On 03/07/14 04:33, Sturla Molden wrote:
> I would also like to say (while we're at it) that parallelizing this
> outside BLAS and LAPACK, whether with threads or processes, will require
> a memory overhead roughly equal to the size of the data array per thread
> or process. That is because computa
On 03/07/14 04:01, Sturla Molden wrote:
> On 02/07/14 06:02, Valerio Maggio wrote:
>
>> You were right when you said that under the hood the main `for` loop
>> iterates over the number of components, but in scikit this is
>> not done *explicitly* via Python loops.
>
> Look at lines 596 and 692.
>
On 02/07/14 06:02, Valerio Maggio wrote:
> You were right when you said that under the hood the main `for` loop iterates
> over the number of components, but in scikit this is
> not done *explicitly* via Python loops.
Look at lines 596 and 692.
https://github.com/scikit-learn/scikit-learn/blob/
Hi Sturla and Yuan.
Yesterday I looked into this and I would like to share with you my two cents.
Yuan Luo wrote:
> Hi,
> Does anyone know how I can make GMM parallel the fitting of some moderately
> big matrix (say, 390,000 x 400) with 200 components?
Actually, with scikit you can't do this ou
Yuan Luo wrote:
> Hi,
> Does anyone know how I can make GMM parallel the fitting of some moderately
> big matrix (say, 390,000 x 400) with 200 components?
I am not sure about GMM code in scikit-learn, but the EM-algorithm for GMMs
is very easy to vectorize. There are several ways to do this:
1.
Hi,
Does anyone know how I can make GMM parallel the fitting of some moderately
big matrix (say, 390,000 x 400) with 200 components?
Best,
Yuan
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