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d4c7aae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | 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
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