>>> x = np.array([['a0', 'a1'],['b0','b1']])
>>> y = np.array([['x0', 'x1'],['y0','y1']])
>>> iterable = [np.outer(x[i],y[i]) for i in xrange(x.shape[0])]
>>> elbareti = np.asarray(iterable)
>>> elbareti
array([[[ 'a0'*'x0', 'a0'*'x1' ],
[ 'a1'*'x0', 'a1'*'x1' ]],
[[ 'b0'*'y0', 'b0'*'y1' ],
[ 'b1'*'y0', 'b1'*'y1' ]]])
Since i'm planning on working with large arrays, is there a more numpy-like version of this? I feel like the answer is right under my nose and I'm thinking it has something to do with reduce
, but numpy's version only works with ufunc
s, not functions. Even a hint would be greatly appreciated.
Thanks in advance.
Is this what you're looking for?
x = np.array([[1,2], [3,4]])
y = np.array([[5,6], [7,8]])
x[:,:,np.newaxis] * y[:,np.newaxis,:]
array([[[ 5, 6],
[10, 12]],
[[21, 24],
[28, 32]]])
EDIT:
Btw, it's alway useful to look the implementation. Helps understanding the "magic". np.outer
looks like this:
return a.ravel()[:,newaxis]*b.ravel()[newaxis,:]
From here, it's easy.
Also, in you question, you have:
[np.outer(x[i],y[i]) for i in xrange(x.shape[0])]
Better written as:
[np.outer(xx,yy) for xx,yy in izip(x,y)]
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