# [Numpy-discussion] How to sum weighted matrices

Nicolas SCHEFFER scheffer.nicolas at gmail.com
Tue Mar 8 12:29:00 EST 2011

```Or just with a dot:

===
In : np.tensordot(weights, matrices, (0,0))

Out:
array([[ 5.,  5.,  5.],
[ 5.,  5.,  5.]])

In : np.dot(matrices.T,weights).T

Out:
array([[ 5.,  5.,  5.],
[ 5.,  5.,  5.]])
==
make matrices.T C_CONTIGUOUS for maximum speed.

-n

On Mon, Mar 7, 2011 at 6:03 PM, shu wei <mailshuwei at gmail.com> wrote:
> Thanks very much. It works.
>
> On Mon, Mar 7, 2011 at 11:53 AM, <qubax at gmx.at> wrote:
>>
>> for your problem, you can do:
>>
>> ----------------------------
>>
>> import numpy as np
>>
>> weights = np.array([1,2])
>>
>> matrix1 = np.ones((2,3))
>> matrix2 = 2*np.ones((2,3))
>>
>> matrices = np.array([matrix1,matrix2])
>>
>> weighted_sum = np.tensordot(weights, matrices, (0,0))
>>
>> --------------------------
>>
>> On Mon, Mar 07, 2011 at 06:16:15AM -0600, shu wei wrote:
>> >    Hello all,
>> >
>> >    I am new to python and numpy.
>> >    My question is how to sum up N weighted matrices.
>> >    For example w=[1,2] (N=2 case)
>> >    m1=[1 2 3,
>> >           3 4 5]
>> >
>> >    m2=[3 4 5,
>> >           4 5 6]
>> >    I want to get a matrix Y=w*m1+w*m2 by using a loop.
>> >
>> >    My original problem is like this
>> >    X=[1 2 3,
>> >         3 4 5,
>> >         4 5 6]
>> >
>> >    a1=[1 2 3]  1st row of X
>> >    m1=a1'*a1 a matirx
>> >    a2=[3 4 5] 2nd row of X
>> >    m2=a2'*a2
>> >    a3=[ 4 5 6] 3rd row of X
>> >    m3=a3'*a3
>> >
>> >    I want to get Y1=w*m1+w*m2
>> >                          Y2=w*m2+w*m3
>> >    So basically it is rolling and to sum up the weighted matries
>> >    I have a big X, the rolling window is relatively small.
>> >
>> >    I tried to use
>> >
>> >    sq=np.array([x[i].reshape(-1,1)*x[i] for i in np.arange(0,len(x)]) #
>> >    s=len(x)
>> >    m=np.array([sq[i:i+t] for i in np.arange(0,s-t+1)]) # t is the len(w)
>> >
>> >    then I was stuck, I tried to use a loop somethig like
>> >    Y=np.array([np.sum(w[i]*m[j,i],axis=0) for i in np.arange(0,t)] )
>> >    Any suggestion is welcome.
>> >
>> >    sue
>>
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>>
>>
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>>
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>>
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>
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```