(self.active_dims), len(self.active_dims)]], 0))
        return cov

    def __add__(self, other):
        return Sum([self, other])

    def __mul__(self, other):
        return Product([self, other])


class Static(Kernel):
    """
    Kernels who don't depend on the value of the inputs are 'Static'.  The only
    parameter is a variance.
    """

    def __init__(self, input_dim, variance=1.0, active_dims=None, name=None):
        super().__init__(input_dim, active_dims, name=name)
        self.variance = Parameter(variance, transform=transforms.positive)

    @params_as_tensors
    def Kdiag(self, X):
        return tf.fill(tf.stack([tf.shape(X)[0]]), tf.squeeze(self.variance))


class White(Static):
    """
    The White kernel
    """

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if X2 is None:
            d = tf.fill(tf.stack([tf.shape(X)[0]]), tf.squeeze(self.variance))
            return tf.matrix_diag(d)
        else:
            shape = tf.stack([tf.shape(X)[0], tf.shape(X2)[0]])
            return tf.zeros(shape, settings.float_type)


class Constant(Static):
    """
    The Constant (aka Bias) kernel
    """

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if X2 is None:
            shape = tf.stack([tf.shape(X)[0], tf.shape(X)[0]])
        else:
            shape = tf.stack([tf.shape(X)[0], tf.shape(X2)[0]])
        return tf.fill(shape, tf.squeeze(self.variance))


class Bias(Constant):
    """
    Another name for the Constant kernel, included for convenience.
    """
    pass


class Stationary(Kernel):
    """
    Base class for kernels that are stationary, that is, they only depend on

        r = || x - x' ||

    This class handles 'ARD' behaviour, which stands for 'Automatic Relevance
    Determination'. This means that the kernel has one lengthscale per
    dimension, otherwise the kernel is isotropic (has a single lengthscale).
    """

    def __init__(self, input_dim, variance=1.0, lengthscales=None,
                 active_dims=None, ARD=False, name=None):
        """
        - input_dim is the dimension of the input to the kernel
        - variance is the (initial) value for the variance parameter
        - lengthscales is the initial value for the lengthscales parameter
          defaults to 1.0 (ARD=False) or np.ones(input_dim) (ARD=True).
        - active_dims is a list of length input_dim which controls which
          columns of X are used.
        - ARD specifies whether the kernel has one lengthscale per dimension
          (ARD=True) or a single lengthscale (ARD=False).
        """
        super().__init__(input_dim, active_dims, name=name)
        self.variance = Parameter(variance, transform=transforms.positive)
        if ARD:
            if lengthscales is None:
                lengthscales = np.ones(input_dim, dtype=settings.float_type)
            else:
                # accepts float or array:
                lengthscales = lengthscales * np.ones(input_dim, dtype=settings.float_type)
            self.lengthscales = Parameter(lengthscales, transform=transforms.positive)
            self.ARD = True
        else:
            if lengthscales is None:
                lengthscales = 1.0
            self.lengthscales = Parameter(lengthscales, transform=transforms.positive)
            self.ARD = False


    def square_dist(self, X, X2):  # pragma: no cover
        warnings.warn('square_dist is deprecated and will be removed at '
                      'GPflow version 1.2.0. Use scaled_square_dist.',
                      DeprecationWarning)
        return self.scaled_square_dist(X, X2)


    def euclid_dist(self, X, X2):  # pragma: no cover
        warnings.warn('euclid_dist is deprecated and will be removed at '
                      'GPflow version 1.2.0. Use scaled_euclid_dist.',
                      DeprecationWarning)
        return self.scaled_euclid_dist(X, X2)


    @params_as_tensors
    def scaled_square_dist(self, X, X2):
        """
        Returns ((X - X2ᵀ)/lengthscales)².
        Due to the implementation and floating-point imprecision, the
        result may actually be very slightly negative for entries very
        close to each other.
        """
        X = X / self.lengthscales
        Xs = tf.reduce_sum(tf.square(X), axis=1)

        if X2 is None:
            dist = -2 * tf.matmul(X, X, transpose_b=True)
            dist += tf.reshape(Xs, (-1, 1))  + tf.reshape(Xs, (1, -1))
            return dist

        X2 = X2 / self.lengthscales
        X2s = tf.reduce_sum(tf.square(X2), axis=1)
        dist = -2 * tf.matmul(X, X2, transpose_b=True)
        dist += tf.reshape(Xs, (-1, 1)) + tf.reshape(X2s, (1, -1))
        return dist


    def scaled_euclid_dist(self, X, X2):
        """
        Returns |(X - X2ᵀ)/lengthscales| (L2-norm).
        """
        r2 = self.scaled_square_dist(X, X2)
        return tf.sqrt(r2 + 1e-12)

    @params_as_tensors
    def Kdiag(self, X, presliced=False):
        return tf.fill(tf.stack([tf.shape(X)[0]]), tf.squeeze(self.variance))


class RBF(Stationary):
    """
    The radial basis function (RBF) or squared exponential kernel
    """

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        return self.variance * tf.exp(-self.scaled_square_dist(X, X2) / 2)

SquaredExponential = RBF


class RationalQuadratic(Stationary):
    """
    Rational Quadratic kernel,

    k(r) = σ² (1 + r² / 2αℓ²)^(-α)

    σ² : variance
    ℓ  : lengthscales
    α  : alpha, determines relative weighting of small-scale and large-scale fluctuations

    For α → ∞, the RQ kernel becomes equivalent to the squared exponential.
    """

    def __init__(self, input_dim, variance=1.0, lengthscales=None, alpha=1.0,
                 active_dims=None, ARD=False, name=None):
        super().__init__(input_dim, variance, lengthscales, active_dims, ARD, name)
        self.alpha = Parameter(alpha, transform=transforms.positive)

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        return self.variance * (1 + self.scaled_square_dist(X, X2) /
                                (2 * self.alpha)) ** (- self.alpha)


class Linear(Kernel):
    """
    The linear kernel
    """

    def __init__(self, input_dim, variance=1.0, active_dims=None, ARD=False, name=None):
        """
        - input_dim is the dimension of the input to the kernel
        - variance is the (initial) value for the variance parameter(s)
          if ARD=True, there is one variance per input
        - active_dims is a list of length input_dim which controls
          which columns of X are used.
        """
        super().__init__(input_dim, active_dims, name=name)
        self.ARD = ARD
        if ARD:
            # accept float or array:
            variance = np.ones(self.input_dim, dtype=settings.float_type) * variance
            self.variance = Parameter(variance, transform=transforms.positive)
        else:
            self.variance = Parameter(variance, transform=transforms.positive)

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        if X2 is None:
            return tf.matmul(X * self.variance, X, transpose_b=True)
        else:
            return tf.matmul(X * self.variance, X2, transpose_b=True)

    @params_as_tensors
    def Kdiag(self, X, presliced=False):
        if not presliced:
            X, _ = self._slice(X, None)
        return tf.reduce_sum(tf.square(X) * self.variance, 1)


class Polynomial(Linear):
    """
    The Polynomial kernel. Samples are polynomials of degree `d`.
    """

    def __init__(self, input_dim,
                 degree=3.0,
                 variance=1.0,
                 offset=1.0,
                 active_dims=None,
                 ARD=False,
                 name=None):
        """
        :param input_dim: the dimension of the input to the kernel
        :param variance: the (initial) value for the variance parameter(s)
                         if ARD=True, there is one variance per input
        :param degree: the degree of the polynomial
        :param active_dims: a list of length input_dim which controls
          which columns of X are used.
        :param ARD: use variance as described
        """
        super().__init__(input_dim, variance, active_dims, ARD, name=name)
        self.degree = degree
        self.offset = Parameter(offset, transform=transforms.positive)

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        return (Linear.K(self, X, X2, presliced=presliced) + self.offset) ** self.degree

    @params_as_tensors
    def Kdiag(self, X, presliced=False):
        return (Linear.Kdiag(self, X, presliced=presliced) + self.offset) ** self.degree


class Exponential(Stationary):
    """
    The Exponential kernel
    """

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        r = self.scaled_euclid_dist(X, X2)
        return self.variance * tf.exp(-0.5 * r)


class Matern12(Stationary):
    """
    The Matern 1/2 kernel
    """

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        r = self.scaled_euclid_dist(X, X2)
        return self.variance * tf.exp(-r)


class Matern32(Stationary):
    """
    The Matern 3/2 kernel
    """

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        r = self.scaled_euclid_dist(X, X2)
        return self.variance * (1. + np.sqrt(3.) * r) * \
               tf.exp(-np.sqrt(3.) * r)


class Matern52(Stationary):
    """
    The Matern 5/2 kernel
    """

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        r = self.scaled_euclid_dist(X, X2)
        return self.variance * (1.0 + np.sqrt(5.) * r + 5. / 3. * tf.square(r)) \
               * tf.exp(-np.sqrt(5.) * r)


class Cosine(Stationary):
    """
    The Cosine kernel
    """

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        r = self.scaled_euclid_dist(X, X2)
        return self.variance * tf.cos(r)


class ArcCosine(Kernel):
    """
    The Arc-cosine family of kernels which mimics the computation in neural
    networks. The order parameter specifies the assumed activation function.
    The Multi Layer Perceptron (MLP) kernel is closely related to the ArcCosine
    kernel of order 0. The key reference is

    ::

        @incollection{NIPS2009_3628,
            title = {Kernel Methods for Deep Learning},
            author = {Youngmin Cho and Lawrence K. Saul},
            booktitle = {Advances in Neural Information Processing Systems 22},
            year = {2009},
            url = {http://papers.nips.cc/paper/3628-kernel-methods-for-deep-learning.pdf}
        }
    """

    implemented_orders = {0, 1, 2}
    def __init__(self, input_dim,
                 order=0,
                 variance=1.0, weight_variances=1., bias_variance=1.,
                 active_dims=None, ARD=False, name=None):
        """
        - input_dim is the dimension of the input to the kernel
        - order specifies the activation function of the neural network
          the function is a rectified monomial of the chosen order.
        - variance is the initial value for the variance parameter
        - weight_variances is the initial value for the weight_variances parameter
          defaults to 1.0 (ARD=False) or np.ones(input_dim) (ARD=True).
        - bias_variance is the initial value for the bias_variance parameter
          defaults to 1.0.
        - active_dims is a list of length input_dim which controls which
          columns of X are used.
        - ARD specifies whether the kernel has one weight_variance per dimension
          (ARD=True) or a single weight_variance (ARD=False).
        """
        super().__init__(input_dim, active_dims, name=name)

        if order not in self.implemented_orders:
            raise ValueError('Requested kernel order is not implemented.')
        self.order = order

        self.variance = Parameter(variance, transform=transforms.positive)
        self.bias_variance = Parameter(bias_variance, transform=transforms.positive)
        if ARD:
            if weight_variances is None:
                weight_variances = np.ones(input_dim, settings.float_type)
            else:
                # accepts float or array:
                weight_variances = weight_variances * np.ones(input_dim, settings.float_type)
            self.weight_variances = Parameter(weight_variances, transform=transforms.positive)
            self.ARD = True
        else:
            if weight_variances is None:
                weight_variances = 1.0
            self.weight_variances = Parameter(weight_variances, transform=transforms.positive)
            self.ARD = False

    @params_as_tensors
    def _weighted_product(self, X, X2=None):
        if X2 is None:
            return tf.reduce_sum(self.weight_variances * tf.square(X), axis=1) + self.bias_variance
        return tf.matmul((self.weight_variances * X), X2, transpose_b=True) + self.bias_variance

    def _J(self, theta):
        """
        Implements the order dependent family of functions defined in equations
        4 to 7 in the reference paper.
        """
        if self.order == 0:
            return np.pi - theta
        elif self.order == 1:
            return tf.sin(theta) + (np.pi - theta) * tf.cos(theta)
        elif self.order == 2:
            return 3. * tf.sin(theta) * tf.cos(theta) + \
                   (np.pi - theta) * (1. + 2. * tf.cos(theta) ** 2)

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)

        X_denominator = tf.sqrt(self._weighted_product(X))
        if X2 is None:
            X2 = X
            X2_denominator = X_denominator
        else:
            X2_denominator = tf.sqrt(self._weighted_product(X2))

        numerator = self._weighted_product(X, X2)
        cos_theta = numerator / X_denominator[:, None] / X2_denominator[None, :]
        jitter = 1e-15
        theta = tf.acos(jitter + (1 - 2 * jitter) * cos_theta)

        return self.variance * (1. / np.pi) * self._J(theta) * \
               X_denominator[:, None] ** self.order * \
               X2_denominator[None, :] ** self.order

    @params_as_tensors
    def Kdiag(self, X, presliced=False):
        if not presliced:
            X, _ = self._slice(X, None)

        X_product = self._weighted_product(X)
        theta = tf.constant(0., settings.float_type)
        return self.variance * (1. / np.pi) * self._J(theta) * X_product ** self.order


class Periodic(Kernel):
    """
    The periodic kernel. Defined in  Equation (47) of

    D.J.C.MacKay. Introduction to Gaussian processes. In C.M.Bishop, editor,
    Neural Networks and Machine Learning, pages 133--165. Springer, 1998.

    Derived using the mapping u=(cos(x), sin(x)) on the inputs.
    """

    def __init__(self, input_dim, period=1.0, variance=1.0,
                 lengthscales=1.0, active_dims=None, name=None):
        # No ARD support for lengthscale or period yet
        super().__init__(input_dim, active_dims, name=name)
        self.variance = Parameter(variance, transform=transforms.positive)
        self.lengthscales = Parameter(lengthscales, transform=transforms.positive)
        self.ARD = False
        self.period = Parameter(period, transform=transforms.positive)

    @params_as_tensors
    def Kdiag(self, X, presliced=False):
        return tf.fill(tf.stack([tf.shape(X)[0]]), tf.squeeze(self.variance))

    @params_as_tensors
    def K(self, X, X2=None, presliced=False):
        if not presliced:
            X, X2 = self._slice(X, X2)
        if X2 is None:
            X2 = X

        # Introduce dummy dimension so we can use broadcasting
        f = tf.expand_dims(X, 1)  # now N x 1 x D
        f2 = tf.expand_dims(X2, 0)  # now 1 x M x D

        r = np.pi * (f - f2) / self.period
        r = tf.reduce_sum(tf.square(tf.sin(r) / self.lengthscales), 2)

        return self.variance * tf.exp(-0.5 * r)


class Coregion(Kernel):
    def __init__(self, input_dim, output_dim, rank, active_dims=None, name=None):
        """
        A Coregionalization kernel. The inputs to this kernel are _integers_
        (we cast them from floats as needed) which usually specify the
        *outputs* of a Coregionalization model.

        The parameters of this kernel, W, kappa, specify a positive-definite
        matrix B.

          B = W W^T + diag(kappa) .

        The kernel function is then an indexing of this matrix, so

          K(x, y) = B[x, y] .

        We refer to the size of B as "num_outputs x num_outputs", since this is
        the number of outputs in a coregionalization model. We refer to the
        number of columns on W as 'rank': it is the number of degrees of
        correlation between the outputs.

        NB. There is a symmetry between the elements of W, which creates a
        local minimum at W=0. To avoid this, it's recommended to initialize the
        optimization (or MCMC chain) using a random W.
        """
        assert input_dim == 1, "Coregion kernel in 1D only"
        super().__init__(input_dim, active_dims, name=name)

        self.output_dim = output_dim
        self.rank = rank
        self.W = Parameter(np.zeros((self.output_dim, self.rank), dtype=settings.float_type))
        self.kappa = Parameter(np.ones(self.output_dim, dtype=settings.float_type), transform=transforms.positive)

    @params_as_tensors
    def K(self, X, X2=None):
        X, X2 = self._slice(X, X2)
        X = tf.cast(X[:, 0], tf.int32)
        if X2 is None:
            X2 = X
        else:
            X2 = tf.cast(X2[:, 0], tf.int32)
        B = tf.matmul(self.W, self.W, transpose_b=True) + tf.matrix_diag(self.kappa)
        return tf.gather(tf.transpose(tf.gather(B, X2)), X)

    @params_as_tensors
    def Kdiag(self, X):
        X, _ = self._slice(X, None)
        X = tf.cast(X[:, 0], tf.int32)
        Bdiag = tf.reduce_sum(tf.square(self.W), 1) + self.kappa
        return tf.gather(Bdiag, X)


def make_kernel_names(kern_list):
    """
    Take a list of kernels and return a list of strings, giving each kernel a
    unique name.

    Each name is made from the lower-case version of the kernel's class name.

    Duplicate kernels are given trailing numbers.
    """
    names = []
    counting_dict = {}
    for k in kern_list:
        inner_name = k.__class__.__name__.lower()

        # check for duplicates: start numbering if needed
        if inner_name in counting_dict:
            if counting_dict[inner_name] == 1:
                names[names.index(inner_name)] = inner_name + '_1'
            counting_dict[inner_name] += 1
            name = inner_name + '_' + str(counting_dict[inner_name])
        else:
            counting_dict[inner_name] = 1
            name = inner_name
        names.append(name)
    return names


class Combination(Kernel):
    """
    Combine a list of kernels, e.g. by adding or multiplying (see inheriting
    classes).

    The names of the kernels to be combined are generated from their class
    names.
    """

    def __init__(self, kern_list, name=None):
        if not all(isinstance(k, Kernel) for k in kern_list):
            raise TypeError("can only combine Kernel instances")  # pragma: no cover

        input_dim = np.max([k.input_dim
                            if type(k.active_dims) is slice else
                            np.max(k.active_dims) + 1
                            for k in kern_list])
        super().__init__(input_dim=input_dim, name=name)

        # add kernels to a list, flattening out instances of this class therein
        self.kern_list = []
        for k in kern_list:
            if isinstance(k, self.__class__):
                self.kern_list.extend(k.kern_list)
            else:
                self.kern_list.append(k)

        # generate a set of suitable names and add the kernels as attributes
        names = make_kernel_names(self.kern_list)
        [setattr(self, name, k) for name, k in zip(names, self.kern_list)]

    @property
    def on_separate_dimensions(self):
        """
        Checks whether the kernels in the combination act on disjoint subsets
        of dimensions. Currently, it is hard to asses whether two slice objects
        will overlap, so this will always return False.
        :return: Boolean indicator.
        """
        if np.any([isinstance(k.active_dims, slice) for k in self.kern_list]):
            # Be conservative in the case of a slice object
            return False
        else:
            dimlist = [k.active_dims for k in self.kern_list]
            overlapping = False
            for i, dims_i in enumerate(dimlist):
                for dims_j in dimlist[i + 1:]:
                    if np.any(dims_i.reshape(-1, 1) == dims_j.reshape(1, -1)):
                        overlapping = True
            return not overlapping


class Sum(Combination):
    def K(self, X, X2=None, presliced=False):
        return reduce(tf.add, [k.K(X, X2) for k in self.kern_list])

    def Kdiag(self, X, presliced=False):
        return reduce(tf.add, [k.Kdiag(X) for k in self.kern_list])


class Product(Combination):
    def K(self, X, X2=None, presliced=False):
        return reduce(tf.multiply, [k.K(X, X2) for k in self.kern_list])

    def Kdiag(self, X, presliced=False):
        return reduce(tf.multiply, [k.Kdiag(X) for k in self.kern_list])


def make_deprecated_class(oldname, NewClass):
    """
    Returns a class that raises NotImplementedError on instantiation.
    e.g.:
    >>> Kern = make_deprecated_class("Kern", Kernel)
    """
    msg = ("{module}.{} has been renamed to {module}.{}"
           .format(oldname, NewClass.__name__, module=NewClass.__module__))

    class OldClass(NewClass):
        def __new__(cls, *args, **kwargs):
            raise NotImplementedError(msg)
    OldClass.__doc__ = msg
    OldClass.__qualname__ = OldClass.__name__ = oldname
    return OldClass

Kern = make_deprecated_class("Kern", Kernel)
Add = make_deprecated_class("Add", Sum)
Prod = make_deprecated_class("Prod", Product)
PeriodicKernel = make_deprecated_class("PeriodicKernel", Periodic)