如何精确小数纽比.pow在Python中

Question

我有如下代码：

pows2nag = np.asarray([ np.power(2.0,x) for x in range(-65,0) ][::-1])

它将产生如下结果：

[  5.00000000e-01   2.50000000e-01   1.25000000e-01   6.25000000e-02
   3.12500000e-02   1.56250000e-02   7.81250000e-03   3.90625000e-03
   1.95312500e-03   9.76562500e-04   4.88281250e-04   2.44140625e-04
   1.22070312e-04   6.10351562e-05   3.05175781e-05   1.52587891e-05
   7.62939453e-06   3.81469727e-06   1.90734863e-06   9.53674316e-07
   4.76837158e-07   2.38418579e-07   1.19209290e-07   5.96046448e-08
   2.98023224e-08   1.49011612e-08   7.45058060e-09   3.72529030e-09
   1.86264515e-09   9.31322575e-10   4.65661287e-10   2.32830644e-10
   1.16415322e-10   5.82076609e-11   2.91038305e-11   1.45519152e-11
   7.27595761e-12   3.63797881e-12   1.81898940e-12   9.09494702e-13
   4.54747351e-13   2.27373675e-13   1.13686838e-13   5.68434189e-14
   2.84217094e-14   1.42108547e-14   7.10542736e-15   3.55271368e-15
   1.77635684e-15   8.88178420e-16   4.44089210e-16   2.22044605e-16
   1.11022302e-16   5.55111512e-17   2.77555756e-17   1.38777878e-17
   6.93889390e-18   3.46944695e-18   1.73472348e-18   8.67361738e-19
   4.33680869e-19   2.16840434e-19   1.08420217e-19   5.42101086e-20
   2.71050543e-20]

我的问题是小数精度没有我想要的那么精确。 例如，2.44140625除以2应该等于1.220703125，但是我得到的是1.22070312。在

我怎么做numpy.力量生成更长的十进制数？在

Answer 1

2.44140625/2确实给出了1.220703125。实际值与控制台中显示的值之间存在差异。看看这个：

np.pi
Out[992]: 3.141592653589793

np.array([np.pi])
Out[993]: array([ 3.14159265])

np.array([np.pi])[0]
Out[994]: 3.1415926535897931

Answer 2

使用np.set_printoptions调整打印数组的精度。在

>>> np.set_printoptions(precision=15)
>>> print(pows2nag)
[  5.000000000000000e-01   2.500000000000000e-01   1.250000000000000e-01
   6.250000000000000e-02   3.125000000000000e-02   1.562500000000000e-02
   7.812500000000000e-03   3.906250000000000e-03   1.953125000000000e-03
   9.765625000000000e-04   4.882812500000000e-04   2.441406250000000e-04
   1.220703125000000e-04   6.103515625000000e-05   3.051757812500000e-05
   1.525878906250000e-05   7.629394531250000e-06   3.814697265625000e-06
   1.907348632812500e-06   9.536743164062500e-07   4.768371582031250e-07
   2.384185791015625e-07   1.192092895507812e-07   5.960464477539062e-08
   2.980232238769531e-08   1.490116119384766e-08   7.450580596923828e-09
   3.725290298461914e-09   1.862645149230957e-09   9.313225746154785e-10
   4.656612873077393e-10   2.328306436538696e-10   1.164153218269348e-10
   5.820766091346741e-11   2.910383045673370e-11   1.455191522836685e-11
   7.275957614183426e-12   3.637978807091713e-12   1.818989403545856e-12
   9.094947017729282e-13   4.547473508864641e-13   2.273736754432321e-13
   1.136868377216160e-13   5.684341886080801e-14   2.842170943040401e-14
   1.421085471520200e-14   7.105427357601002e-15   3.552713678800501e-15
   1.776356839400250e-15   8.881784197001252e-16   4.440892098500626e-16
   2.220446049250313e-16   1.110223024625157e-16   5.551115123125783e-17
   2.775557561562891e-17   1.387778780781446e-17   6.938893903907228e-18
   3.469446951953614e-18   1.734723475976807e-18   8.673617379884035e-19
   4.336808689942018e-19   2.168404344971009e-19   1.084202172485504e-19
   5.421010862427522e-20   2.710505431213761e-20]