| import numpy as np | |
| # lost function and its derivatives | |
| def mse(y_true, y_pred): | |
| # print(f'y-true = {y_true} & y-predict = {y_pred}') | |
| return np.mean(np.power(y_true-y_pred, 2)) | |
| def mse_prime(y_true, y_pred): | |
| # dE/dY = d(ERROR ROOT MEAN SQUARE)/dY | |
| # dE/dY = d/dy (y-y_predict)^2 | |
| # dE/dY = 2(y-y_predict) | |
| # dE/dY = 2*error | |
| return 2*(y_pred-y_true)/y_true.size | |
| def rmspe(y_true, y_pred): | |
| rmspe = (np.sqrt(np.mean(np.square((y_true - y_pred) / y_true)))) * 100 | |
| return rmspe | |