import numpy as np

# Mean Absolute Error
def mae(predict, actual):
    """
    Examples(rounded for precision):
    >>> actual = [1,2,3];predict = [1,4,3]
    >>> np.around(mae(predict,actual),decimals = 2)
    0.67
    >>> actual = [1,1,1];predict = [1,1,1]
    >>> mae(predict,actual)
    0.0
    """
    predict = np.array(predict)
    actual = np.array(actual)

    difference = abs(predict - actual)
    score = difference.mean()

    return score


# Mean Squared Error
def mse(predict, actual):
    """
    Examples(rounded for precision):
    >>> actual = [1,2,3];predict = [1,4,3]
    >>> np.around(mse(predict,actual),decimals = 2)
    1.33
    >>> actual = [1,1,1];predict = [1,1,1]
    >>> mse(predict,actual)
    0.0
    """
    predict = np.array(predict)
    actual = np.array(actual)

    difference = predict - actual
    square_diff = np.square(difference)

    score = square_diff.mean()
    return score


# Root Mean Squared Error
def rmse(predict, actual):
    """
    Examples(rounded for precision):
    >>> actual = [1,2,3];predict = [1,4,3]
    >>> np.around(rmse(predict,actual),decimals = 2)
    1.15
    >>> actual = [1,1,1];predict = [1,1,1]
    >>> rmse(predict,actual)
    0.0
    """
    predict = np.array(predict)
    actual = np.array(actual)

    difference = predict - actual
    square_diff = np.square(difference)
    mean_square_diff = square_diff.mean()
    score = np.sqrt(mean_square_diff)
    return score


# Root Mean Square Logarithmic Error
def rmsle(predict, actual):
    """
    Examples(rounded for precision):
    >>> actual = [10,10,30];predict = [10,2,30]
    >>> np.around(rmsle(predict,actual),decimals = 2)
    0.75
    >>> actual = [1,1,1];predict = [1,1,1]
    >>> rmsle(predict,actual)
    0.0
    """
    predict = np.array(predict)
    actual = np.array(actual)

    log_predict = np.log(predict + 1)
    log_actual = np.log(actual + 1)

    difference = log_predict - log_actual
    square_diff = np.square(difference)
    mean_square_diff = square_diff.mean()

    score = np.sqrt(mean_square_diff)

    return score


# Mean Bias Deviation
def mbd(predict, actual):
    """
    This value is Negative, if the model underpredicts,
    positive, if it overpredicts.
    Example(rounded for precision):
    Here the model overpredicts
    >>> actual = [1,2,3];predict = [2,3,4]
    >>> np.around(mbd(predict,actual),decimals = 2)
    50.0
    Here the model underpredicts
    >>> actual = [1,2,3];predict = [0,1,1]
    >>> np.around(mbd(predict,actual),decimals = 2)
    -66.67
    """
    predict = np.array(predict)
    actual = np.array(actual)

    difference = predict - actual
    numerator = np.sum(difference) / len(predict)
    denumerator = np.sum(actual) / len(predict)
    # print(numerator, denumerator)
    score = float(numerator) / denumerator * 100

    return score