numpy:在排序列表中,找到每个唯一值的第一个和最后一个索引 [英] numpy: in a sorted list, find the first and the last index for each unique value
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问题描述
具有排序列表,任何人如何找到(使用numpy)每个唯一值的第一个和最后一个索引?
Having a sorted list, how can anyone find (using numpy) the first and the last index for each unique value?
示例:
初始排序列表:
>>> import numpy as np
>>> initial_list = np.array([1, 3, 2, 3, 0, 3, 0, 1, 0])
>>> initial_list.sort()
>>> initial_list
array([0, 0, 0, 1, 1, 2, 3, 3, 3])
其结果将是:
第一:[0,0,0,3,3,5,6,6,6]
first: [ 0, 0, 0, 3, 3, 5, 6, 6, 6 ]
最后:[2,2,2,4,4,5,8,8,8]
last: [ 2, 2, 2, 4, 4, 5, 8, 8, 8 ]
提前谢谢
推荐答案
这是一种利用输入数据的排序特性的方法,该方法利用了非常有效的NumPy array-slicing
和其他NumPy函数-
Here's one approach leveraging the sorted nature of input data, making use of the very efficient NumPy array-slicing
and other NumPy functions -
def start_stop_arr(initial_list):
a = np.asarray(initial_list)
mask = np.concatenate(([True], a[1:] != a[:-1], [True]))
idx = np.flatnonzero(mask)
l = np.diff(idx)
start = np.repeat(idx[:-1], l)
stop = np.repeat(idx[1:]-1, l)
return start, stop
级联重复可以进一步提高性能-
Further performance boost is possible with concatenated repetitions -
def start_stop_arr_concat_repeat(initial_list):
a = np.asarray(initial_list)
mask = np.concatenate(([True], a[1:] != a[:-1], [True]))
idx = np.flatnonzero(mask)
l = np.diff(idx)
idx2 = np.concatenate((idx[:-1,None], (idx[1:,None]-1)),axis=1)
ss = np.repeat(idx2, l, axis=0)
return ss[:,0], ss[:,1]
样品运行-
In [38]: initial_list
Out[38]: array([0, 0, 0, 1, 1, 2, 3, 3, 3])
In [39]: start_stop_arr(initial_list)
Out[39]: (array([0, 0, 0, 3, 3, 5, 6, 6, 6]), array([2, 2, 2, 4, 4, 5, 8, 8, 8]))
运行时测试-
其他方法-
# @Mohammed Elmahgiubi's soln
def reversed_app(initial_list): # input expected is a list
reversed_initial_list = list(reversed(initial_list))
first = [initial_list.index(i) for i in initial_list]
last = list(reversed([(len(initial_list) -
(reversed_initial_list.index(i) + 1))
for i in reversed_initial_list]))
return first, last
def unique_app(a): # @B. M.'s soln
_,ind1,inv1,cou1 = np.unique(a, return_index=True, return_inverse=True,
return_counts=True)
return ind1[inv1],(ind1+cou1-1)[inv1]
时间-
案例1:较小的数据集
In [295]: initial_list = np.random.randint(0,1000,(10000))
...: initial_list.sort()
In [296]: input_list = initial_list.tolist()
In [297]: %timeit reversed_app(input_list)
1 loop, best of 3: 789 ms per loop
In [298]: %timeit unique_app(initial_list)
1000 loops, best of 3: 353 µs per loop
In [299]: %timeit start_stop_arr(initial_list)
10000 loops, best of 3: 96.3 µs per loop
案例2:更大的数据集
In [438]: initial_list = np.random.randint(0,100000,(1000000))
...: initial_list.sort()
In [439]: %timeit unique_app(initial_list) # @B. M.'s soln
10 loops, best of 3: 53 ms per loop
In [440]: %timeit start_stop_arr(initial_list)
100 loops, best of 3: 9.64 ms per loop
In [441]: %timeit start_stop_arr_concat_repeat(initial_list)
100 loops, best of 3: 6.76 ms per loop
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