On 2009-08-27 16:09 , Jonathan T wrote:
> Hi,
>
> I want to define a 3-D array as the sum of two 2-D arrays as follows:
>
> C[x,y,z] := A[x,y] + B[x,z]
>
> My linear algebra is a bit rusty; is there a good way to do this that does not
> require me to loop over x,y,z? Thanks!
Numpy's broadcasti
Perfect, that is exactly what I was looking for. Thanks to all who responded.
There is one more problem which currently has me stumped. Same idea but slightly
different effect:
V[p,x,r] := C[p, E[p,x,r], r]
This multidimensional array stuff is confusing but the time savings seem to be
worth i
On Thu, Aug 27, 2009 at 3:32 PM, Citi, Luca wrote:
> Or
> a[:,:,None] + b[:,None,:]
I think that is the way to go.
Chuck
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Hi Jonathan,
This isn't quite your typical linear algebra. NumPy has a nice feature
called array broadcasting, which enables you to perform element-wise
operations on arrays of different shapes. The number of dimensions of
the arrays must be the same, in your case, all the arrays must have
three d
Or
a[:,:,None] + b[:,None,:]
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One solution I can think of still requires one loop (instead of three):
import numpy as np
a = np.arange(12).reshape(3,4)
b = np.arange(15).reshape(3,5)
z = np.empty(a.shape + (b.shape[-1],))
for i in range(len(z)):
z[i] = np.add.outer(a[i], b[i])
_
Jonathan T wrote:
> I want to define a 3-D array as the sum of two 2-D arrays as follows:
>
>C[x,y,z] := A[x,y] + B[x,z]
Is this what you mean?
In [14]: A = np.arange(6).reshape((2,3,1))
In [15]: B = np.arange(12).reshape((1,3,4))
In [18]: A
Out[18]:
array([[[0],
[1],
[2
Hi,
I want to define a 3-D array as the sum of two 2-D arrays as follows:
C[x,y,z] := A[x,y] + B[x,z]
My linear algebra is a bit rusty; is there a good way to do this that does not
require me to loop over x,y,z? Thanks!
Jonathan
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