Python Newaxis vs for loop

[jfnossa]

Hi, I am trying to make my program faster.
I have a matrix and a vector:
GDES = N.array([[1,2,3,4,5],
[6,7,8,9,10],
[11,12,13,14,15],
[16,17,18,19,20],
[21,22,23,24,25]])
Ene=N.array([1,2,3,4,5])
NN=len(GDES);

I have defined a function for matrix multiplication:
def Gl(n,np,k,q):
matrix = GDES[k,np]*GDES[k,n]*GDES[q,np]*GDES[q,n]
return matrix

and I have made a for loop in my calculation:
SIl = N.zeros((NN,NN),N.float)
for n in xrange(NN):
for np in xrange(NN):
SumJ = N.sum(N.sum(Gl(n,np,k,q) for q in xrange(NN)) for k in xrange(NN))
SIl[n,np]=SumJ

print 'SIl:',SIl

output:
SIl: [[ 731025. 828100. 931225. 1040400. 1155625.]
[ 828100. 940900. 1060900. 1188100. 1322500.]
[ 931225. 1060900. 1199025. 1345600. 1500625.]
[ 1040400. 1188100. 1345600. 1512900. 1690000.]
[ 1155625. 1322500. 1500625. 1690000. 1890625.]]



I want to use newaxis to make it faster:

def G():
Mknp = GDES[:, :, N.newaxis, N.newaxis]
Mkn = GDES[:, N.newaxis, :, N.newaxis]
Mqnp = GDES[:, N.newaxis, N.newaxis, :]
Mqn = GDES[N.newaxis, :, :, N.newaxis]
matrix=Mknp*Mkn*Mqnp*Mqn
return matrix

tmp = G()
MGI = N.sum(N.sum(tmp,axis=3), axis=2)
MGI = N.reshape(MGI,(NN,NN))
print 'MGI:', MGI
output:
MGI: [[ 825 3900 9225 16800 26625]
[ 31200 92400 169600 262800 372000]
[ 146575 413400 722475 1073800 1467375]
[ 403200 1116900 1911600 2787300 3744000]
[ 857325 2352900 3980725 5740800 7633125]]


Any idea how can I get the right answer???
Thanks