在python中将类型从BigFloat转换为Float
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
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]]