from numpy import (abs, asarray, cos, exp, log, arange, pi, prod, sin, sqrt,
                   sum, tan)
from .go_benchmark import Benchmark, safe_import

with safe_import():
    from scipy.special import factorial


class Matyas(Benchmark):

    r"""
    Matyas objective function.

    This class defines the Matyas [1]_ global optimization problem. This is a
    multimodal minimization problem defined as follows:

    .. math::

        f_{\text{Matyas}}(x) = 0.26(x_1^2 + x_2^2) - 0.48 x_1 x_2


    with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.

    *Global optimum*: :math:`f(x) = 0` for :math:`x = [0, 0]`

    .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
    For Global Optimization Problems Int. Journal of Mathematical Modelling
    and Numerical Optimisation, 2013, 4, 150-194.
    """

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))

        self.global_optimum = [[0 for _ in range(self.N)]]
        self.fglob = 0.0

    def fun(self, x, *args):
        self.nfev += 1

        return 0.26 * (x[0] ** 2 + x[1] ** 2) - 0.48 * x[0] * x[1]


class McCormick(Benchmark):

    r"""
    McCormick objective function.

    This class defines the McCormick [1]_ global optimization problem. This is a
    multimodal minimization problem defined as follows:

    .. math::

       f_{\text{McCormick}}(x) = - x_{1} + 2 x_{2} + \left(x_{1}
       - x_{2}\right)^{2} + \sin\left(x_{1} + x_{2}\right) + 1

    with :math:`x_1 \in [-1.5, 4]`, :math:`x_2 \in [-3, 4]`.

    *Global optimum*: :math:`f(x) = -1.913222954981037` for
    :math:`x = [-0.5471975602214493, -1.547197559268372]`

    .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
    For Global Optimization Problems Int. Journal of Mathematical Modelling
    and Numerical Optimisation, 2013, 4, 150-194.
    """

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = [(-1.5, 4.0), (-3.0, 3.0)]

        self.global_optimum = [[-0.5471975602214493, -1.547197559268372]]
        self.fglob = -1.913222954981037

    def fun(self, x, *args):
        self.nfev += 1

        return (sin(x[0] + x[1]) + (x[0] - x[1]) ** 2 - 1.5 * x[0]
                + 2.5 * x[1] + 1)


class Meyer(Benchmark):

    r"""
    Meyer [1]_ objective function.

    ..[1] https://www.itl.nist.gov/div898/strd/nls/data/mgh10.shtml

    TODO NIST regression standard
    """

    def __init__(self, dimensions=3):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([0., 100., 100.],
                           [1, 1000., 500.]))
        self.global_optimum = [[5.6096364710e-3, 6.1813463463e3,
                                3.4522363462e2]]
        self.fglob = 8.7945855171e1
        self.a = asarray([3.478E+04, 2.861E+04, 2.365E+04, 1.963E+04, 1.637E+04,
                          1.372E+04, 1.154E+04, 9.744E+03, 8.261E+03, 7.030E+03,
                          6.005E+03, 5.147E+03, 4.427E+03, 3.820E+03, 3.307E+03,
                          2.872E+03])
        self.b = asarray([5.000E+01, 5.500E+01, 6.000E+01, 6.500E+01, 7.000E+01,
                          7.500E+01, 8.000E+01, 8.500E+01, 9.000E+01, 9.500E+01,
                          1.000E+02, 1.050E+02, 1.100E+02, 1.150E+02, 1.200E+02,
                          1.250E+02])

    def fun(self, x, *args):
        self.nfev += 1

        vec = x[0] * exp(x[1] / (self.b + x[2]))
        return sum((self.a - vec) ** 2)


class Michalewicz(Benchmark):

    r"""
    Michalewicz objective function.

    This class defines the Michalewicz [1]_ global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Michalewicz}}(x) = - \sum_{i=1}^{2} \sin\left(x_i\right)
       \sin^{2 m}\left(\frac{i x_i^{2}}{\pi}\right)


    Where, in this exercise, :math:`m = 10`.

    with :math:`x_i \in [0, \pi]` for :math:`i = 1, 2`.

    *Global optimum*: :math:`f(x_i) = -1.8013` for :math:`x = [0, 0]`

    .. [1] Adorio, E. MVF - "Multivariate Test Functions Library in C for
    Unconstrained Global Optimization", 2005

    TODO: could change dimensionality, but global minimum might change.
    """

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([0.0] * self.N, [pi] * self.N))

        self.global_optimum = [[2.20290555, 1.570796]]
        self.fglob = -1.8013

    def fun(self, x, *args):
        self.nfev += 1

        m = 10.0
        i = arange(1, self.N + 1)
        return -sum(sin(x) * sin(i * x ** 2 / pi) ** (2 * m))


class MieleCantrell(Benchmark):

    r"""
    Miele-Cantrell [1]_ objective function.

    This class defines the Miele-Cantrell global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

       f_{\text{MieleCantrell}}({x}) = (e^{-x_1} - x_2)^4 + 100(x_2 - x_3)^6
       + \tan^4(x_3 - x_4) + x_1^8


    with :math:`x_i \in [-1, 1]` for :math:`i = 1, ..., 4`.

    *Global optimum*: :math:`f(x) = 0` for :math:`x = [0, 1, 1, 1]`

    .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
    For Global Optimization Problems Int. Journal of Mathematical Modelling
    and Numerical Optimisation, 2013, 4, 150-194.
    """

    def __init__(self, dimensions=4):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-1.0] * self.N, [1.0] * self.N))

        self.global_optimum = [[0.0, 1.0, 1.0, 1.0]]
        self.fglob = 0.0

    def fun(self, x, *args):
        self.nfev += 1

        return ((exp(-x[0]) - x[1]) ** 4 + 100 * (x[1] - x[2]) ** 6
                + tan(x[2] - x[3]) ** 4 + x[0] ** 8)


class Mishra01(Benchmark):

    r"""
    Mishra 1 objective function.

    This class defines the Mishra 1 [1]_ global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Mishra01}}(x) = (1 + x_n)^{x_n}


    where

    .. math::

        x_n = n - \sum_{i=1}^{n-1} x_i


    with :math:`x_i \in [0, 1]` for :math:`i =1, ..., n`.

    *Global optimum*: :math:`f(x) = 2` for :math:`x_i = 1` for
    :math:`i = 1, ..., n`

    .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
    For Global Optimization Problems Int. Journal of Mathematical Modelling
    and Numerical Optimisation, 2013, 4, 150-194.
    """
    change_dimensionality = True

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([0.0] * self.N,
                           [1.0 + 1e-9] * self.N))

        self.global_optimum = [[1.0 for _ in range(self.N)]]
        self.fglob = 2.0

    def fun(self, x, *args):
        self.nfev += 1

        xn = self.N - sum(x[0:-1])
        return (1 + xn) ** xn


class Mishra02(Benchmark):

    r"""
    Mishra 2 objective function.

    This class defines the Mishra 2 [1]_ global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

        f_{\text{Mishra02}}({x}) = (1 + x_n)^{x_n}


     with

     .. math::

         x_n = n - \sum_{i=1}^{n-1} \frac{(x_i + x_{i+1})}{2}


    Here, :math:`n` represents the number of dimensions and
    :math:`x_i \in [0, 1]` for :math:`i = 1, ..., n`.

    *Global optimum*: :math:`f(x) = 2` for :math:`x_i = 1`
    for :math:`i = 1, ..., n`

    .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
    For Global Optimization Problems Int. Journal of Mathematical Modelling
    and Numerical Optimisation, 2013, 4, 150-194.
    """
    change_dimensionality = True

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([0.0] * self.N,
                           [1.0 + 1e-9] * self.N))

        self.global_optimum = [[1.0 for _ in range(self.N)]]
        self.fglob = 2.0

    def fun(self, x, *args):
        self.nfev += 1

        xn = self.N - sum((x[:-1] + x[1:]) / 2.0)
        return (1 + xn) ** xn


class Mishra03(Benchmark):

    r"""
    Mishra 3 objective function.

    This class defines the Mishra 3 [1]_ global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Mishra03}}(x) = \sqrt{\lvert \cos{\sqrt{\lvert x_1^2
       + x_2^2 \rvert}} \rvert} + 0.01(x_1 + x_2)


    with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.

    *Global optimum*: :math:`f(x) = -0.1999` for
    :math:`x = [-9.99378322, -9.99918927]`

    .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
    For Global Optimization Problems Int. Journal of Mathematical Modelling
    and Numerical Optimisation, 2013, 4, 150-194.

    TODO: I think that Jamil#76 has the wrong global minimum, a smaller one
    is possible
    """

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))

        self.global_optimum = [[-9.99378322, -9.99918927]]
        self.fglob = -0.19990562

    def fun(self, x, *args):
        self.nfev += 1

        return (0.01 * (x[0] + x[1])
                + sqrt(abs(cos(sqrt(abs(x[0] ** 2 + x[1] ** 2))))))


class Mishra04(Benchmark):

    r"""
    Mishra 4 objective function.

    This class defines the Mishra 4 [1]_ global optimization problem. This is a
    multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Mishra04}}({x}) = \sqrt{\lvert \sin{\sqrt{\lvert
       x_1^2 + x_2^2 \rvert}} \rvert} + 0.01(x_1 + x_2)

    with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.

    *Global optimum*: :math:`f(x) = -0.17767` for
    :math:`x = [-8.71499636, -9.0533148]`

    .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
    For Global Optimization Problems Int. Journal of Mathematical Modelling
    and Numerical Optimisation, 2013, 4, 150-194.

    TODO: I think that Jamil#77 has the wrong minimum, not possible
    """

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))

        self.global_optimum = [[-8.88055269734, -8.89097599857]]
        self.fglob = -0.177715264826

    def fun(self, x, *args):
        self.nfev += 1

        return (0.01 * (x[0] + x[1])
                + sqrt(abs(sin(sqrt(abs(x[0] ** 2 + x[1] ** 2))))))


class Mishra05(Benchmark):

    r"""
    Mishra 5 objective function.

    This class defines the Mishra 5 [1]_ global optimization problem. This is a
    multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Mishra05}}(x) = \left [ \sin^2 ((\cos(x_1) + \cos(x_2))^2)
       + \cos^2 ((\sin(x_1) + \sin(x_2))^2) + x_1 \right ]^2 + 0.01(x_1 + x_2)


    with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.

    *Global optimum*: :math:`f(x) = -0.119829` for :math:`x = [-1.98682, -10]`

    .. [1] Mishra, S. Global Optimization by Differential Evolution and
    Particle Swarm Methods: Evaluation on Some Benchmark Functions.
    Munich Personal RePEc Archive, 2006, 1005

    TODO Line 381 in paper
    """

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))

        self.global_optimum = [[-1.98682, -10.0]]
        self.fglob = -1.019829519930646

    def fun(self, x, *args):
        self.nfev += 1

        return (0.01 * x[0] + 0.1 * x[1]
                + (sin((cos(x[0]) + cos(x[1])) ** 2) ** 2
                   + cos((sin(x[0]) + sin(x[1])) ** 2) ** 2 + x[0]) ** 2)


class Mishra06(Benchmark):

    r"""
    Mishra 6 objective function.

    This class defines the Mishra 6 [1]_ global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Mishra06}}(x) = -\log{\left [ \sin^2 ((\cos(x_1)
       + \cos(x_2))^2) - \cos^2 ((\sin(x_1) + \sin(x_2))^2) + x_1 \right ]^2}
       + 0.01 \left[(x_1 -1)^2 + (x_2 - 1)^2 \right]


    with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.

    *Global optimum*: :math:`f(x_i) = -2.28395` for :math:`x = [2.88631, 1.82326]`

    .. [1] Mishra, S. Global Optimization by Differential Evolution and
    Particle Swarm Methods: Evaluation on Some Benchmark Functions.
    Munich Personal RePEc Archive, 2006, 1005

    TODO line 397
    """

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))

        self.global_optimum = [[2.88631, 1.82326]]
        self.fglob = -2.28395

    def fun(self, x, *args):
        self.nfev += 1

        a = 0.1 * ((x[0] - 1) ** 2 + (x[1] - 1) ** 2)
        u = (cos(x[0]) + cos(x[1])) ** 2
        v = (sin(x[0]) + sin(x[1])) ** 2
        return a - log((sin(u) ** 2 - cos(v) ** 2 + x[0]) ** 2)


class Mishra07(Benchmark):

    r"""
    Mishra 7 objective function.

    This class defines the Mishra 7 [1]_ global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Mishra07}}(x) = \left [\prod_{i=1}^{n} x_i - n! \right]^2


    Here, :math:`n` represents the number of dimensions and
    :math:`x_i \in [-10, 10]` for :math:`i = 1, ..., n`.

    *Global optimum*: :math:`f(x) = 0` for :math:`x_i = \sqrt{n}`
    for :math:`i = 1, ..., n`

    .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
    For Global Optimization Problems Int. Journal of Mathematical Modelling
    and Numerical Optimisation, 2013, 4, 150-194.
    """
    change_dimensionality = True

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
        self.custom_bounds = [(-2, 2), (-2, 2)]
        self.global_optimum = [[sqrt(self.N)
                               for i in range(self.N)]]
        self.fglob = 0.0

    def fun(self, x, *args):
        self.nfev += 1

        return (prod(x) - factorial(self.N)) ** 2.0


class Mishra08(Benchmark):

    r"""
    Mishra 8 objective function.

    This class defines the Mishra 8 [1]_ global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Mishra08}}(x) = 0.001 \left[\lvert x_1^{10} - 20x_1^9
       + 180x_1^8 - 960 x_1^7 + 3360x_1^6 - 8064x_1^5 + 13340x_1^4 - 15360x_1^3
       + 11520x_1^2 - 5120x_1 + 2624 \rvert \lvert x_2^4 + 12x_2^3 + 54x_2^2
       + 108x_2 + 81 \rvert \right]^2


    with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.

    *Global optimum*: :math:`f(x) = 0` for :math:`x = [2, -3]`

    .. [1] Mishra, S. Global Optimization by Differential Evolution and
    Particle Swarm Methods: Evaluation on Some Benchmark Functions.
    Munich Personal RePEc Archive, 2006, 1005

    TODO Line 1065
    """

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
        self.custom_bounds = [(1.0, 2.0), (-4.0, 1.0)]
        self.global_optimum = [[2.0, -3.0]]
        self.fglob = 0.0

    def fun(self, x, *args):
        self.nfev += 1

        val = abs(x[0] ** 10 - 20 * x[0] ** 9 + 180 * x[0] ** 8
                  - 960 * x[0] ** 7 + 3360 * x[0] ** 6 - 8064 * x[0] ** 5
                  + 13340 * x[0] ** 4 - 15360 * x[0] ** 3 + 11520 * x[0] ** 2
                  - 5120 * x[0] + 2624)
        val += abs(x[1] ** 4 + 12 * x[1] ** 3 +
                   54 * x[1] ** 2 + 108 * x[1] + 81)
        return 0.001 * val ** 2


class Mishra09(Benchmark):

    r"""
    Mishra 9 objective function.

    This class defines the Mishra 9 [1]_ global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Mishra09}}({x}) = \left[ ab^2c + abc^2 + b^2
       + (x_1 + x_2 - x_3)^2 \right]^2


    Where, in this exercise:

    .. math::

        \begin{cases} a = 2x_1^3 + 5x_1x_2 + 4x_3 - 2x_1^2x_3 - 18 \\
        b = x_1 + x_2^3 + x_1x_2^2 + x_1x_3^2 - 22 \\
        c = 8x_1^2 + 2x_2x_3 + 2x_2^2 + 3x_2^3 - 52 \end{cases}


    with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2, 3`.

    *Global optimum*: :math:`f(x) = 0` for :math:`x = [1, 2, 3]`

    .. [1] Mishra, S. Global Optimization by Differential Evolution and
    Particle Swarm Methods: Evaluation on Some Benchmark Functions.
    Munich Personal RePEc Archive, 2006, 1005

    TODO Line 1103
    """

    def __init__(self, dimensions=3):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
        self.global_optimum = [[1.0, 2.0, 3.0]]
        self.fglob = 0.0

    def fun(self, x, *args):
        self.nfev += 1

        a = (2 * x[0] ** 3 + 5 * x[0] * x[1]
             + 4 * x[2] - 2 * x[0] ** 2 * x[2] - 18)
        b = x[0] + x[1] ** 3 + x[0] * x[1] ** 2 + x[0] * x[2] ** 2 - 22.0
        c = (8 * x[0] ** 2 + 2 * x[1] * x[2]
            + 2 * x[1] ** 2 + 3 * x[1] ** 3 - 52)

        return (a * c * b ** 2 + a * b * c ** 2 + b ** 2
                + (x[0] + x[1] - x[2]) ** 2) ** 2


class Mishra10(Benchmark):

    r"""
    Mishra 10 objective function.

    This class defines the Mishra 10 global optimization problem. This is a
    multimodal minimization problem defined as follows:

    .. math::
    TODO - int(x) should be used instead of floor(x)!!!!!
       f_{\text{Mishra10}}({x}) = \left[ \lfloor x_1 \perp x_2 \rfloor -
       \lfloor x_1 \rfloor - \lfloor x_2 \rfloor \right]^2

    with :math:`x_i \in [-10, 10]` for :math:`i =1, 2`.

    *Global optimum*: :math:`f(x) = 0` for :math:`x = [2, 2]`

    .. [1] Mishra, S. Global Optimization by Differential Evolution and
    Particle Swarm Methods: Evaluation on Some Benchmark Functions.
    Munich Personal RePEc Archive, 2006, 1005

    TODO line 1115
    """

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
        self.global_optimum = [[2.0, 2.0]]
        self.fglob = 0.0

    def fun(self, x, *args):
        self.nfev += 1

        x1, x2 = int(x[0]), int(x[1])
        f1 = x1 + x2
        f2 = x1 * x2
        return (f1 - f2) ** 2.0


class Mishra11(Benchmark):

    r"""
    Mishra 11 objective function.

    This class defines the Mishra 11 [1]_ global optimization problem. This is a
    multimodal minimization problem defined as follows:

    .. math::

       f_{\text{Mishra11}}(x) = \left [ \frac{1}{n} \sum_{i=1}^{n} \lvert x_i
       \rvert - \left(\prod_{i=1}^{n} \lvert x_i \rvert \right )^{\frac{1}{n}}
       \right]^2


    Here, :math:`n` represents the number of dimensions and
    :math:`x_i \in [-10, 10]` for :math:`i = 1, ..., n`.

    *Global optimum*: :math:`f(x) = 0` for :math:`x_i = 0` for
    :math:`i = 1, ..., n`

    .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
    For Global Optimization Problems Int. Journal of Mathematical Modelling
    and Numerical Optimisation, 2013, 4, 150-194.
    """
    change_dimensionality = True

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
        self.custom_bounds = [(-3, 3), (-3, 3)]

        self.global_optimum = [[0.0 for _ in range(self.N)]]
        self.fglob = 0.0

    def fun(self, x, *args):
        self.nfev += 1

        N = self.N
        return ((1.0 / N) * sum(abs(x)) - (prod(abs(x))) ** 1.0 / N) ** 2.0


class MultiModal(Benchmark):

    r"""
    MultiModal objective function.

    This class defines the MultiModal global optimization problem. This
    is a multimodal minimization problem defined as follows:

    .. math::

       f_{\text{MultiModal}}(x) = \left( \sum_{i=1}^n \lvert x_i \rvert
       \right) \left( \prod_{i=1}^n \lvert x_i \rvert \right)


    Here, :math:`n` represents the number of dimensions and
    :math:`x_i \in [-10, 10]` for :math:`i = 1, ..., n`.

    *Global optimum*: :math:`f(x) = 0` for :math:`x_i = 0` for
    :math:`i = 1, ..., n`

    .. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
    """
    change_dimensionality = True

    def __init__(self, dimensions=2):
        Benchmark.__init__(self, dimensions)

        self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
        self.custom_bounds = [(-5, 5), (-5, 5)]

        self.global_optimum = [[0.0 for _ in range(self.N)]]
        self.fglob = 0.0

    def fun(self, x, *args):
        self.nfev += 1

        return sum(abs(x)) * prod(abs(x))
