[NumPy-Tickets] [NumPy] #1860: einsum '...'-based broadcasting

NumPy Trac numpy-tickets@scipy....
Tue Jun 7 10:12:07 CDT 2011

#1860: einsum '...'-based broadcasting
 Reporter:  wieland                                |       Owner:  somebody   
     Type:  enhancement                            |      Status:  new        
 Priority:  normal                                 |   Milestone:  Unscheduled
Component:  numpy.core                             |     Version:  1.6.0      
 Keywords:  einsum, broadcasting, high dimensions  |  
 I (and M. Wiebe) suggest a new function that generalizes the broadcasting
 that is so nicely implemented into 'einsum'.

 Consider the following example:

 >>> A = np.arange(25).reshape(5,5)
 >>> B = np.arange(5)
 >>> np.einsum('ij,j', A, B)
 array([ 30,  80, 130, 180, 230])

 Here, einsum takes the product of every element A_{ij}, multiplies with
 B_{j} and then sums over j leaving i fixed. So, two binary operations are
 at the heart of einsum, np.add and np.multiply. In this logic we could
 rewrite 'einsum' using a more general function 'broadcast_op',

 einsum('ij,j', A, B) = broadcast_op('ij,j', A, B, oper=[np.add,

 With this notation we can consider any kind of binary operation to replace
 np.add and np.multiply. As an example, consider np.add instead of

 >>> broadcast_op('ij,j', A, B, oper=[np.add, np.add])
 array([ 20, 45, 70, 95, 120])

 Equivalent but more cumbersome to write (especially in higher dimensions
 (!)) is

 >>> sum(A + B.reshape((1,5)),axis=1)
 array([ 20, 45, 70, 95, 120])

 Note that you are spared from any reshapes. One could also image to use
 'np.power' instead of 'np.multiply' such that B is the vector of powers
 and we take all elements of A_{ij} to the power of B_j. In some sense, you
 can see this broadcasting as a generalization of 'reduce' to higher

Ticket URL: <http://projects.scipy.org/numpy/ticket/1860>
NumPy <http://projects.scipy.org/numpy>
My example project

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