我要评论1.多维坐标点之间的距离计算：
import math
def distance(p0,p1,digits=2):
a=map(lambda x: (x[0]-x[1])**2, zip(p0, p1))
return round(math.sqrt(sum(a)),digits)
测试：
p0=(1,4,5)
p1=(1,3,5)
print(distance(p0,p1))
print(" ")
p2=(1,2)
p3=(2,3)
print(distance(p2,p3,3))
print(" ")
结果：
1.0
1.414
Press any key to continue . . .
2.多维坐标点相关计算：
a=[1,0,1]
b=[1,0,2]
error = []
for i in range(len(a)):
error.append(a[i] - b[i])
print("Errors: ", error)
print(error)
squaredError = []
absError = []
for val in error:
squaredError.append(val * val)#a-b之差平方
absError.append(abs(val))#误差绝对值
print("Square Error: ", squaredError)
print("Absolute Value of Error: ", absError)
print("MSE = ", sum(squaredError) / len(squaredError))#均方误差MSE
from math import sqrt
print("RMSE = ", sqrt(sum(squaredError) / len(squaredError)))#均方根误差RMSE
print("MAE = ", sum(absError) / len(absError))#平均绝对误差MAE
aDeviation = []
aMean = sum(a) / len(a)#a平均值
for val in a:
aDeviation.append((val - aMean) * (val - aMean))
print("a efficiency = ", 1-sum(squaredError) / sum(aDeviation))#效率系数E
本文地址：https://blog.csdn.net/weixin_44671418/article/details/107309968
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