我需要数字化一些值,以便返回的索引是“floor”或“ceiling”bin。
例如,用于
bins = numpy.array([0.0, 0.5, 1.0, 1.5, 2.0])
和一个价值
0.2
我希望指数是
0
,作为一个值
0.26
返回的索引应该是
1
,
等等。
我有以下丑陋的功能来做我想做的事:
import numpy
def get_bin_index(value, bins):
bin_diff = bins[1]-bins[0]
index = numpy.digitize(value, bins)
if bins[index] - value > bin_diff/2.0:
index -= 1
return index
有没有什么简洁(读得更好/更有效)的方法可以做到这一点?
编辑:包括计时值(只是满足我的好奇心!)
In [1]: def get_bin_index(value, bins):
...: bin_diff = bins[1]-bins[0]
...: index = numpy.digitize(value, bins)
...: if bins[index] - value > bin_diff/2.0:
...: index -= 1
...: return index
...:
In [2]: def get_bin_index_c(value, bins):
...: return numpy.rint((value-bins[0])/(bins[1]-bins[0]))
...:
In [3]: def get_bin_index_mid_digitized(value, bins):
...: return numpy.digitize(0.6, (bins[1:] + bins[:-1])/2.0)
...:
In [4]: bin_halfs = numpy.array([0.0, 0.5, 1.0, 1.5, 2.0])
In [5]: %timeit get_bin_index(0.9, bin_halfs)
The slowest run took 5.71 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 4.93 µs per loop
In [6]: %timeit get_bin_index_c(0.9, bin_halfs)
The slowest run took 14.60 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 2.34 µs per loop
In [7]: %timeit get_bin_index_mid_digitized(0.9, bin_halfs)
The slowest run took 4.09 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 8.37 µs per loop
