在python中将类型从BigFloat转换为Float

在python中将类型从BigFloat转换为Float,python,numpy,bigfloat,Python,Numpy,Bigfloat,我正在使用python中的BigFloat库计算exp。但是我必须计算一个大浮点数矩阵的逆。我使用函数numpy.linalg.inv,但我得到以下错误: 找不到与指定签名和强制转换匹配的循环 ufunc投资公司 所以我想说我需要将BigFloat转换成其他类型。我该怎么做呢?考虑一个大浮点值的二维列表(带exp): 结果: A single element in bigfloat: 2.7182818284590452353602874713526624977572470936999

我正在使用python中的BigFloat库计算exp。但是我必须计算一个大浮点数矩阵的逆。我使用函数
numpy.linalg.inv
,但我得到以下错误:

找不到与指定签名和强制转换匹配的循环 ufunc投资公司


所以我想说我需要将BigFloat转换成其他类型。我该怎么做呢?

考虑一个大浮点值的二维列表(带exp):

结果:

A single element in bigfloat: 
    2.71828182845904523536028747135266249775724709369995957496696762772407663035354759457138217852516642742746639193200305992181741359662904357290033429526059563073813232862794349076323382988075319525101901157383418793070215408914993488416750924476146066808226480016847741185374234544243710753907774499206950

Numpived matrix of bigfloat: 
[[ 2.71828183  1.64872127  1.39561243  1.28402542]
 [ 1.64872127  1.39561243  1.28402542  1.22140276]
 [ 1.39561243  1.28402542  1.22140276  1.18136041]
 [ 1.28402542  1.22140276  1.18136041  1.15356499]]

Inverse of numpivized bigfloat matrix: 
[[    3.82106765   -30.68951855    61.37113204   -34.60880961]
 [  -30.68951855   385.05574805  -890.19682475   538.10687764]
 [   61.37113204  -890.19682475  2211.11911647 -1390.16166038]
 [  -34.60880961   538.10687764 -1390.16166038   893.29628089]]
应用逐点
float()
操作。大浮点覆盖
A single element in bigfloat: 
    2.71828182845904523536028747135266249775724709369995957496696762772407663035354759457138217852516642742746639193200305992181741359662904357290033429526059563073813232862794349076323382988075319525101901157383418793070215408914993488416750924476146066808226480016847741185374234544243710753907774499206950

Numpived matrix of bigfloat: 
[[ 2.71828183  1.64872127  1.39561243  1.28402542]
 [ 1.64872127  1.39561243  1.28402542  1.22140276]
 [ 1.39561243  1.28402542  1.22140276  1.18136041]
 [ 1.28402542  1.22140276  1.18136041  1.15356499]]

Inverse of numpivized bigfloat matrix: 
[[    3.82106765   -30.68951855    61.37113204   -34.60880961]
 [  -30.68951855   385.05574805  -890.19682475   538.10687764]
 [   61.37113204  -890.19682475  2211.11911647 -1390.16166038]
 [  -34.60880961   538.10687764 -1390.16166038   893.29628089]]