import numpy as np
def RSE(pred, true):
return np.sqrt(np.sum((true-pred)**2)) / np.sqrt(np.sum((true-true.mean())**2))
def CORR(pred, true):
u = ((true-true.mean(0))*(pred-pred.mean(0))).sum(0)
d = np.sqrt(((true-true.mean(0))**2*(pred-pred.mean(0))**2).sum(0))
return (u/d).mean(-1)
def MAE(pred, true):
return np.mean(np.abs(pred-true))
def MSE(pred, true):
return np.mean((pred-true)**2)
def RMSE(pred, true):
return np.sqrt(MSE(pred, true))
def MAPE(pred, true):
return np.mean(np.abs((pred - true) / true))
def MSPE(pred, true):
return np.mean(np.square((pred - true) / true))
def metric(pred, true):
mae = MAE(pred, true)
mse = MSE(pred, true)
rmse = RMSE(pred, true)
mape = MAPE(pred, true)
mspe = MSPE(pred, true)
return mae,mse,rmse,mape,mspe
MAE, MSE, RMSE, MAPE calculation method
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Origin blog.csdn.net/qq_38735017/article/details/130654931
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