numpy performance and list comprehension

[TG]

Hi there.

Reading the page on python performance ( http://scipy.org/PerformancePython
) made me realize that I can achieve tremendous code acceleration with
numpy just by using "u[:,:]" kind of syntax the clever way.

Here is a little problem (Oja's rule of synaptic plasticity)

* W is a matrix containing the weights of connections between elements
i
and j
* V is an array containing the values of elements

I want to make W evolve with this rule :

dW[i,j] / dt = alpha * (V[i] * V[j] - W[i,j] * V[i]^2)

(don't pay attention to the derivate and stuff)

So, how would you write it in this nifty clever way ?

As a begining I wrote this :

  W += V.flatten().reshape((V.size,1)) *
V.flatten().reshape((1,V.size))

But it is not complete and, I guess, not efficient.