Python小记：9.求多维坐标点之间的距离、均方误差（MSE）、均方根误差（RMSE）、平均绝对误差（MAE）等

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