On Fri, Feb 3, 2012 at 4:49 PM, Alan G Isaac wrote:
> On 2/3/2012 3:37 PM, josef.p...@gmail.com wrote:
>> res = - np.dot(x.T, mass*x)
>> res[np.arange(3), np.arange(3)] -= np.trace(res)
>
>
> Nice!
> Get some speed gain with slicing:
>
> res = - np.dot(x.T, mass*x)
> res.flat[slice(0,None,4)] -= n
On 2/3/2012 3:37 PM, josef.p...@gmail.com wrote:
> res = - np.dot(x.T, mass*x)
> res[np.arange(3), np.arange(3)] -= np.trace(res)
Nice!
Get some speed gain with slicing:
res = - np.dot(x.T, mass*x)
res.flat[slice(0,None,4)] -= np.trace(res)
Alan
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>>> 1. Re: Trick for fast (santhu kumar)
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>>
>> 1. Re: Trick for fast (santhu kumar)
>> 2. Re: Trick for fast (josef.p...@gmail.com)
>>
>>
>> --
>>
>> Message: 1
>> Date: Fri, 3 Feb 2012 12:29:26 -0600
>> Fro
s Topics:
>
> 1. Re: Trick for fast (santhu kumar)
> 2. Re: Trick for fast (josef.p...@gmail.com)
>
>
> --
>
> Message: 1
> Date: Fri, 3 Feb 2012 12:29:26 -0600
> From: santhu kumar
> Subject:
>> 2. Re: Trick for fast (S?ren Gammelmark)
>> 3. Re: Trick for fast (Sebastian Berg)
>>
>>
>> --
>>
>> Message: 1
>> Date: Fri, 3 Feb 2012 09:10:28 -0500
>> From: josef.p
-17287.5]
> [-16755. 0. -17865. ]
> [-17287.5 -17865. 0. ]]
> [[ 0. -16755. -17287.5]
> [-16755. 0. -17865. ]
> [-17287.5 -17865. 0. ]]
>
> Josef
>
>
> >
> > ___
> > NumPy-Discussion
I guess Einsum is much cleaner, but I already had started with this and
maybe someone likes it, this is fully vectorized and uses a bit of funny
stuff too:
# The dot product(s), written using broadcasting rules:
a = -(x.reshape(-1,1,3) * x[...,None])
# Magic, to avoid the eye thing, takes all dia
What about this?
A = einsum("i,ij->", mass, x ** 2)
B = einsum("i,ij,ik->jk", mass, x, x)
I = A * eye(3) - B
/Søren
On 3 February 2012 15:10, wrote:
> On Fri, Feb 3, 2012 at 8:44 AM, Alan G Isaac wrote:
> > On 2/3/2012 5:16 AM, santhu kumar wrote:
> >> x = nX3 vector.
> >> mass = nX1 vector
>
On Fri, Feb 3, 2012 at 8:44 AM, Alan G Isaac wrote:
> On 2/3/2012 5:16 AM, santhu kumar wrote:
>> x = nX3 vector.
>> mass = nX1 vector
>> inert = zeros((3,3))
>> for i in range(n):
>> ri = x[i,:].reshape(1,3)
>> inert = inert + mass[i,]*(sum(ri*ri)*eye(3) - dot(ri.T,ri))
>>
>
>
> Thi
On 2/3/2012 5:16 AM, santhu kumar wrote:
> x = nX3 vector.
> mass = nX1 vector
> inert = zeros((3,3))
> for i in range(n):
>ri = x[i,:].reshape(1,3)
>inert = inert + mass[i,]*(sum(ri*ri)*eye(3) - dot(ri.T,ri))
>
This should buy you a bit.
xdot = (x*x).sum(axis=1)
for (massi,xi,xd
Hello all,
I have tried to optimize most of my code but this ones seems to be the
major bottleneck as it gets called many times. I have run out of ideas to
make it more faster, can somebody please help me here.
x = nX3 vector.
mass = nX1 vector
inert = zeros((3,3))
for i in range(n):
ri = x
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