dose_evaluation_metrics

import numpy as np

""" Mean Error """
def mean_error(real, pred):
    ME = np.subtract(np.array(real), np.array(pred)).mean()
    ME_std = np.std(np.subtract(np.array(real), np.array(pred)))
    return print("mean_error =", ME, "±", ME_std)


""" Mean Absolute Error """
def mean_abs_error(real, pred):
    MAE = (np.abs(np.subtract(np.array(real), np.array(pred)))).mean()
    MAE_std = np.std(np.abs(np.subtract(np.array(real), np.array(pred))))
    return print("mean_abs_error =", MAE, "±", MAE_std)


""" Mean-Squared Error """
def mean_sqrd_error(real, pred):
    MSE = np.square(np.subtract(np.array(real), np.array(pred))).mean()
    MSE_std = np.std(np.square(np.subtract(np.array(real), np.array(pred))))
    return print("mean_sqrd_error =", MSE, "±", MSE_std)


""" Median Absolute Error """
def median_abs_error(real, pred):
    AE = np.abs(np.subtract(np.array(real), np.array(pred)))
    MdAE = np.median(AE)
    return print("median_abs_error =", MdAE)


""" Mean Absolute Percentage Error """
def mean_abs_percent_error(real, pred):
    MAPE = np.abs(np.subtract(np.array(real), np.array(pred)) / np.array(real)).mean()
    MAPE_std = np.std(np.abs(np.subtract(np.array(real), np.array(pred)) / np.array(real)))
    return print("mean_abs_percent_error =", MAPE, "±", MAPE_std)