RMSE
洛 2021/10/30
# gma.math.Evaluation.RMSE(Measure, Simulation, Axis = 0)
功能：【RMSE】。均方根误差。
参数：
Measure：list||array。第一组数据。
Simulation：list||array。第二组数据。
可选参数：
Axis = int 。数据评估使用的轴。
返回：float||array。
示例：
from gma import math
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序列（1 维）
MEA = [15.1, 33, 88, 158.4]
SIM = [0.8, 1.7, 7.8, 7.4]
EVA = math.Evaluation.RMSE(MEA, SIM)
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>>> 87.20266624364189
更多维度
MEA = [[ 0.05120073, 0.05444646, 0.05096978, 0.05096978],
[ 0.16359164, 0.18061367, 0.16168582, 0.16168582],
[-0.07699195, -0.07706093, -0.07740774, -0.07740774],
[ 0.17469311, 0.19315895, 0.15614156, 0.15614156],
[ 0.2006536 , 0.18265162, 0.15587704, 0.15587704]]
SIM = [[ 0.22857143, 0.22911051, 0.1908772 , 0.1908772 ],
[ 0.2956548 , 0.3080475 , 0.22230114, 0.22230114],
[ 0.32706437, 0.35 , 0.29352903, 0.29352903],
[ 0.03658536, 0.03522885, 0.03478987, 0.03478987],
[-0.10225949, -0.07193749, -0.05467691, -0.05467691]]
## 按照第一个维度计算
EVA0 = math.Evaluation.RMSE(MEA, SIM, Axis = 0)
print('"Axis = 0":', EVA0)
## 按照第二个维度计算
EVA1 = gma.math.Evaluation.RMSE(MEA, SIM, Axis = 1)
print('"Axis = 1":', EVA1)
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>>> "Axis = 0": [0.25416241 0.25254128 0.20971485 0.20971485]
>>> "Axis = 1": [0.15899376 0.10127746 0.39396406 0.13552503 0.2476075 ]