[Numpy-discussion] Trick for fast
santhu kumar
mesanthu@gmail....
Fri Feb 3 12:58:43 CST 2012
Hi Josef,
I am unclear on what you want to say, but all I am doing in the code is
getting inertia tensor for a bunch of particle masses.
(http://en.wikipedia.org/wiki/Moment_of_inertia#Moment_of_inertia_tensor)
So the diagonals are not actually zeros but would have z^2 + y^2 ..
The reason which I said 3secs could be misunderstood .. This code is called
many times over a loop and the bulk time is taken in computing this
inertial tensor.
After code change, the entire loop finishes off in 3 ses.
Thanks for alertness,
Santhosh
On Fri, Feb 3, 2012 at 12:47 PM, <numpy-discussion-request@scipy.org> wrote:
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> 1. Re: Trick for fast (santhu kumar)
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> Message: 1
> Date: Fri, 3 Feb 2012 12:29:26 -0600
> From: santhu kumar <mesanthu@gmail.com>
> Subject: Re: [Numpy-discussion] Trick for fast
> To: numpy-discussion@scipy.org
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> <CA+7TRsszzxPLAOz_9pgU5e9mYcHTDQZMzRoMPzpfhrRkcStZEQ@mail.gmail.com
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> On Fri, Feb 3, 2012 at 1:29 PM, santhu kumar <mesanthu@gmail.com> wrote:
> > Hello all,
> >
> > Thanks for lovely solutions. I have sat on it for some time and wrote it
> > myself :
> >
> > n =x.shape[0]
> > ea = np.array([1,0,0,0,1,0,0,0,1])
> > inert = ((np.tile(ea,(n,1))*((x*x).sum(axis=1)[:,np.newaxis]) -
> >
> np.hstack([x*x[:,0][:,np.newaxis],x*x[:,1][:,np.newaxis],x*x[:,2][:,np.newaxis]]))*mass[:,np.newaxis]).sum(axis=0)
> > inert.shape = 3,3
> >
> > Does the trick and reduces the time from over 45 secs to 3 secs.
> > I do want to try einsum but my numpy is little old and it does not have
> it.
> >
> > Thanks Sebastian (it was tricky to understand your code for me) and Josef
> > (clean).
>
> Isn't the entire substraction of the first term just to set the
> diagonal of the result to zero.
>
> It looks to me now just like the weighted dot product and setting the
> diagonal to zero. That shouldn't take 3 secs unless you actual
> dimensions are huge.
>
> Josef
>
>
>
>
>
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