from __future__ import absolute_import
import numpy as np
from . import backend as K


def mean_squared_error(y_true, y_pred):
    return K.mean(K.square(y_pred - y_true), axis=-1)


def root_mean_squared_error(y_true, y_pred):
    return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))


def mean_absolute_error(y_true, y_pred):
    return K.mean(K.abs(y_pred - y_true), axis=-1)


def mean_absolute_percentage_error(y_true, y_pred):
    diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), np.inf))
    return 100. * K.mean(diff, axis=-1)


def mean_squared_logarithmic_error(y_true, y_pred):
    first_log = K.log(K.clip(y_pred, K.epsilon(), np.inf) + 1.)
    second_log = K.log(K.clip(y_true, K.epsilon(), np.inf) + 1.)
    return K.mean(K.square(first_log - second_log), axis=-1)


def squared_hinge(y_true, y_pred):
    return K.mean(K.square(K.maximum(1. - y_true * y_pred, 0.)), axis=-1)


def hinge(y_true, y_pred):
    return K.mean(K.maximum(1. - y_true * y_pred, 0.), axis=-1)


def categorical_crossentropy(y_true, y_pred):
    '''Expects a binary class matrix instead of a vector of scalar classes.
    '''
    return K.mean(K.categorical_crossentropy(y_pred, y_true), axis=-1)


def binary_crossentropy(y_true, y_pred):
    """Computes the binary cross-entropy between predictions y_pred and targets
    y_true.

    .. math:: L = -t \\log(p) - (1 - t) \\log(1 - p)

    Parameters
    ----------
    y_true : Theano tensor
        Targets in [0, 1], such as ground truth labels.
    y_pred : Theano tensor
        Predictions in (0, 1), such as sigmoidal output of a neural network.

    Returns
    -------
    Theano tensor
        An expression for the element-wise binary cross-entropy.

    Notes
    -----
    This is the loss function of choice for binary classification problems
    and sigmoid output units.
    """
    return K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)


def poisson_loss(y_true, y_pred):
    return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1)

# aliases
mse = MSE = mean_squared_error
rmse = RMSE = root_mean_squared_error
mae = MAE = mean_absolute_error
mape = MAPE = mean_absolute_percentage_error
msle = MSLE = mean_squared_logarithmic_error

from .utils.generic_utils import get_from_module
def get(identifier):
    return get_from_module(identifier, globals(), 'objective')
