Brian Blais wrote:
> Hello,
>
> I am trying to translate some Matlab/mex code to Python, for doing neural
> simulations. This application is definitely computing-time limited, and I
> need to
> optimize at least one inner loop of the code, or perhaps even rethink the
> algorithm.
> The procedure is very simple, after initializing any variables:
>
> 1) select a random input vector, which I will call "x". right now I have it
> as an
> array, and I choose columns from that array randomly. in other cases, I may
> need to
> take an image, select a patch, and then make that a column vector.
>
> 2) calculate an output value, which is the dot product of the "x" and a weight
> vector, "w", so
>
> y=dot(x,w)
>
> 3) modify the weight vector based on a matrix equation, like:
>
> w=w+ eta * (y*x - y**2*w)
> ^
> |
> +---- learning rate constant
>
> 4) repeat steps 1-3 many times
>
Brian
ETA = 0.001
INV_TAU = 1.0/100.0
rather than:
> params={}
> params['eta']=0.001;
> params['tau']=100.0;
ie. take the dictionary lookup (and the repeated division) out?
If you have a collection of random vectors do you gain anything by
*choosing* them randomly?
I'm not exactly sure if it's equivalent to your existing code, but how
about:
x=random((100,250000))
w=random((100,1));
th=random((1,1))
for vector in x:
...
w=w+ ETA * (y*vector - y**2*w)
th = th + INV_TAU *(y**2-th)
...
? (I'm not familar with Numeric)
Gerard
> old_mx=0;
> for e in range(100):
>
> rnd=randint(0,numpats,250000)
> t1=time.time()
> if 0: # straight python
> for i in range(len(rnd)):
> pat=rnd[i]
> xx=reshape(x[:,pat],(1,-1))
> y=matrixmultiply(xx,w)
> w=w+params['eta']*(y*transpose(xx)-y**2*w);
> th=th+(1.0/params['tau'])*(y**2-th);
> else: # pyrex
> dohebb(params,w,th,x,rnd)
> print time.time()-t1
>
>
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