问Python - LinAlgError: SVD没有在线性最小二乘中收敛-数据中没有Nans或infs

EN

a = np.array([0.5  , 0.505, 0.51 , 0.515, 0.52 , 0.525, 0.53 , 0.535, 0.54 ,
       0.545, 0.55 , 0.555, 0.56 , 0.565, 0.57 , 0.575, 0.58 , 0.585,
       0.59 , 0.595])
b = np.array([ 49.62358846,  50.21487603,  53.03564434,  51.68435625,
        53.25301205,  54.04002965,  54.97835498,  52.83363803,
        59.1954023 ,  59.82532751,  60.33057851,  56.16438356,
        53.33333333,  72.22222222,  51.72413793,  41.66666667,
        33.33333333,  44.44444444,  25.        , 100.        ])

np.polyfit(a, b, 1)

*LinAlgError                               Traceback (most recent call last)
<ipython-input-274-d8db33e4c692> in <module>
----> 1 np.polyfit(a, b, 1)

<__array_function__ internals> in polyfit(*args, **kwargs)

C:\ProgramData\other\lib\site-packages\numpy\lib\polynomial.py in polyfit(x, y, deg, rcond, full, w, cov)
    627     scale = NX.sqrt((lhs*lhs).sum(axis=0))
    628     lhs /= scale
--> 629     c, resids, rank, s = lstsq(lhs, rhs, rcond)
    630     c = (c.T/scale).T  # broadcast scale coefficients
    631 

<__array_function__ internals> in lstsq(*args, **kwargs)

C:\ProgramData\other\lib\site-packages\numpy\linalg\linalg.py in lstsq(a, b, rcond)
   2304         # lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis
   2305         b = zeros(b.shape[:-2] + (m, n_rhs + 1), dtype=b.dtype)
-> 2306     x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)
   2307     if m == 0:
   2308         x[...] = 0

C:\ProgramData\other\lib\site-packages\numpy\linalg\linalg.py in _raise_linalgerror_lstsq(err, flag)
     98 
     99 def _raise_linalgerror_lstsq(err, flag):
--> 100     raise LinAlgError("SVD did not converge in Linear Least Squares")
    101 
    102 def get_linalg_error_extobj(callback):

LinAlgError: SVD did not converge in Linear Least Squares*