# -*- encoding:utf-8 -*-

'''
@Author : dingjiawen
@Date : 2023/11/23 16:32
@Usage : 
@Desc :
'''

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


def mae(y_true, y_predict):
    return np.mean(np.mean(np.abs(y_true - y_predict), axis=-1))


def mape(y_true, y_predict):
    return np.mean(np.mean(np.abs((y_true - y_predict) / y_true), axis=-1))


def score(y_true, y_predict):
    Eri = y_predict - y_true
    dw = np.log(0.5)
    total = []

    if len(y_true.shape) > 1:
        b, c = y_true.shape

        for i in range(b):
            cList = []
            for j in range(c):
                if Eri[i, j] < 0:
                    cList.append(np.exp(-(Eri[i, j] / 13)) - 1)
                else:
                    cList.append(np.exp((Eri[i, j] / 10)) - 1)
            total.append(np.stack(cList))
        total = np.stack(total, axis=0)

        return np.mean(np.mean(total, axis=-1))
    else:
        b, = y_true.shape
        for i in range(b):
            if Eri[i] <= 0:
                total.append(np.exp((-dw) * (Eri[i] / 5)))
            else:
                total.append(np.exp((dw) * (Eri[i] / 20)))
        total = np.stack(total, axis=0)

        return np.mean(total)

    pass


def rmse(y_true, y_predict):
    loss = np.sqrt(np.mean(np.mean((y_predict - y_true) ** 2, axis=-1)))

    return loss


def getEvaluate(y_true, y_predict):
    return [rmse(y_true, y_predict), mae(y_true, y_predict), mape(y_true, y_predict), score(y_true, y_predict)]


if __name__ == '__main__':
    # a = torch.log(torch.tensor(0.5))
    # print(a)
    # x = torch.rand((32,))
    # y = torch.rand((32,))
    x = np.random.random(size=(32,))
    y = np.random.random(size=(32,))

    print(mae(x, y))
    print(mape(x, y))
    print(rmse(x, y))
    print(score(x, y))