为什么numpy.apply_along_axis给出相同的结果，而不管轴是什么？

如何解决为什么numpy.apply_along_axis给出相同的结果，而不管轴是什么？

我有一个包含分数的numpy数组的numpy数组：

import numpy as np

In [1]: scores                                                                 
Out[1]: 
array([[-1.354,0.,0.6921,...,-0.1972,-0.0454,-0.1233],[-1.6837,0.7019,-0.1534,0.0536,-0.0269],[-1.4549,-0.346,0.7698,0.385,0.3527,0.0277],[-0.7322,1.7791,1.5935,0.515,1.0949,0.3007],[-0.3222,1.2375,1.6012,0.4675,0.5924,0.1081],[-1.4317,0.7675,-0.3711,-0.2111,-0.2084]])

In [2]: scores.shape                                                           
Out[2]: (7,1324)

我应用了一个函数来沿轴0标准化-1和1之间的数据：

normlized_scores = np.apply_along_axis(lambda x,mini=np.amin(scores),maxi=np.amax(scores): 2*((x-mini)/(maxi-mini))-1,scores)

我得到了预期的结果：

In [3]: normlized_scores                                                                                                                                                                 
Out[3]: 
array([[-0.13278178,0.26165992,0.46327963,0.20421243,0.24843418,0.22574067],[-0.22882862,0.46613453,0.21697206,0.27727445,0.25382352],[-0.16217555,0.16086463,0.48591488,0.37381653,0.36440703,0.26972937],[ 0.04835844,0.77993999,0.72587176,0.41168759,0.58062167,0.3492586 ],[ 0.16779794,0.62216331,0.72811489,0.39785009,0.43423544,0.29315116],[-0.15541702,0.48524485,0.1535526,0.20016314,0.20094969]])

我应用相同的函数沿轴1标准化-1和1之间的数据：

normlized_scores = np.apply_along_axis(lambda x,1,scores)

我也得到了预期的结果：

In [4]: normlized_scores                                                                                                                                                                
Out[4]: 
array([[-0.13278178,0.20094969]])

我不知道这是怎么可能的...我想像这样的轴工作方式：

因此，例如np.sum函数的工作方式如图像中提到的那样：

In [5]: np.sum(scores) # axis=None                                                                                                                                                                 
Out[5]: -6527.8252

In [6]: np.apply_along_axis(np.sum,scores) # axis=0                                                                                                                                                          
Out[6]: array([-7.7028,4.2714,7.6119,0.8379,2.1619,0.1256]) # shape: (1324,)

In [7]: np.apply_along_axis(np.sum,scores) # axis=1                                                                                                                                                          
Out[7]: 
array([ -855.0827,-1011.2521,-954.4777,-948.0853,-938.6737,-948.9815,-871.2722]) # shape: (7,)

为什么我的函数不受axis参数的影响？我会错过什么吗？

编辑（请参见注释）：根据@Divyessh，归一化主要不取决于轴。但是为什么它返回具有相同形状的数组？