I see.
Should functionality like this be included in numpy?
Jon.
On Tue, Mar 8, 2011 at 3:39 PM, josef.p...@gmail.com wrote:
On Tue, Mar 8, 2011 at 3:03 PM, Jonathan Taylor
jonathan.tay...@utoronto.ca wrote:
I am wanting to use an array b to index into an array x with dimension
bigger by 1 where the element of b indicates what value to extract
along a certain direction. For example, b = x.argmin(axis=1).
Perhaps I want to use b to create x.min(axis=1) but also to index
perhaps another array of the same size.
I had a difficult time finding a way to do this with np.take easily
and even with fancy indexing the resulting line is very complicated:
In [322]: x.shape
Out[322]: (2, 3, 4)
In [323]: x.min(axis=1)
Out[323]:
array([[ 2, 1, 7, 4],
[ 8, 0, 15, 12]])
In [324]: x[np.arange(x.shape[0])[:,np.newaxis,np.newaxis],
idx[:,np.newaxis,:], np.arange(x.shape[2])]
Out[324]:
array([[[ 2, 1, 7, 4]],
[[ 8, 0, 15, 12]]])
In any case I wrote myself my own function for doing this (below) and
am wondering if this is the best way to do this or if there is
something else in numpy that I should be using? -- I figure that this
is a relatively common usecase.
Thanks,
Jon.
def mytake(A, b, axis):
assert len(A.shape) == len(b.shape)+1
idx = []
for i in range(len(A.shape)):
if i == axis:
temp = b.copy()
shapey = list(temp.shape)
shapey.insert(i,1)
else:
temp = np.arange(A.shape[i])
shapey = [1]*len(b.shape)
shapey.insert(i,A.shape[i])
shapey = tuple(shapey)
temp = temp.reshape(shapey)
idx += [temp]
return A[tuple(idx)].squeeze()
In [319]: util.mytake(x,x.argmin(axis=1), 1)
Out[319]:
array([[ 2, 1, 7, 4],
[ 8, 0, 15, 12]])
In [320]: x.min(axis=1)
Out[320]:
array([[ 2, 1, 7, 4],
[ 8, 0, 15, 12]])
fewer lines but essentially the same thing and no shortcuts, I think
x= np.random.randint(5, size=(2, 3, 4))
x
array([[[3, 1, 0, 1],
[4, 2, 2, 1],
[2, 3, 2, 2]],
[[2, 1, 1, 1],
[0, 2, 0, 3],
[2, 3, 3, 1]]])
idx = [np.arange(i) for i in x.shape]
idx = list(np.ix_(*idx))
idx[axis]=np.expand_dims(x.argmin(axis),axis)
x[idx]
array([[[2, 1, 0, 1]],
[[0, 1, 0, 1]]])
np.squeeze(x[idx])
array([[2, 1, 0, 1],
[0, 1, 0, 1]])
mytake(x,x.argmin(axis=1), 1)
array([[2, 1, 0, 1],
[0, 1, 0, 1]])
Josef
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