numpy performance and list comprehension
robert.kern at gmail.com
Tue Apr 3 13:30:13 EDT 2007
> 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
> 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 ?
irstas is correct. I'm just going to show off another feature of numpy,
import numpy as np
V = np.array([1, 2, 3])
VT = V[:, np.newaxis] # VT.shape == (3, 1)
W = np.array([[1,2,3], [4,5,6], [7,8,9]])
dWdt = alpha * VT*(V - W*VT)
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
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