# Copyright 2018 the GPflow authors.
#
# 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.

import copy

import numpy as np
import pytest
import tensorflow as tf
from numpy.testing import assert_allclose

import gpflow
from gpflow.config import default_float, default_int
from gpflow.kernels import (RBF, ArcCosine, Constant, Linear, Periodic,
                            Polynomial, Stationary)

rng = np.random.RandomState(1)


def _ref_rbf(X, lengthscale, signal_variance):
    num_data, _ = X.shape
    kernel = np.zeros((num_data, num_data))
    for row_index in range(num_data):
        for column_index in range(num_data):
            vecA = X[row_index, :]
            vecB = X[column_index, :]
            delta = vecA - vecB
            distance_squared = np.dot(delta.T, delta)
            kernel[row_index, column_index] = signal_variance * \
                np.exp(-0.5 * distance_squared / lengthscale ** 2)
    return kernel


def _ref_arccosine(X, order, weight_variances, bias_variance, signal_variance):
    num_points = X.shape[0]
    kernel = np.empty((num_points, num_points))
    for row in range(num_points):
        for col in range(num_points):
            x = X[row]
            y = X[col]

            numerator = (weight_variances * x).dot(y) + bias_variance

            x_denominator = np.sqrt((weight_variances * x).dot(x) +
                                    bias_variance)
            y_denominator = np.sqrt((weight_variances * y).dot(y) +
                                    bias_variance)
            denominator = x_denominator * y_denominator

            theta = np.arccos(np.clip(numerator / denominator, -1., 1.))
            if order == 0:
                J = np.pi - theta
            elif order == 1:
                J = np.sin(theta) + (np.pi - theta) * np.cos(theta)
            elif order == 2:
                J = 3. * np.sin(theta) * np.cos(theta)
                J += (np.pi - theta) * (1. + 2. * np.cos(theta)**2)

            kernel[row, col] = signal_variance * (1. / np.pi) * J * \
                x_denominator ** order * \
                y_denominator ** order
    return kernel


def _ref_periodic(X, lengthScale, signal_variance, period):
    # Based on the GPy implementation of standard_period kernel
    base = np.pi * (X[:, None, :] - X[None, :, :]) / period
    exp_dist = np.exp(-0.5 *
                      np.sum(np.square(np.sin(base) / lengthScale), axis=-1))
    return signal_variance * exp_dist


@pytest.mark.parametrize('variance, lengthscale', [[2.3, 1.4]])
def test_rbf_1d(variance, lengthscale):
    X = rng.randn(3, 1)
    kernel = gpflow.kernels.RBF(lengthscale=lengthscale, variance=variance)

    gram_matrix = kernel(X)
    reference_gram_matrix = _ref_rbf(X, lengthscale, variance)

    assert_allclose(gram_matrix, reference_gram_matrix)


@pytest.mark.parametrize('variance, lengthscale', [[2.3, 1.4]])
def test_rq_1d(variance, lengthscale):
    kSE = gpflow.kernels.RBF(lengthscale=lengthscale, variance=variance)
    kRQ = gpflow.kernels.RationalQuadratic(lengthscale=lengthscale,
                                           variance=variance,
                                           alpha=1e8)
    rng = np.random.RandomState(1)
    X = rng.randn(6, 1).astype(default_float())

    gram_matrix_SE = kSE(X)
    gram_matrix_RQ = kRQ(X)
    assert_allclose(gram_matrix_SE, gram_matrix_RQ)


def _assert_arccosine_kern_err(variance, weight_variances, bias_variance,
                               order, ard, X):
    kernel = gpflow.kernels.ArcCosine(order=order,
                                      variance=variance,
                                      weight_variances=weight_variances,
                                      bias_variance=bias_variance,
                                      ard=ard)

    if weight_variances is None:
        weight_variances = 1.

    gram_matrix = kernel(X)
    reference_gram_matrix = _ref_arccosine(X, order, weight_variances,
                                           bias_variance, variance)
    assert_allclose(gram_matrix, reference_gram_matrix)


@pytest.mark.parametrize('order', gpflow.kernels.ArcCosine.implemented_orders)
@pytest.mark.parametrize('D', [1, 3])
@pytest.mark.parametrize('N, weight_variances, bias_variance, variance',
                         [[3, 1.7, 0.6, 2.3]])
def test_arccosine_1d_and_3d(order, D, N, weight_variances, bias_variance,
                             variance):
    ard = False if D == 1 else True
    X_data = rng.randn(N, D)
    _assert_arccosine_kern_err(variance, weight_variances, bias_variance,
                               order, ard, X_data)


@pytest.mark.parametrize('order', [42])
def test_arccosine_non_implemented_order(order):
    with pytest.raises(ValueError):
        gpflow.kernels.ArcCosine(order=order)


@pytest.mark.parametrize('ard', [True, False])
@pytest.mark.parametrize(
    'order, D, N, weight_variances, bias_variance, variance',
    [[0, 1, 3, 1., 1., 1.]])
def test_arccosine_weight_initializations(ard, order, D, N, weight_variances,
                                          bias_variance, variance):
    X_data = rng.randn(N, D)
    _assert_arccosine_kern_err(variance, weight_variances, bias_variance,
                               order, ard, X_data)


@pytest.mark.parametrize('D, N', [[1, 4]])
def test_arccosine_nan_gradient(D, N):
    X = rng.rand(N, D)
    kernel = gpflow.kernels.ArcCosine()
    with tf.GradientTape() as tape:
        Kff = kernel(X)
    grads = tape.gradient(Kff, kernel.trainable_variables)
    assert not np.any(np.isnan(grads))


def _assert_periodic_kern_err(lengthscale, variance, period, X):
    kernel = gpflow.kernels.Periodic(period=period,
                                     variance=variance,
                                     lengthscale=lengthscale)
    gram_matrix = kernel(X)
    reference_gram_matrix = _ref_periodic(X, lengthscale, variance, period)

    assert_allclose(gram_matrix, reference_gram_matrix)


@pytest.mark.parametrize('D', [1, 2])
@pytest.mark.parametrize('N, lengthscale, variance, period',
                         [[3, 2., 2.3, 2.], [5, 11.5, 1.3, 20.]])
def test_periodic_1d_and_2d(D, N, lengthscale, variance, period):
    X = rng.randn(N, D) if D == 1 else rng.multivariate_normal(
        np.zeros(D), np.eye(D), N)
    _assert_periodic_kern_err(lengthscale, variance, period, X)


kernel_setups = [
    kernel() for kernel in gpflow.kernels.Stationary.__subclasses__()
] + [
    gpflow.kernels.Constant(),
    gpflow.kernels.Linear(),
    gpflow.kernels.Polynomial(),
    gpflow.kernels.ArcCosine()
]


@pytest.mark.parametrize('D', [1, 5])
@pytest.mark.parametrize('kernel', kernel_setups)
@pytest.mark.parametrize('N', [10])
def test_kernel_symmetry_1d_and_5d(D, kernel, N):
    X = rng.randn(N, D)
    errors = kernel(X) - kernel(X, X)
    assert np.allclose(errors, 0)


@pytest.mark.parametrize('N, N2, input_dim, output_dim, rank',
                         [[10, 12, 1, 3, 2]])
def test_coregion_shape(N, N2, input_dim, output_dim, rank):
    X = np.random.randint(0, output_dim, (N, input_dim))
    X2 = np.random.randint(0, output_dim, (N2, input_dim))
    kernel = gpflow.kernels.Coregion(output_dim=output_dim, rank=rank)
    kernel.W = rng.randn(output_dim, rank)
    kernel.kappa = rng.randn(output_dim, 1).reshape(-1) + 1.

    Kff2 = kernel(X, X2)
    assert Kff2.shape == (10, 12)
    Kff = kernel(X)
    assert Kff.shape == (10, 10)


@pytest.mark.parametrize('N, input_dim, output_dim, rank', [[10, 1, 3, 2]])
def test_coregion_diag(N, input_dim, output_dim, rank):
    X = np.random.randint(0, output_dim, (N, input_dim))
    kernel = gpflow.kernels.Coregion(output_dim=output_dim, rank=rank)
    kernel.W = rng.randn(output_dim, rank)
    kernel.kappa = rng.randn(output_dim, 1).reshape(-1) + 1.

    K = kernel(X)
    Kdiag = kernel.K_diag(X)
    assert np.allclose(np.diag(K), Kdiag)


@pytest.mark.parametrize('N, input_dim, output_dim, rank', [[10, 1, 3, 2]])
def test_coregion_slice(N, input_dim, output_dim, rank):
    X = np.random.randint(0, output_dim, (N, input_dim))
    X = np.hstack((X, rng.randn(10, 1)))
    kernel1 = gpflow.kernels.Coregion(output_dim=output_dim,
                                      rank=rank,
                                      active_dims=[0])
    # compute another kernel with additinoal inputs,
    # make sure out kernel is still okay.
    kernel2 = gpflow.kernels.RBF(active_dims=[1])
    kernel_prod = kernel1 * kernel2
    K1 = kernel_prod(X)
    K2 = kernel1(X) * kernel2(X)  # slicing happens inside kernel
    assert np.allclose(K1, K2)


_dim = 3
kernel_setups_extended = kernel_setups + [
    RBF() + Linear(),
    RBF() * Linear(),
    RBF() + Linear(ard=True, variance=rng.rand(_dim, 1).reshape(-1))
] + [ArcCosine(order=order) for order in ArcCosine.implemented_orders]


@pytest.mark.parametrize('kernel', kernel_setups_extended)
@pytest.mark.parametrize('N, dim', [[30, _dim]])
def test_diags(kernel, N, dim):
    X = np.random.randn(N, dim)
    kernel1 = kernel(X)
    kernel2 = tf.linalg.diag_part(kernel(X))
    assert np.allclose(np.diagonal(kernel1), kernel2)


# Add a rbf and linear kernel, make sure the result is the same as adding the result of
# the kernels separately.
_kernel_setups_add = [
    gpflow.kernels.RBF(),
    gpflow.kernels.Linear(), (gpflow.kernels.RBF() + gpflow.kernels.Linear())
]


@pytest.mark.parametrize('N, D', [[10, 1]])
def test_add_symmetric(N, D):
    X = rng.randn(N, D)
    Kffs = [kernel(X) for kernel in _kernel_setups_add]

    assert np.allclose(Kffs[0] + Kffs[1], Kffs[2])


@pytest.mark.parametrize('N, M, D', [[10, 12, 1]])
def test_add_asymmetric(N, M, D):
    X, Z = rng.randn(N, D), rng.randn(M, D)
    Kfus = [kernel(X, Z) for kernel in _kernel_setups_add]

    assert np.allclose(Kfus[0] + Kfus[1], Kfus[2])


@pytest.mark.parametrize('N, D', [[10, 1]])
def test_white(N, D):
    """
    The white kernel should not give the same result when called with k(X) and
    k(X, X)
    """
    X = rng.randn(N, D)
    kernel = gpflow.kernels.White()
    Kff_sym = kernel(X)
    Kff_asym = kernel(X, X)

    assert not np.allclose(Kff_sym, Kff_asym)


_kernel_classes_slice = [kernel for kernel in gpflow.kernels.Stationary.__subclasses__()] + \
    [gpflow.kernels.Constant,
     gpflow.kernels.Linear,
     gpflow.kernels.Polynomial]

_kernel_triples_slice = [
    (k1(active_dims=[0]), k2(active_dims=[1]), k3(active_dims=slice(0, 1)))
    for k1, k2, k3 in zip(_kernel_classes_slice, _kernel_classes_slice,
                          _kernel_classes_slice)
]


@pytest.mark.parametrize('kernel_triple', _kernel_triples_slice)
@pytest.mark.parametrize('N, D', [[20, 2]])
def test_slice_symmetric(kernel_triple, N, D):
    X = rng.randn(N, D)
    K1, K3 = kernel_triple[0](X), kernel_triple[2](X[:, :1])
    assert np.allclose(K1, K3)
    K2, K4 = kernel_triple[1](X), kernel_triple[2](X[:, 1:])
    assert np.allclose(K2, K4)


@pytest.mark.parametrize('kernel_triple', _kernel_triples_slice)
@pytest.mark.parametrize('N, M, D', [[10, 12, 2]])
def test_slice_asymmetric(kernel_triple, N, M, D):
    X = rng.randn(N, D)
    Z = rng.randn(M, D)
    K1, K3 = kernel_triple[0](X, Z), kernel_triple[2](X[:, :1], Z[:, :1])
    assert np.allclose(K1, K3)
    K2, K4 = kernel_triple[1](X, Z), kernel_triple[2](X[:, 1:], Z[:, 1:])
    assert np.allclose(K2, K4)


_kernel_setups_prod = [
    gpflow.kernels.Matern32(),
    gpflow.kernels.Matern52(lengthscale=0.3),
    gpflow.kernels.Matern32() * gpflow.kernels.Matern52(lengthscale=0.3)
]


@pytest.mark.parametrize('N, D', [[30, 2]])
def test_product(N, D):
    X = rng.randn(N, D)
    Kffs = [kernel(X) for kernel in _kernel_setups_prod]

    assert np.allclose(Kffs[0] * Kffs[1], Kffs[2])


@pytest.mark.parametrize('N, D', [[30, 4], [10, 7]])
def test_active_product(N, D):
    X = rng.randn(N, D)
    dims, rand_idx, ls = list(range(D)), int(rng.randint(0, D)), rng.uniform(
        1., 7., D)
    active_dims_list = [
        dims[:rand_idx] + dims[rand_idx + 1:], [rand_idx], dims
    ]
    lengthscale_list = [
        np.hstack([ls[:rand_idx], ls[rand_idx + 1:]]), ls[rand_idx], ls
    ]
    kernels = [
        gpflow.kernels.RBF(lengthscale=lengthscale, active_dims=dims, ard=True)
        for dims, lengthscale in zip(active_dims_list, lengthscale_list)
    ]
    kernel_prod = kernels[0] * kernels[1]

    Kff = kernels[2](X)
    Kff_prod = kernel_prod(X)

    assert np.allclose(Kff, Kff_prod)


@pytest.mark.parametrize('D', [4, 7])
def test_ard_init_scalar(D):
    """
    For ard kernels, make sure that kernels can be instantiated with a single
    lengthscale or a suitable array of lengthscale
    """
    kernel_1 = gpflow.kernels.RBF(lengthscale=2.3)
    kernel_2 = gpflow.kernels.RBF(lengthscale=np.ones(D) * 2.3, ard=True)
    lengthscale_1 = kernel_1.lengthscale.read_value()
    lengthscale_2 = kernel_2.lengthscale.read_value()
    assert np.allclose(lengthscale_1, lengthscale_2, atol=1e-10)


@pytest.mark.parametrize('N', [4, 7])
@pytest.mark.parametrize('ard', [True, False, None])
def test_ard_init_shapes(N, ard):
    with pytest.raises(tf.errors.InvalidArgumentError):
        k1 = gpflow.kernels.RBF(lengthscale=np.ones(2), ard=ard)
        k1(rng.randn(N, 4))
    with pytest.raises(tf.errors.InvalidArgumentError):
        k2 = gpflow.kernels.RBF(lengthscale=np.ones(3), ard=ard)
        k2(rng.randn(N, 2))


@pytest.mark.parametrize('D', [4, 7])
def test_ard_init_MLP(D):
    """
    For ard kernels, make sure that kernels can be instantiated with a single
    lengthscale or a suitable array of lengthscale
    """
    kernel_1 = gpflow.kernels.ArcCosine(weight_variances=1.23, ard=True)
    kernel_2 = gpflow.kernels.ArcCosine(weight_variances=np.ones(3) * 1.23,
                                        ard=True)
    variances_1 = kernel_1.weight_variances.read_value()
    variances_2 = kernel_2.weight_variances.read_value()
    assert np.allclose(variances_1, variances_2, atol=1e-10)