I'm sort of newbie in numpy so I'm sorry if this question was already asked. I'm looking for a vectorization solution which enable to run multiple cumsum of different size within a one dimension numpy array.
my_vector=np.array([1,2,3,4,5])
size_of_groups=np.array([3,2])
I would like something like
np.cumsum.group(my_vector,size_of_groups)
[1,3,6,4,9]
I do not want a solution with loops. Either numpy functions or numpy operations.
Not sure about numpy, but pandas can do this pretty easily with a groupby + cumsum:
import pandas as pd
s = pd.Series(my_vector)
s.groupby(s.index.isin(size_of_groups.cumsum()).cumsum()).cumsum()
0 1
1 3
2 6
3 4
4 9
dtype: int64
Here's a vectorized solution -
def intervaled_cumsum(ar, sizes):
# Make a copy to be used as output array
out = ar.copy()
# Get cumumlative values of array
arc = ar.cumsum()
# Get cumsumed indices to be used to place differentiated values into
# input array's copy
idx = sizes.cumsum()
# Place differentiated values that when cumumlatively summed later on would
# give us the desired intervaled cumsum
out[idx[0]] = ar[idx[0]] - arc[idx[0]-1]
out[idx[1:-1]] = ar[idx[1:-1]] - np.diff(arc[idx[:-1]-1])
return out.cumsum()
Sample run -
In [114]: ar = np.array([1,2,3,4,5,6,7,8,9,10,11,12])
...: sizes = np.array([3,2,2,3,2])
In [115]: intervaled_cumsum(ar, sizes)
Out[115]: array([ 1, 3, 6, 4, 9, 6, 13, 8, 17, 27, 11, 23])
Other approach(es) -
# @cᴏʟᴅsᴘᴇᴇᴅ's solution
import pandas as pd
def pandas_soln(my_vector, sizes):
s = pd.Series(my_vector)
return s.groupby(s.index.isin(sizes.cumsum()).cumsum()).cumsum().values
The given sample used two intervals of lengths 2 and 3 Keeping that and simply giving it more number of groups for timing purpose.
Timings -
In [146]: N = 10000 # number of groups
...: np.random.seed(0)
...: sizes = np.random.randint(2,4,(N))
...: ar = np.random.randint(0,N,sizes.sum())
In [147]: %timeit intervaled_cumsum(ar, sizes)
...: %timeit pandas_soln(ar, sizes)
10000 loops, best of 3: 178 µs per loop
1000 loops, best of 3: 1.82 ms per loop
In [148]: N = 100000 # number of groups
...: np.random.seed(0)
...: sizes = np.random.randint(2,4,(N))
...: ar = np.random.randint(0,N,sizes.sum())
In [149]: %timeit intervaled_cumsum(ar, sizes)
...: %timeit pandas_soln(ar, sizes)
100 loops, best of 3: 3.91 ms per loop
100 loops, best of 3: 17.3 ms per loop
In [150]: N = 1000000 # number of groups
...: np.random.seed(0)
...: sizes = np.random.randint(2,4,(N))
...: ar = np.random.randint(0,N,sizes.sum())
In [151]: %timeit intervaled_cumsum(ar, sizes)
...: %timeit pandas_soln(ar, sizes)
10 loops, best of 3: 31.6 ms per loop
1 loop, best of 3: 357 ms per loop
Here is an unconventional solution. Not very fast, though. (Even a bit slower than pandas).
>>> from scipy import linalg
>>>
>>> N = len(my_vector)
>>> D = np.repeat((*zip((1,-1)),), N, axis=1)
>>> D[1, np.cumsum(size_of_groups) - 1] = 0
>>>
>>> linalg.solve_banded((1, 0), D, my_vector)
array([1., 3., 6., 4., 9.])
