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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