numba：未找到用于签名的函数 Function(<built-in function getitem>) 的实现：
numba: No implementation of function Function(<built-in function getitem>) found for signature:
我很难在我的函数中实现 numba。
基本上，如果尚未添加新数据，我想连接到具有 22 列的数组。如果没有旧数据，新数据应该变成二维数组。
该函数在没有装饰器的情况下工作正常：
@jit(nopython=True)
def add(new,original=np.array([])):
duplicate=True
if original.size!=0:
for raw in original:
for ii in range(11,19):
if raw[ii]!=new[ii]:
duplicate=False
if duplicate==False:
res=np.zeros((original.shape[0]+1,22))
res[:original.shape[0]]=original
res[-1]=new
return res
else:
return original
else:
res=np.zeros((1,22))
res[0]=new
return res
此外，如果我删除代码的最后一部分：
else:
res=np.zeros((1,22))
res[0]=new
return res
它将与 njit 一起工作
因此，如果我忽略还没有旧数据的情况，一切都会好起来的。
仅供参考：我传入的数据是混合浮点数和 np.nan。
有什么想法吗？
提前致谢！
这是我的错误日志：
---------------------------------------------------------------------------
TypingError Traceback (most recent call last)
<ipython-input-255-d05a5f4ea944> in <module>()
19 return res
20 #add(a,np.array([b]))
---> 21 add(a)
2 frames
/usr/local/lib/python3.7/dist-packages/numba/core/dispatcher.py in _compile_for_args(self, *args, **kws)
413 e.patch_message(msg)
414
--> 415 error_rewrite(e, 'typing')
416 except errors.UnsupportedError as e:
417 # Something unsupported is present in the user code, add help info
/usr/local/lib/python3.7/dist-packages/numba/core/dispatcher.py in error_rewrite(e, issue_type)
356 raise e
357 else:
--> 358 reraise(type(e), e, None)
359
360 argtypes = []
/usr/local/lib/python3.7/dist-packages/numba/core/utils.py in reraise(tp, value, tb)
78 value = tp()
79 if value.__traceback__ is not tb:
---> 80 raise value.with_traceback(tb)
81 raise value
82
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
No implementation of function Function(<built-in function getitem>) found for signature:
>>> getitem(float64, int64)
There are 22 candidate implementations:
- Of which 22 did not match due to:
Overload of function 'getitem': File: <numerous>: Line N/A.
With argument(s): '(float64, int64)':
No match.
During: typing of intrinsic-call at <ipython-input-255-d05a5f4ea944> (7)
File "<ipython-input-255-d05a5f4ea944>", line 7:
def add(new,original=np.array([])):
<source elided>
for ii in range(11,19):
if raw[ii]!=new[ii]:
^
更新：
这是它应该如何工作。功能主要涵盖三种情况
新数据的示例输入（一维数组）：
array([9.0000000e+00, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00,
0.0000000e+00, nan, 5.7300000e-01, 9.2605450e-01,
9.3171725e-01, 9.2039175e-01, 9.3450000e-01, 1.6491636e+09,
1.6494228e+09, 1.6496928e+09, 1.6497504e+09, 9.2377000e-01,
9.3738000e-01, 9.3038000e-01, 9.3450000e-01, nan,
nan, nan])
原始数据的样本输入（二维数组）：
array([[4.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,
0.00000000e+00, nan, 5.23000000e-01, 8.31589755e-01,
8.34804877e-01, 8.28374632e-01, 8.36090000e-01, 1.64938320e+09,
1.64966400e+09, 1.64968920e+09, 1.64975760e+09, 8.30750000e-01,
8.38020000e-01, 8.34290000e-01, 8.36090000e-01, nan,
nan, nan]])
- 将添加新数据，没有原始数据
add(new)
Output:
array([[9.0000000e+00, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00,
0.0000000e+00, nan, 5.7300000e-01, 9.2605450e-01,
9.3171725e-01, 9.2039175e-01, 9.3450000e-01, 1.6491636e+09,
1.6494228e+09, 1.6496928e+09, 1.6497504e+09, 9.2377000e-01,
9.3738000e-01, 9.3038000e-01, 9.3450000e-01, nan,
nan, nan]])
- 将添加新数据，以前没有添加过且有原始数据
add(new,original)
Output:
array([[4.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,
0.00000000e+00, nan, 5.23000000e-01, 8.31589755e-01,
8.34804877e-01, 8.28374632e-01, 8.36090000e-01, 1.64938320e+09,
1.64966400e+09, 1.64968920e+09, 1.64975760e+09, 8.30750000e-01,
8.38020000e-01, 8.34290000e-01, 8.36090000e-01, nan,
nan, nan],
[9.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,
0.00000000e+00, nan, 5.73000000e-01, 9.26054500e-01,
9.31717250e-01, 9.20391750e-01, 9.34500000e-01, 1.64916360e+09,
1.64942280e+09, 1.64969280e+09, 1.64975040e+09, 9.23770000e-01,
9.37380000e-01, 9.30380000e-01, 9.34500000e-01, nan,
nan, nan]])
- 将添加之前已经添加的新数据
add(new,original)
Output:
array([[9.0000000e+00, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00,
0.0000000e+00, nan, 5.7300000e-01, 9.2605450e-01,
9.3171725e-01, 9.2039175e-01, 9.3450000e-01, 1.6491636e+09,
1.6494228e+09, 1.6496928e+09, 1.6497504e+09, 9.2377000e-01,
9.3738000e-01, 9.3038000e-01, 9.3450000e-01, nan,
nan, nan]])
主要问题是 Numba 假定 original 是一个 1D 数组，但事实并非如此。 pure-Python 代码之所以有效，是因为 解释器 它从不执行循环体 for raw in original 但 Numba 需要在执行之前编译所有代码。您可以使用以下函数原型解决此问题：
def add(new,original=np.array([[]])): # Note the `[[]]` instead of `[]`
由此，Numba 可以正确推断出 original 数组是二维数组。
请注意，指定 Numpy 数组和输入的维度和类型是避免此类错误和偷偷摸摸的错误（例如，由于 integer/float 截断）的好方法。
我很难在我的函数中实现 numba。
基本上，如果尚未添加新数据，我想连接到具有 22 列的数组。如果没有旧数据，新数据应该变成二维数组。
该函数在没有装饰器的情况下工作正常：
@jit(nopython=True)
def add(new,original=np.array([])):
duplicate=True
if original.size!=0:
for raw in original:
for ii in range(11,19):
if raw[ii]!=new[ii]:
duplicate=False
if duplicate==False:
res=np.zeros((original.shape[0]+1,22))
res[:original.shape[0]]=original
res[-1]=new
return res
else:
return original
else:
res=np.zeros((1,22))
res[0]=new
return res
此外，如果我删除代码的最后一部分：
else:
res=np.zeros((1,22))
res[0]=new
return res
它将与 njit 一起工作
因此，如果我忽略还没有旧数据的情况，一切都会好起来的。
仅供参考：我传入的数据是混合浮点数和 np.nan。
有什么想法吗？ 提前致谢！
这是我的错误日志：
---------------------------------------------------------------------------
TypingError Traceback (most recent call last)
<ipython-input-255-d05a5f4ea944> in <module>()
19 return res
20 #add(a,np.array([b]))
---> 21 add(a)
2 frames
/usr/local/lib/python3.7/dist-packages/numba/core/dispatcher.py in _compile_for_args(self, *args, **kws)
413 e.patch_message(msg)
414
--> 415 error_rewrite(e, 'typing')
416 except errors.UnsupportedError as e:
417 # Something unsupported is present in the user code, add help info
/usr/local/lib/python3.7/dist-packages/numba/core/dispatcher.py in error_rewrite(e, issue_type)
356 raise e
357 else:
--> 358 reraise(type(e), e, None)
359
360 argtypes = []
/usr/local/lib/python3.7/dist-packages/numba/core/utils.py in reraise(tp, value, tb)
78 value = tp()
79 if value.__traceback__ is not tb:
---> 80 raise value.with_traceback(tb)
81 raise value
82
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
No implementation of function Function(<built-in function getitem>) found for signature:
>>> getitem(float64, int64)
There are 22 candidate implementations:
- Of which 22 did not match due to:
Overload of function 'getitem': File: <numerous>: Line N/A.
With argument(s): '(float64, int64)':
No match.
During: typing of intrinsic-call at <ipython-input-255-d05a5f4ea944> (7)
File "<ipython-input-255-d05a5f4ea944>", line 7:
def add(new,original=np.array([])):
<source elided>
for ii in range(11,19):
if raw[ii]!=new[ii]:
^
更新： 这是它应该如何工作。功能主要涵盖三种情况
新数据的示例输入（一维数组）：
array([9.0000000e+00, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00,
0.0000000e+00, nan, 5.7300000e-01, 9.2605450e-01,
9.3171725e-01, 9.2039175e-01, 9.3450000e-01, 1.6491636e+09,
1.6494228e+09, 1.6496928e+09, 1.6497504e+09, 9.2377000e-01,
9.3738000e-01, 9.3038000e-01, 9.3450000e-01, nan,
nan, nan])
原始数据的样本输入（二维数组）：
array([[4.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,
0.00000000e+00, nan, 5.23000000e-01, 8.31589755e-01,
8.34804877e-01, 8.28374632e-01, 8.36090000e-01, 1.64938320e+09,
1.64966400e+09, 1.64968920e+09, 1.64975760e+09, 8.30750000e-01,
8.38020000e-01, 8.34290000e-01, 8.36090000e-01, nan,
nan, nan]])
- 将添加新数据，没有原始数据
add(new)
Output:
array([[9.0000000e+00, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00,
0.0000000e+00, nan, 5.7300000e-01, 9.2605450e-01,
9.3171725e-01, 9.2039175e-01, 9.3450000e-01, 1.6491636e+09,
1.6494228e+09, 1.6496928e+09, 1.6497504e+09, 9.2377000e-01,
9.3738000e-01, 9.3038000e-01, 9.3450000e-01, nan,
nan, nan]])
- 将添加新数据，以前没有添加过且有原始数据
add(new,original)
Output:
array([[4.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,
0.00000000e+00, nan, 5.23000000e-01, 8.31589755e-01,
8.34804877e-01, 8.28374632e-01, 8.36090000e-01, 1.64938320e+09,
1.64966400e+09, 1.64968920e+09, 1.64975760e+09, 8.30750000e-01,
8.38020000e-01, 8.34290000e-01, 8.36090000e-01, nan,
nan, nan],
[9.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,
0.00000000e+00, nan, 5.73000000e-01, 9.26054500e-01,
9.31717250e-01, 9.20391750e-01, 9.34500000e-01, 1.64916360e+09,
1.64942280e+09, 1.64969280e+09, 1.64975040e+09, 9.23770000e-01,
9.37380000e-01, 9.30380000e-01, 9.34500000e-01, nan,
nan, nan]])
- 将添加之前已经添加的新数据
add(new,original)
Output:
array([[9.0000000e+00, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00,
0.0000000e+00, nan, 5.7300000e-01, 9.2605450e-01,
9.3171725e-01, 9.2039175e-01, 9.3450000e-01, 1.6491636e+09,
1.6494228e+09, 1.6496928e+09, 1.6497504e+09, 9.2377000e-01,
9.3738000e-01, 9.3038000e-01, 9.3450000e-01, nan,
nan, nan]])
主要问题是 Numba 假定 original 是一个 1D 数组，但事实并非如此。 pure-Python 代码之所以有效，是因为 解释器 它从不执行循环体 for raw in original 但 Numba 需要在执行之前编译所有代码。您可以使用以下函数原型解决此问题：
def add(new,original=np.array([[]])): # Note the `[[]]` instead of `[]`
由此，Numba 可以正确推断出 original 数组是二维数组。
请注意，指定 Numpy 数组和输入的维度和类型是避免此类错误和偷偷摸摸的错误（例如，由于 integer/float 截断）的好方法。