# coding:utf-8
import autograd.numpy as np

EPS = 1e-15


def unhot(function):
    """Convert one-hot representation into one column."""

    def wrapper(actual, predicted):
        if len(actual.shape) > 1 and actual.shape[1] > 1:
            actual = actual.argmax(axis=1)
        if len(predicted.shape) > 1 and predicted.shape[1] > 1:
            predicted = predicted.argmax(axis=1)
        return function(actual, predicted)

    return wrapper


def absolute_error(actual, predicted):
    return np.abs(actual - predicted)


@unhot
def classification_error(actual, predicted):
    return (actual != predicted).sum() / float(actual.shape[0])


@unhot
def accuracy(actual, predicted):
    return 1.0 - classification_error(actual, predicted)


def mean_absolute_error(actual, predicted):
    return np.mean(absolute_error(actual, predicted))


def squared_error(actual, predicted):
    return (actual - predicted) ** 2


def squared_log_error(actual, predicted):
    return (np.log(np.array(actual) + 1) - np.log(np.array(predicted) + 1)) ** 2


def mean_squared_log_error(actual, predicted):
    return np.mean(squared_log_error(actual, predicted))


def mean_squared_error(actual, predicted):
    return np.mean(squared_error(actual, predicted))


def root_mean_squared_error(actual, predicted):
    return np.sqrt(mean_squared_error(actual, predicted))


def root_mean_squared_log_error(actual, predicted):
    return np.sqrt(mean_squared_log_error(actual, predicted))


def logloss(actual, predicted):
    predicted = np.clip(predicted, EPS, 1 - EPS)
    loss = -np.sum(actual * np.log(predicted))
    return loss / float(actual.shape[0])


def hinge(actual, predicted):
    return np.mean(np.max(1.0 - actual * predicted, 0.0))


def binary_crossentropy(actual, predicted):
    predicted = np.clip(predicted, EPS, 1 - EPS)
    return np.mean(-np.sum(actual * np.log(predicted) + (1 - actual) * np.log(1 - predicted)))


# aliases
mse = mean_squared_error
rmse = root_mean_squared_error
mae = mean_absolute_error


def get_metric(name):
    """Return metric function by name"""
    try:
        return globals()[name]
    except Exception:
        raise ValueError("Invalid metric function.")
