"""Module containing definitions of arithmetic functions used by perceptrons"""

from abc import ABC, abstractmethod

import numpy as np

from NaiveNeurals.utils import ErrorAlgorithm


class ActivationFunction(ABC):
    """Abstract function for defining functions"""

    label = ''

    @staticmethod
    @abstractmethod
    def function(arg: np.array) -> np.array:
        """Implementation of function

        :param arg: float
        :return: float
        """
        raise NotImplementedError()

    @classmethod
    @abstractmethod
    def prime(cls, arg: np.array) -> np.array:
        """First derivative of implemented function

        :param arg: float
        :return: float
        """
        raise NotImplementedError()


class Sigmoid(ActivationFunction):
    """Represents sigmoid function and its derivative"""

    label = 'sigmoid'

    @staticmethod
    def function(arg: np.array) -> np.array:
        """Calculate sigmoid(arg)

        :param arg: float input value
        :return: float sig(arg) value
        """
        return 1 / (1 + np.exp(-arg))

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of sigmoid's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        return cls.function(arg) * (1 - cls.function(arg))


class Tanh(ActivationFunction):
    """Represents hyperbolic tangent"""

    label = 'tanh'

    @staticmethod
    def function(arg: np.array) -> np.array:
        """Calculate tanh(arg)

        :param arg: float input value
        :return: float tanh(arg) value
        """
        return np.tanh(arg)

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of tanh's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        return 1 - np.tanh(arg)**2


class Linear(ActivationFunction):
    """Represents linear function"""

    label = 'lin'

    @staticmethod
    def function(arg: np.array) -> np.array:
        """Calculate lin(arg)

        :param arg: float input value
        :return: float lin(arg) value
        """
        return arg

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of lin's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        ones = np.array(arg)
        ones[::] = 1.0
        return ones


class SoftMax(ActivationFunction):
    """Represents SoftMax function

    The ``softmax`` function takes an N-dimensional vector of arbitrary real values and produces
    another N-dimensional vector with real values in the range (0, 1) that add up to 1.0.

    source: https://eli.thegreenplace.net/2016/the-softmax-function-and-its-derivative/
    """

    label = 'softmax'

    @staticmethod
    def function(arg: np.array, beta: int = 20) -> np.array:        # pylint: disable=arguments-differ
        """Calculate softmax(arg)

        :param arg: float input value
        :param beta: scaling parameter
        :return: float softmax(arg) value
        """
        exps = np.exp(beta * arg - beta * arg.max())
        return exps / np.sum(exps)

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of softmax's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        return cls.function(arg) * (1 - cls.function(arg))


class SoftPlus(ActivationFunction):
    """Represents softplus function"""

    label = 'softplus'

    @staticmethod
    def function(arg: np.array) -> np.array:
        """Calculate softplus(arg)

        :param arg: float input value
        :return: float softmax(arg) value
        """
        return np.log(1 + np.exp(arg))

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of softplus's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        return 1/(1 + np.exp(-arg))


def get_activation_function(label: str) -> ActivationFunction:
    """Get activation function by label

    :param label: string denoting function
    :return: callable function
    """
    if label == 'lin':
        return Linear()
    if label == 'sigmoid':
        return Sigmoid()
    if label == 'tanh':
        return Tanh()
    return Sigmoid()


def calculate_error(target: np.array, actual: np.array,
                    func_type: ErrorAlgorithm = ErrorAlgorithm.SQR) -> np.array:
    """Calculates error for provided actual and targeted data.

    :param target: target data
    :param actual: actual training data
    :param func_type: denotes type of used function for error
    :return: calculated error
    """
    if func_type == ErrorAlgorithm.SQR:
        return np.sum(0.5 * np.power(actual - target, 2), axis=1)
    elif func_type == ErrorAlgorithm.CE:
        return -1 * np.sum(target * np.log(abs(actual)), axis=1)
    raise NotImplementedError()

"""Module containing definitions of arithmetic functions used by perceptrons"""

from abc import ABC, abstractmethod

import numpy as np

from NaiveNeurals.utils import ErrorAlgorithm


class ActivationFunction(ABC):
    """Abstract function for defining functions"""

    label = ''

    @staticmethod
    @abstractmethod
    def function(arg: np.array) -> np.array:
        """Implementation of function

        :param arg: float
        :return: float
        """
        raise NotImplementedError()

    @classmethod
    @abstractmethod
    def prime(cls, arg: np.array) -> np.array:
        """First derivative of implemented function

        :param arg: float
        :return: float
        """
        raise NotImplementedError()


class Sigmoid(ActivationFunction):
    """Represents sigmoid function and its derivative"""

    label = 'sigmoid'

    @staticmethod
    def function(arg: np.array) -> np.array:
        """Calculate sigmoid(arg)

        :param arg: float input value
        :return: float sig(arg) value
        """
        return 1 / (1 + np.exp(-arg))

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of sigmoid's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        return cls.function(arg) * (1 - cls.function(arg))


class Tanh(ActivationFunction):
    """Represents hyperbolic tangent"""

    label = 'tanh'

    @staticmethod
    def function(arg: np.array) -> np.array:
        """Calculate tanh(arg)

        :param arg: float input value
        :return: float tanh(arg) value
        """
        return np.tanh(arg)

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of tanh's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        return 1 - np.tanh(arg)**2


class Linear(ActivationFunction):
    """Represents linear function"""

    label = 'lin'

    @staticmethod
    def function(arg: np.array) -> np.array:
        """Calculate lin(arg)

        :param arg: float input value
        :return: float lin(arg) value
        """
        return arg

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of lin's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        ones = np.array(arg)
        ones[::] = 1.0
        return ones


class SoftMax(ActivationFunction):
    """Represents SoftMax function

    The ``softmax`` function takes an N-dimensional vector of arbitrary real values and produces
    another N-dimensional vector with real values in the range (0, 1) that add up to 1.0.

    source: https://eli.thegreenplace.net/2016/the-softmax-function-and-its-derivative/
    """

    label = 'softmax'

    @staticmethod
    def function(arg: np.array, beta: int = 20) -> np.array:        # pylint: disable=arguments-differ
        """Calculate softmax(arg)

        :param arg: float input value
        :param beta: scaling parameter
        :return: float softmax(arg) value
        """
        exps = np.exp(beta * arg - beta * arg.max())
        return exps / np.sum(exps)

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of softmax's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        return cls.function(arg) * (1 - cls.function(arg))


class SoftPlus(ActivationFunction):
    """Represents softplus function"""

    label = 'softplus'

    @staticmethod
    def function(arg: np.array) -> np.array:
        """Calculate softplus(arg)

        :param arg: float input value
        :return: float softmax(arg) value
        """
        return np.log(1 + np.exp(arg))

    @classmethod
    def prime(cls, arg: np.array) -> np.array:
        """Calculate value of softplus's prime derivative for given arg

        :param arg: float input value
        :return: float value
        """
        return 1/(1 + np.exp(-arg))


def get_activation_function(label: str) -> ActivationFunction:
    """Get activation function by label

    :param label: string denoting function
    :return: callable function
    """
    if label == 'lin':
        return Linear()
    if label == 'sigmoid':
        return Sigmoid()
    if label == 'tanh':
        return Tanh()
    return Sigmoid()


def calculate_error(target: np.array, actual: np.array,
                    func_type: ErrorAlgorithm = ErrorAlgorithm.SQR) -> np.array:
    """Calculates error for provided actual and targeted data.

    :param target: target data
    :param actual: actual training data
    :param func_type: denotes type of used function for error
    :return: calculated error
    """
    if func_type == ErrorAlgorithm.SQR:
        return np.sum(0.5 * np.power(actual - target, 2), axis=1)
    elif func_type == ErrorAlgorithm.CE:
        return -1 * np.sum(target * np.log(abs(actual)), axis=1)
    raise NotImplementedError()

Module containing definitions of arithmetic functions used by perceptrons Abstract function for defining functions Implementation of function          :param arg: float         :return: float First derivative of implemented function          :param arg: float         :return: float Represents sigmoid function and its derivative Calculate sigmoid(arg)          :param arg: float input value         :return: float sig(arg) value Calculate value of sigmoid's prime derivative for given arg          :param arg: float input value         :return: float value Represents hyperbolic tangent Calculate tanh(arg)          :param arg: float input value         :return: float tanh(arg) value Calculate value of tanh's prime derivative for given arg          :param arg: float input value         :return: float value Represents linear function Calculate lin(arg)          :param arg: float input value         :return: float lin(arg) value Calculate value of lin's prime derivative for given arg          :param arg: float input value         :return: float value Represents SoftMax function      The ``softmax`` function takes an N-dimensional vector of arbitrary real values and produces     another N-dimensional vector with real values in the range (0, 1) that add up to 1.0.      source: https://eli.thegreenplace.net/2016/the-softmax-function-and-its-derivative/ # pylint: disable=arguments-differ Calculate softmax(arg)          :param arg: float input value         :param beta: scaling parameter         :return: float softmax(arg) value Calculate value of softmax's prime derivative for given arg          :param arg: float input value         :return: float value Represents softplus function Calculate softplus(arg)          :param arg: float input value         :return: float softmax(arg) value Calculate value of softplus's prime derivative for given arg          :param arg: float input value         :return: float value Get activation function by label      :param label: string denoting function     :return: callable function Calculates error for provided actual and targeted data.      :param target: target data     :param actual: actual training data     :param func_type: denotes type of used function for error     :return: calculated error