Question

我有一个包含正值和负值的numpy数组，我想调整负数条目，使得总和不是负数，从最负的条目开始。最大调整是使负条目为零。我有一个使用循环的实现，有没有办法使用numpy数组方法？这是我的代码：

initial_values = np.asarray([50,-200,-180,110])
sorted_index = np.argsort(initial_values)

final_values = initial_values
for i, entry in enumerate(final_values[sorted_index]):
    ss = final_values.sum()
    if ss >= 0:
        break
    adjustment = max(entry, ss)
    final_values[sorted_index[i]] -= adjustment

print final_values

起始数组是[50，-200，-180,110]，这种情况下的答案是[50,0，-160,110]，所以最负的条目设置为零，然后是下一个最负的条目调整条目以使总和为零。

有没有人有一个更简单，更快速的基于numpy的解决方案？

Answer 1

这是一种矢量化方法 -

# Get a copy of input as the output
out = initial_values.copy()

# Get sorted indices
sorted_index = np.argsort(out)

# Mask of elements that would be made zero for sure and zero them
mask = out.sum() < out[sorted_index].cumsum()
out[sorted_index[mask]] = 0

# There might be one element left to make the sum absolutely zero. 
# Make it less negative to make the absolute sum zero.
out[sorted_index[np.where(mask)[0][-1]+1]] -= out.sum()

示例运行 -

功能定义 -

In [155]: def vectorized(initial_values):
     ...:   out = initial_values.copy()
     ...:   sorted_index = np.argsort(out)
     ...:   mask = out.sum() < out[sorted_index].cumsum()
     ...:   out[sorted_index[mask]] = 0
     ...:   out[sorted_index[np.where(mask)[0][-1]+1]] -= out.sum()
     ...:   return out
     ...: 
     ...: def org_app(initial_values):  
     ...:   final_values = initial_values.copy()
     ...:   sorted_index = np.argsort(initial_values)
     ...:   for i, entry in enumerate(final_values[sorted_index]):
     ...:       ss = final_values.sum()
     ...:       if ss >= 0:
     ...:           break
     ...:       adjustment = max(entry, ss)
     ...:       final_values[sorted_index[i]] -= adjustment
     ...:   return final_values
     ...:

案例＃1：

In [156]: initial_values
Out[156]: array([  50, -200, -180,  110])

In [157]: vectorized(initial_values)
Out[157]: array([  50,    0, -160,  110])

In [158]: org_app(initial_values)
Out[158]: array([  50,    0, -160,  110])

案例＃2：

In [163]: initial_values
Out[163]: array([ 50, -20, -14, -22, -15,   6, -21, -19, -17,   4,   5, -56])

In [164]: vectorized(initial_values)
Out[164]: array([ 50,   0, -14,   0, -15,   6,   0, -19, -17,   4,   5,   0])

In [165]: org_app(initial_values)
Out[165]: array([ 50,   0, -14,   0, -15,   6,   0, -19, -17,   4,   5,   0])

运行时测试 -

In [177]: initial_values = np.random.randint(-100,20,(50000))

In [178]: np.array_equal(vectorized(initial_values),org_app(initial_values))
Out[178]: True

In [179]: %timeit org_app(initial_values)
1 loops, best of 3: 2.08 s per loop

In [180]: %timeit vectorized(initial_values)
100 loops, best of 3: 5.7 ms per loop

这是先前提出的方法的稍微改进的（较少的代码和更好的运行时）版本 -

# Get a copy of input as the output
out = initial_values.copy()

# Get sorted indices
sorted_index = np.argsort(out)

# Last index in sorted indexed indices for setting elements in input array to 0's
idx = np.where(out.sum() < out[sorted_index].cumsum())[0][-1]

# Set until idx indexed into sorted_index in turn indexed into input array t0 0's
out[sorted_index[:idx+1]] = 0

# There might be one element left to make the sum absolutely zero. 
# Make it less negative to make the absolute sum zero.
out[sorted_index[idx+1]] -= out.sum()

运行时测试 -

In [18]: initial_values = np.random.randint(-100,20,(50000))

In [19]: %timeit vectorized(initial_values)
100 loops, best of 3: 5.58 ms per loop

In [20]: %timeit vectorized_v2(initial_values) # improved version
100 loops, best of 3: 5.4 ms per loop

通过调整负数条目来限制numpy数组中的条目总和

1 个答案: