基于LSTM的滚动轴承剩余使用寿命预测（FEMTO-ST数据集，Python，iypnb文件）

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https://www.zhihu.com/consult/people/792359672131756032

from math import sqrt
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score


# 使用sklearn计算 MSE 、 RMSE、 MAE、r2
#test_answer = np.array(test_answer)
# generate dataset
# predicted expect and calculate confidence interval
#predicted_test = np.mean(test_answer, 0)
#my_true_test = yScaler.inverse_transform(yTest)
print("mean_absolute_error:", mean_absolute_error(yTrue[(timeStep+1):], predicted_expect))
print("mean_squared_error:", mean_squared_error(yTrue[(timeStep+1):], predicted_expect))
print("rmse:", sqrt(mean_squared_error(yTrue[(timeStep+1):], predicted_expect)))
print("r2 score:", r2_score(yTrue[(timeStep+1):], predicted_expect))

mean_absolute_error: 0.1584930792342556

mean_squared_error: 0.0365404064887125

rmse: 0.19115545110907117

r2 score: 0.5566075250325875