algorithm, optimization, or other problem?

[Gerard Flanagan]

Gerard Flanagan wrote:
> 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;
>

or:

ETA = params['eta']
INV_TAU = 1.0/params['tau']


Gerard

> 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
> > 
> >