条件与平均numpy的

Question

给定的2x3阵列，我要计算在axis=0的平均水平，但只考虑那些值大于0

所以给出的阵列

[ [1,0], 
    [0,0], 
    [1,0] ] 

我想要的输出要

# 1, 0, 1 filtered for > 0 gives 1, 1, average = (1+1)/2 = 1 
# 0, 0, 0 filtered for > 0 gives 0, 0, 0, average = 0  
[1 0] 

我当前的代码是

import numpy as np 

frame = np.array([ [1,0], 
        [0,0], 
        [1,0] ]) 

weights=np.array(frame)>0 

print("weights:") 
print(weights) 

print("average without weights:") 
print((np.average(frame, axis=0))) 

print("average with weights:") 
print((np.average(frame, axis=0, weights=weights))) 

这给了我

weights: 
[[ True False] 
[False False] 
[ True False]] 
average without weights: 
[ 0.66666667 0.  ] 
average with weights: 
Traceback (most recent call last): 
File "C:\Users\myuser\project\test.py", line 123, in <module> 
print((np.average(frame, axis=0, weights=weights))) 
File "C:\Users\myuser\Miniconda3\envs\myenv\lib\site-packages\numpy\lib\function_base.py", line 1140, in average 
"Weights sum to zero, can't be normalized") 
ZeroDivisionError: Weights sum to zero, can't be normalized 

我不明白这个错误。我在做什么错了，我怎么能得到沿axis=0沿大于零的所有值的平均值？谢谢！

Answer 1

您可以获得大于零的掩码，并使用它沿第一个轴执行元素复用和减法。最后，除以沿着第一轴的屏蔽元素的数量以获得平均值。

因此，一个解决办法是 -

mask = a > 0 # Input array : a 
out = np.einsum('i...,i...->...',a,mask)/mask.sum(0) 

采样运行 -

In [52]: a 
Out[52]: 
array([[ 3, -3, 3], 
     [ 2, 2, 0], 
     [ 0, -3, 1], 
     [ 0, 1, 1]]) 

In [53]: mask = a > 0 

In [56]: np.einsum('i...,i...->...',a,mask) # summations of > 0s 
Out[56]: array([5, 3, 5]) 

In [57]: np.einsum('i...,i...->...',a,mask)/mask.sum(0) # avg values of >0s 
Out[57]: array([ 2.5  , 1.5  , 1.66666667]) 

要占全部零列，看来我们期待0作为结果。所以，我们可以用np.where做选择，就像这样 -

In [61]: a[:,-1] = 0 

In [62]: a 
Out[62]: 
array([[ 3, -3, 0], 
     [ 2, 2, 0], 
     [ 0, -3, 0], 
     [ 0, 1, 0]]) 

In [63]: mask = a > 0 

In [65]: np.where(mask.any(0), np.einsum('i...,i...->...',a,mask)/mask.sum(0), 0) 
__main__:1: RuntimeWarning: invalid value encountered in true_divide 
Out[65]: array([ 2.5, 1.5, 0. ]) 

只是忽略警告那里。

如果你觉得偏执的警告，使用masking -

mask = a > 0 
vm = mask.any(0) # valid mask 
out = np.zeros(a.shape[1]) 
out[vm] = np.einsum('ij,ij->j',a[:,vm],mask[:,vm])/mask.sum(0)[vm]