Is there a way to make numpy.argmin() as fast as min()?
arrays, min, numpy, python
Solution
In [1]: import numpy as np
In [2]: a = np.random.rand(3000, 16000)
In [3]: %timeit a.min(axis=0)
1 loops, best of 3: 421 ms per loop
In [4]: %timeit a.argmin(axis=0)
1 loops, best of 3: 1.95 s per loop
In [5]: %timeit a.min(axis=1)
1 loops, best of 3: 302 ms per loop
In [6]: %timeit a.argmin(axis=1)
1 loops, best of 3: 303 ms per loop
In [7]: %timeit a.T.argmin(axis=1)
1 loops, best of 3: 1.78 s per loop
In [8]: %timeit np.asfortranarray(a).argmin(axis=0)
1 loops, best of 3: 1.97 s per loop
In [9]: b = np.asfortranarray(a)
In [10]: %timeit b.argmin(axis=0)
1 loops, best of 3: 329 ms per loop
Maybe `min` is smart enough to do its job sequentially over the array (hence with cache locality), and `argmin` is jumping around the array (causing a lot of cache misses)?
Anyway, if you're willing to keep `randvals` as a Fortran-ordered array from the start, it'll be faster, though copying into Fortran-ordered doesn't help.
Problem
I'm trying to find the minimum array indices along one dimension of a very large 2D numpy array. I'm finding that this is very slow (already tried speeding it up with bottleneck, which was only a minimal improvement). However, taking the straight minimum appears to be an order of magnitude faster: ``` import numpy as np import time randvals = np.random.rand(3000,160000) start = time.time() minval = randvals.min(axis=0) print "Took {0:.2f} seconds to compute min".format(time.time()-start) start = time.time() minindex = np.argmin(randvals,axis=0) print "Took {0:.2f} seconds to compute argmin".format(time.time()-start) ``` On my machine this outputs: ``` Took 0.83 seconds to compute min Took 9.58 seconds to compute argmin ``` Is there any reason why argmin is so much slower? Is there any way to speed it up to comparable to min?