如何让80位浮点运算在numpy中工作

Question

在numpy(或一般的python(中，我想利用Intel x86 FPU本机支持80位长的双数据类型这一事实。我怎么能那样做。在我的机器上(intel core i7，macOS Catalina，python 3.8.1，numpy 1.19.1(，以下尝试似乎失败了，因为额外的数字似乎没有保留：

>>> scalar = np.array([1.4756563577476488347],dtype=np.float64)
... with np.printoptions(precision=100,suppress=False):
...     print(scalar)
[1.475656357747649]

>>> scalar = np.array([1.4756563577476488347],dtype=np.float128)
... with np.printoptions(precision=100,suppress=False):
...     print(scalar)
[1.4756563577476489169]
>>> scalar = np.array([1.4756563577476488347],dtype=np.longfloat)
... with np.printoptions(precision=100,suppress=False):
...     print(scalar)
[1.4756563577476489169]

这似乎很奇怪，因为数据类型似乎是我认为的(64位对80位(：

print(np.finfo(np.float64))
Machine parameters for float64
---------------------------------------------------------------
precision =  15   resolution = 1.0000000000000001e-15
machep =    -52   eps =        2.2204460492503131e-16
negep =     -53   epsneg =     1.1102230246251565e-16
minexp =  -1022   tiny =       2.2250738585072014e-308
maxexp =   1024   max =        1.7976931348623157e+308
nexp =       11   min =        -max
---------------------------------------------------------------
print(np.finfo(np.float128))
Machine parameters for float128
---------------------------------------------------------------
precision =  18   resolution = 1.0000000000000000715e-18
machep =    -63   eps =        1.084202172485504434e-19
negep =     -64   epsneg =     5.42101086242752217e-20
minexp = -16382   tiny =       3.3621031431120935063e-4932
maxexp =  16384   max =        1.189731495357231765e+4932
nexp =       15   min =        -max
---------------------------------------------------------------

它与解析输入数字的能力有关吗？

Answer 1

问题是Python只使用64位浮点，并且您正在将Python对象传递给np.array。

试试这个：

In [26]: scalar = np.array(['1.4756563577476488347'], dtype=np.float128)                                    
In [27]: with np.printoptions(precision=100, suppress=False): 
...:     print(scalar) 
...:                                                                                                   
[1.4756563577476488347]

通过使用字符串作为文本，创建float128对象的代码现在是NumPy，这将保留值的精度。